The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'doi', 'abstract', 'year', 'labels', 'title'}) and 9 missing columns ({'recordSet', '@id', 'description', 'license', 'name', '@context', 'url', 'distribution', '@type'}).
This happened while the json dataset builder was generating data using
hf://datasets/nasa-gesdisc/es-publications-researchareas/publications_researchareas.json (at revision 002f46582ab454ca27ddea57da3e2787644f03a0)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
doi: string
year: int64
title: string
abstract: string
labels: list<item: struct<id: int64, name: string>>
child 0, item: struct<id: int64, name: string>
child 0, id: int64
child 1, name: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 665
to
{'@context': Value(dtype='string', id=None), '@type': Value(dtype='string', id=None), '@id': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'distribution': [{'@type': Value(dtype='string', id=None), '@id': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'contentUrl': Value(dtype='string', id=None), 'encodingFormat': Value(dtype='string', id=None), 'sha256': Value(dtype='string', id=None)}], 'recordSet': [{'@type': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'field': [{'@type': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'dataType': Value(dtype='string', id=None), '@id': Value(dtype='string', id=None)}], 'dataType': Value(dtype='string', id=None), 'key': {'@id': Value(dtype='string', id=None)}, 'data': [{'research_areas/id': Value(dtype='int64', id=None), 'research_areas/name': Value(dtype='string', id=None)}]}]}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'doi', 'abstract', 'year', 'labels', 'title'}) and 9 missing columns ({'recordSet', '@id', 'description', 'license', 'name', '@context', 'url', 'distribution', '@type'}).
This happened while the json dataset builder was generating data using
hf://datasets/nasa-gesdisc/es-publications-researchareas/publications_researchareas.json (at revision 002f46582ab454ca27ddea57da3e2787644f03a0)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
@context
string | @type
string | @id
string | name
string | description
string | license
string | url
string | distribution
list | recordSet
list | doi
string | year
int64 | title
string | abstract
string | labels
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://mlcommons.org/croissant/v1 | sc:Dataset | doi:10.57967/hf/2914 | NASA GESDISC Earth Science Publications by Research Areas | This dataset contains scientific publications that cite datasets from NASA's Goddard Earth Sciences Data and Information Services Center (GES-DISC). Each publication is tagged with one or more Earth science research areas, covering various topics like Atmospheric Composition, Water & Energy Cycles, Climate Variability, and more. | https://www.apache.org/licenses/LICENSE-2.0 | https://huggingface.co/datasets/nasa-gesdisc/es-publications-researchareas | [
{
"@type": "cr:FileObject",
"@id": "publications_researchareas.json",
"name": "publications_researchareas.json",
"contentUrl": "https://huggingface.co/datasets/nasa-gesdisc/es-publications-researchareas/blob/main/publications_researchareas.json",
"encodingFormat": "application/json",
"sha256": "7da13658bef1cdb385b8a0c6e3903fc331e9de68ef5f0ead34ca3712b2d6dfb9"
}
] | [
{
"@type": "cr:RecordSet",
"name": "publications",
"description": "A record of scientific publications and their associated research areas.",
"field": [
{
"@type": "cr:Field",
"name": "doi",
"description": "Digital Object Identifier of the publication.",
"dataType": "sc:Text",
"@id": null
},
{
"@type": "cr:Field",
"name": "year",
"description": "Year of publication.",
"dataType": "sc:Integer",
"@id": null
},
{
"@type": "cr:Field",
"name": "title",
"description": "Title of the publication.",
"dataType": "sc:Text",
"@id": null
},
{
"@type": "cr:Field",
"name": "abstract",
"description": "Abstract of the publication.",
"dataType": "sc:Text",
"@id": null
},
{
"@type": "cr:Field",
"name": "labels",
"description": "Research area labels associated with the publication.",
"dataType": "sc:Text",
"@id": null
}
],
"dataType": null,
"key": null,
"data": null
},
{
"@type": "cr:RecordSet",
"name": "research_areas",
"description": null,
"field": [
{
"@type": "cr:Field",
"name": null,
"description": null,
"dataType": "sc:Integer",
"@id": "research_areas/id"
},
{
"@type": "cr:Field",
"name": null,
"description": null,
"dataType": "sc:Text",
"@id": "research_areas/name"
}
],
"dataType": "sc:Enumeration",
"key": {
"@id": "research_areas/id"
},
"data": [
{
"research_areas/id": 0,
"research_areas/name": "Agriculture"
},
{
"research_areas/id": 1,
"research_areas/name": "Air Quality"
},
{
"research_areas/id": 2,
"research_areas/name": "Atmospheric/Ocean Indicators"
},
{
"research_areas/id": 3,
"research_areas/name": "Cryospheric Indicators"
},
{
"research_areas/id": 4,
"research_areas/name": "Droughts"
},
{
"research_areas/id": 5,
"research_areas/name": "Earthquakes"
},
{
"research_areas/id": 6,
"research_areas/name": "Ecosystems"
},
{
"research_areas/id": 7,
"research_areas/name": "Energy Production/Use"
},
{
"research_areas/id": 8,
"research_areas/name": "Environmental Impacts"
},
{
"research_areas/id": 9,
"research_areas/name": "Floods"
},
{
"research_areas/id": 10,
"research_areas/name": "Greenhouse Gases"
},
{
"research_areas/id": 11,
"research_areas/name": "Habitat Conversion/Fragmentation"
},
{
"research_areas/id": 12,
"research_areas/name": "Heat"
},
{
"research_areas/id": 13,
"research_areas/name": "Land Surface/Agriculture Indicators"
},
{
"research_areas/id": 14,
"research_areas/name": "Public Health"
},
{
"research_areas/id": 15,
"research_areas/name": "Severe Storms"
},
{
"research_areas/id": 16,
"research_areas/name": "Sun-Earth Interactions"
},
{
"research_areas/id": 17,
"research_areas/name": "Validation"
},
{
"research_areas/id": 18,
"research_areas/name": "Volcanic Eruptions"
},
{
"research_areas/id": 19,
"research_areas/name": "Water Quality"
},
{
"research_areas/id": 20,
"research_areas/name": "Wildfires"
}
]
}
] | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | 10.3389/FRSEN.2021.748362 | 2,021 | Atmospheric Correction of DSCOVR EPIC: Version 2 MAIAC Algorithm | The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides multispectral images of the sunlit disk of Earth since 2015 from the L1 orbit, approximately 1.5 million km from Earth toward the Sun. The NASAs Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been adapted for DSCOVR/EPIC data providing operational processing since 2018. Here, we describe the latest version 2 (v2) MAIAC EPIC algorithm over land that features improved aerosol retrieval with updated regional aerosol models and new atmospheric correction scheme based on the ancillary bidirectional reflectance distribution function (BRDF) model of the Earth from MAIAC MODIS. The global validation of MAIAC EPIC aerosol optical depth (AOD) with AERONET measurements shows a significant improvement over v1 and the mean bias error MBE = 0.046, RMSE = 0.159, and
R
= 0.77. Over 66.7% of EPIC AOD retrievals agree with the AERONET AOD to within (0.1 + 0.1AOD). We also analyze the role of surface anisotropy, particularly important for the backscattering view geometry of EPIC, on the result of atmospheric correction. The retrieved BRDF-based bidirectional reflectance factors (BRF) are found higher than the Lambertian reflectance by 815% at 443 nm and 12% at 780 nm for EPIC observations near the local noon. Due to higher uncertainties, the atmospheric correction at UV wavelengths of 340, 388 nm is currently performed using a Lambertian approximation. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2021WR030612 | 2,022 | A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground‐based Precipitation Products | A two-level clustering approach is proposed for optimal design/expansion of a ground-based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter-site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station's location in each group is identified by a proposed ranking procedure that accounts for population density, land-use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest-based regression relationships developed for gauged grids between merged multiple satellite-based precipitation products (CHIRPS, IMERG) and ground-based precipitation measurements. The potential of the proposed approach over other clustering-based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km<SUP>2</SUP>) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/JTECH-D-20-0005.1 | 2,020 | The RELAMPAGO lightning mapping array: Overview and initial comparison with the geostationary lightning mapper | Abstract During November 2018April 2019, an 11-station very high frequency (VHF) Lightning Mapping Array (LMA) was deployed to Cordoba Province, Argentina. The purpose of the LMA was validation of the Geostationary Lightning Mapper (GLM), but the deployment was coordinated with two field campaigns. The LMA observed 2.9 million flashes ( five sources) during 163 days, and level-1 (VHF locations), level-2 (flashes classified), and level-3 (gridded products) datasets have been made public. The networks performance allows scientifically useful analysis within 100 km when at least seven stations were active. Careful analysis beyond 100 km is also possible. The LMA dataset includes many examples of intense storms with extremely high flash rates (>1 s1), electrical discharges in overshooting tops (OTs), as well as anomalously charged thunderstorms with low-altitude lightning. The modal flash altitude was 10 km, but many flashes occurred at very high altitude (1520 km). There were also anomalous and stratiform flashes near 57 km in altitude. Most flashes were small (<50 km2 area). Comparisons with GLM on 14 and 20 December 2018 indicated that GLM most successfully detected larger flashes (i.e., more than 100 VHF sources), with detection efficiency (DE) up to 90%. However, GLM DE was reduced for flashes that were smaller or that occurred lower in the cloud (e.g., near 6-km altitude). GLM DE also was reduced during a period of OT electrical discharges. Overall, GLM DE was a strong function of thunderstorm evolution and the dominant characteristics of the lightning it produced. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2019MS001916 | 2,020 | The Community Earth System Model Version 2 (CESM2) | An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the "low-top" (40 km, with limited chemistry) and "high-top" (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low-latitude precipitation and shortwave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Niño-Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1-5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9-2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.2151/SOLA.2022-025 | 2,022 | Diurnal Variation of Surface Wind Divergence in the Maritime Continent Using ASCAT and SeaWinds Observations and ERA5 Reanalysis Data | This study investigates the diurnal variation of surface wind divergence in the seas of the Maritime Continent by using satellite scatterometer observations and atmospheric reanalysis data. This is the first study to demonstrate the distribution and seasonal variation of the diurnally varying surface winds in the Maritime Continent in terms of wind divergence. Wind divergence develops from the coasts of the islands toward the center of the seas and dominates during the afternoon and evening hours. Wind convergence dominates over the seas during the nighttime and morning hours. The offshore extensions of the wind divergence and convergence from the coast differ regionally and thus show the asymmetric patterns with respect to the center of the seas. In particular, strong wind divergence develops from the southern coasts of the Java Sea and the Arafura Sea to extend northward beyond the center of the seas. The diurnal amplitudes of wind divergence vary seasonally and reach a peak in September in most of the seas. The switching times between wind divergence and convergence are almost fixed throughout the year regardless of the monsoon reversal. | [
{
"id": 2,
"name": "Atmospheric/Ocean Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.OCEMOD.2021.101850 | 2,021 | Blending drifters and altimetric data to estimate surface currents: Application in the Levantine Mediterranean and objective validation with different data types | An improved estimation of the surface currents in the Levantine Basin of the Mediterranean sea is crucial for a wide range of applications, including pollutants transport and nutrients distribution. This estimation remains challenging due to the scarcity or shortcomings of various data types used for this purpose. In this paper, we present an objective validation of a variational assimilation algorithm that blends geostrophic velocities derived from altimetry, wind-induced velocities, and drifter positions, to continuously obtain velocity corrections. The assessment of the validation impact was based on available independent in-situ data (current meters, gliders, and independent drifters) and satellite ocean color images. In all cases, the improvement was shown either qualitatively (position of the eddies) or quantitatively. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.JAG.2021.102307 | 2,021 | Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping | Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ATMOSENV.2020.118160 | 2,021 | Towards a regional dust modeling system in the central Middle East: Evaluation, uncertainties and recommendations | This study aims towards an accurate regional dust modeling system in the central Middle East area (CME), through the implementation of the state-of-art dust parameterizations. The modeling system consists of the natural emission model NEMO, the meteorological model WRF and the chemistry transport model CAMx. An extensive evaluation of 16 different configurations has been realized, incorporating all the combinations of the components utilized in the state-of-art dust modeling approaches, namely the drag partition, the sandblasting efficiency, the horizontal mass flux, as well two commonly used soil particle size distributions. Daily mean PM10 measurements in Doha, as well the satellite AOD products of MODIS have been used for the quantitative and the qualitative assessment of the simulations. Noteworthy, each of these assessments did not yield to the exact same ranking of the configurations (e.g. best five) but they assisted on identifying clear patterns. For example, a consistent overestimation of the daily mean PM10, when the MacKinnon's drag partition scheme is utilized, was found. On the other hand, the assessments led to best three performing configurations, with common components the Raupach's drag partition scheme and Alfaro and Gomes sandblasting efficiency. Although their overall performance is good, several issues were found i.e. on individual dust events and a mean underestimation during the studied period, ranging from 49 to 75 g/m3. One cause of this underestimation could be the aerodynamic entrainment, a mechanism usually neglected from the dust modeling approaches. Another cause could be missed regional and/or local sources or underestimation of their emission rates. | [
{
"id": 1,
"name": "Air Quality"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.JHYDROL.2020.125186 | 2,020 | A unified framework of water balance models for monthly, annual, and mean annual timescales | At present, many conceptual water balance models have been proposed on monthly, annual, and mean annual timescales. With the increasing applications of these models, an emerging question is whether the water balance models on different timescales have some commonalities and can be integrated. To this end, this study established and applied a practical unified framework of water balance models that can be applied to different timescales. The framework was first developed on monthly timescale, where the water balance was captured by combining the abcd model and Budyko hypothesis with four parameters. Then, this framework was extended to annual and mean annual timescales under which certain parameters can be eliminated or assumed to be certain values. To be specific, on the annual timescale, the water balance was simulated by a Budyko-type model incorporating the water storage change; on the mean annual timescale the water balance was further simplified into a Budyko-type model which neglects the water storage change. Thus, all of the three typical water balance models on different timescales were unified to a general framework. The models were applied to 437 catchments in the contiguous United States with satisfying results achieved. Considering the water storage changes can improve the performance of annual water balance models in regions with clear interannual carrying water storage. Furthermore, the water balance models on different timescales share common supply and demand limits which is similar to Budyko hypothesis. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2018GL080976 | 2,018 | Lake level and surface topography measured with spaceborne GNSS‐reflectometry from CYGNSS mission: Example for the lake Qinghai | This paper demonstrates inland water altimetry of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) using the Cyclone GNSS (CYGNSS) mission data. From 12 tracks of raw data overpassing the Lake Qinghai, the bistatic group delay and carrier phase delay are extracted from the quasi-specular GNSS reflections. The water levels derived from the group delay observations are consistent with the Cryosat-2 and in situ gauge measurements. The surface topography profiles from the phase delay measurements show good self-consistence along the coincident tracks. Decimeter-level surface height anomalies are resolved with the phase delay measurements, which can be associated with the accuracy degradation of the geoid model in this region. Systematic errors remain in both group delay and phase delay altimetry measurements, which are attributed to the receiver orbit errors and ionospheric correction residuals. These limitations can be eliminated in further GNSS-R missions dedicated to altimetry applications. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.RSE.2018.12.031 | 2,019 | Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 | Accurate quantification of terrestrial evapotranspiration (ET) is essential to understand the Earth's energy and water budgets under climate change. However, despite water and carbon cycle coupling, there are few diagnostic global evapotranspiration models that have complete carbon constraint on water flux run at a high spatial resolution. Here we estimate 8-day global ET and gross primary production (GPP) at 500 m resolution from July 2002 to December 2017 using a coupled diagnostic biophysical model (called PML-V2) that, built using Google Earth Engine, takes MODIS data (leaf area index, albedo, and emissivity) together with GLDAS meteorological forcing data as model inputs. PML-V2 is well calibrated against 8-day measurements at 95 widely-distributed flux towers for 10 plant functional types, indicated by Root Mean Square Error (RMSE) and Bias being 0.69 mm d1 and 1.8% for ET respectively, and being 1.99 g C m2 d1 and 4.2% for GPP. Compared to that performance, the cross-validation results are slightly degraded, with RMSE and Bias being 0.73 mm d1 and 3% for ET, and 2.13 g C m2 d1 and 3.3% for GPP, which indicates robust model performance. The PML-V2 products are noticeably better than most GPP and ET products that have a similar spatial resolution, and suitable for assessing the influence of carbon-induced impacts on ET. Our estimates show that global ET and GPP both significantly (p | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ENVC.2021.100359 | 2,021 | A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data | In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spatial features in satellite data. With that aim, we extend the algebraic concepts of cubical complexes to the satellite data perspective and describe homology groups portrayal. As a proof by example, we present an inter-site comparison of Enhanced Vegetation Index from MODerate-resolution Imaging Spectroradiometer over fifteen regions worldwide. There, the Local Climate Zone (LCZ) framework is used to examine the outcomes of the PH filtration. Then, the features from every region that were encapsulated by the PH were compared against each other with the aid of the Bottleneck Distance metric. After that, it was performed a dimensionality reduction with a multi-dimensional scaling to build a 2-D geometry of the level of similarity among them. Thereby, the common aspects of the regions became explicit by their coordinates proximity in space. Then, with the use of the K-means algorithm, we were able to cluster those areas belonging to the same LCZ class. The results indicate that the proposed methods are robust to missing data in the satellite data and insensitive to a certain level of inhomogeneity in the spatial subsetting of data. Furthermore, the outcomes provide insights on several viable applications for future research. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1109/LGRS.2021.3058956 | 2,022 | Monthly Surface Elevation Changes of the Greenland Ice Sheet From ICESat-1, CryoSat-2, and ICESat-2 Altimetry Missions | The Greenland Ice Sheet (GrIS) mass balance shows significant variabilities over a range of time scales. As geodetic records lengthen over time, it becomes insufficient to characterize the temporal evolution of the ice sheet by using a best-fit linear trend over a given observation period. This study investigates the joint analysis of laser and radar satellite altimeter measurements for estimating GrIS surface elevation changes (SECs) with a 30-day resolution. We first apply a crossover analysis to assess the precisions of the surface elevations measured by ICESat-1/2 laser altimeters and CryoSat-2 radar altimeter over the GrIS, which are needed for assigning weights for each data set in the joint analysis. Then, based on a modified repeat-track approach, we analyze the surface elevation measurements of ICESat-1/2 and CryoSat-2 to produce monthly SEC estimates for the past two decades, together with their associated uncertainties. The multimission SEC estimates are further assessed by using IceBridge airborne laser measurements, showing differences with a median value of 12 cm 60 cm. The monthly SEC time series reveal important variations over a range of time scales across different parts of the GrIS and would facilitate the investigation of complex spatiotemporal patterns of GrIS changes. | [
{
"id": 3,
"name": "Cryospheric Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ACP-19-3307-2019 | 2,019 | Multi-satellite retrieval of single scattering albedo using the OMI–MODIS algorithm | Abstract. Single scattering albedo (SSA) represents a unique identification of aerosol type and can be a determinant factor in the estimation of aerosol radiative forcing. However, SSA retrievals are highly uncertain due to cloud contamination and aerosol composition. The recent improvement in the SSA retrieval algorithm has combined the superior cloud-masking technique of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the higher sensitivity of the Ozone Monitoring Instrument (OMI) to aerosol absorption. The combined OMIMODIS algorithm has only been validated over a small spatial and temporal scale. The present study validates the algorithm over global oceans for the period from 2008 to 2012. The geographical heterogeneity in the aerosol type and concentration over the Atlantic Ocean, the Arabian Sea and the Bay of Bengal was useful to delineate the effect of aerosol type on the retrieval algorithm. We also noted that OMI overestimated SSA when absorbing aerosols were present closer to the surface. We attribute this overestimation to data discontinuity in the aerosol height climatology derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. OMI uses predefined aerosol heights over regions where CALIPSO climatology is not present, leading to the overestimation of SSA. The importance of aerosol height was also studied using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The results from the joint retrievals were validated using cruise-based measurements. It was seen that OMIMODIS SSA retrievals performed better than the OMI only retrieval over the Bay of Bengal during winter, when the aerosols are present closer to the surface. Discrepancy between satellite retrievals and cruise measurements was seen when elevated aerosols were present which might not have been detected by the cruise instruments. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.JAFREARSCI.2021.104377 | 2,021 | Estimating gravity field and quasi-geoid in Cameroon (CGM20) | In this paper, the computation of the new gravimetric quasi-geoid of Cameroon based on some recent global geopotential models and a detailed Digital Terrain Model (DTM) of the area is presented. Besides gravity data supplied by the Bureau Gravimetrique International (BGI) new data have been collected by the National Institute of Cartography (NIC) of Cameroon and used in the computation. Three different quasi-geoid models have been obtained based on three different global models, namely GOCE-dir5, EGM2008 and XGM2019e_2159. The well-known remove-compute-restore technique has been applied and Fast Collocation has been used for estimating the residual quasi-geoid component from gridded gravity data. The comparisons with Global Navigation Satellite System/levelling (GNSS/levelling) data distributed over the Cameroon territory show that some improvements have been obtained either with respect to the global model solutions and to previous local estimates computed in the past. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.RSE.2020.112191 | 2,021 | Remotely sensed ensembles of the terrestrial water budget over major global river basins: An assessment of three closure techniques | Remote sensing is a useful tool for observing the water cycle. However, combining remote sensing products over any major river basin will result in a residual error in the overall water balance. Previous studies have either quantified this error without correcting it, or have merged observations together with land surface models (LSMs) to produce a single best estimate of the water balance. Here, we present a new approach in which combinations of remote sensing and in situ observations are constrained to enforce water balance closure. Rather than a single estimate, this produces an ensemble of unique water balance estimates intended to characterize uncertainty and to avoid biases implicit in LSMs. We evaluate three techniques of varying complexity to enforce water balance closure for individual ensemble members over 24 global basins from Oct. 2002 - Dec. 2014, resulting in as many as 60 realizations of the monthly water budget, contingent upon data availability. Compared with a published climate data record, the ensemble shows strong agreement for precipitation, evapotranspiration and changes in storage (R2: 0.910.95), with less agreement for streamflow (R2: 0.420.47), which may be indicative of LSM biases in the climate data record. Water balance residual errors resulting from combinations of raw products vary significantly (p | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2019JD030911 | 2,020 | Fifty Years of Research on the MaddenJulian Oscillation: Recent Progress, Challenges, and Perspectives | Since its discovery in the early 1970s, the crucial role of the Madden-Julian Oscillation (MJO) in the global hydrological cycle and its tremendous influence on high-impact climate and weather extremes have been well recognized. The MJO also serves as a primary source of predictability for global Earth system variability on subseasonal time scales. The MJO remains poorly represented in our state-of-the-art climate and weather forecasting models, however. Moreover, despite the advances made in recent decades, theories for the MJO still disagree at a fundamental level. The problems of understanding and modeling the MJO have attracted significant interest from the research community. As a part of the AGU's Centennial collection, this article provides a review of recent progress, particularly over the last decade, in observational, modeling, and theoretical study of the MJO. A brief outlook for near-future MJO research directions is also provided. | [
{
"id": 2,
"name": "Atmospheric/Ocean Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1080/2150704X.2019.1602790 | 2,019 | Statistical and visual quality assessment of nearly-global and continental digital elevation models of Trentino, Italy | Nearly-global digital elevation models (DEMs) Shuttle Radar Topography Mission DEM (SRTM1 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), and Advanced Land Observing Satellite World 3D digital surface model (AW3D30 DSM) are widely used in geosciences. A nearly-continental European DEM (EU-DEM) is also publicly available. We assess statistical accuracy and artefact occurrence of the latest versions of these models for Trentino, Italian Alps. We used a set of 111 geodetic network points and a lidar-based reference DEM. Statistically, AW3D30 DSM has the highest accuracy, while EU-DEM is marked by the worst characteristics. To visualize the spatial distribution of errors, we calculated residuals between the reference and validated DEMs. Voids abound in SRTM1 DEM that makes problematic its application in the Alpine region. Randomly distributed, grain-like artefacts are common for ASTER GDEM. AW3D30 DSM includes artefacts caused by to the Gibbs phenomenon. Retaining defects of SRTM1 DEM and ASTER GDEM, EU-DEM has artefacts caused by its hydrological correction. Usual statistical evaluation of DEM accuracy is not capable to describe completely DEM quality. A robust spatial analysis, including interactive visual inspection, should be adopted for a quality control of DEMs. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ACP-17-11541-2017 | 2,017 | Reanalysis comparisons of upper troposphericlower stratospheric jets and multiple tropopauses | Abstract. The representation of upper troposphericlower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistryclimate models. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5334/DSJ-2017-004 | 2,017 | Enhancing interoperability and capabilities of earth science data using the Observations Data Model 2 (ODM2) | Earth Science researchers require access to integrated, cross-disciplinary data in order to answer critical research questions. Partially due to these science drivers, it is common for disciplinary data systems to expand from their original scope in order to accommodate collaborative research. The result is multiple disparate databases with overlapping but incompatible data. In order to enable more complete data integration and analysis, the Observations Data Model Version 2 (ODM2) was developed to be a general information model, with one of its major goals to integrate data collected by in situ sensors with those by ex-situ analyses of field specimens. Four use cases with different science drivers and disciplines have adopted ODM2 because of benefits to their users. The disciplines behind the four cases are diverse hydrology, rock geochemistry, soil geochemistry, and biogeochemistry. For each case, we outline the benefits, challenges, and rationale for adopting ODM2. In each case, the decision to implement ODM2 was made to increase interoperability and expand data and metadata capabilities. One of the common benefits was the ability to use the flexible handling and comprehensive description of specimens and data collection sites in ODM2s sampling feature concept. We also summarize best practices for implementing ODM2 based on the experience of these initial adopters. The descriptions here should help other potential adopters of ODM2 implement their own instances or to modify ODM2 to suit their needs. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/JTECH-D-14-00080.1 | 2,015 | An improved near-surface specific humidity and air temperature climatology for the SSM/I satellite period | Abstract A near-surface specific humidity (Qa) and air temperature (Ta) climatology on daily and 0.25 grids was constructed by the objectively analyzed airsea fluxes (OAFlux) project by objectively merging two recent satellite-derived high-resolution analyses, the OAFlux existing 1 analysis, and atmospheric reanalyses. The two satellite products include the multi-instrument microwave regression (MIMR) Qa and Ta analysis and the Goddard Satellite-Based Surface Turbulent Fluxes, version 3 (GSSTF3), Qa analysis. This study assesses the degree of improvement made by OAFlux using buoy time series measurements at 137 locations and a global empirical orthogonal function (EOF) analysis. There are a total of 130 855 collocated daily values for Qa and 283 012 collocated daily values for Ta in the buoy evaluation. It is found that OAFlux Qa has a mean difference close to 0 and a root-mean-square (RMS) difference of 0.73 g kg1, and Ta has a mean difference of 0.03C and an RMS difference of 0.45C. OAFlux shows no major systematic bias with respect to buoy measurements over all buoy locations except for the vicinity of the Gulf Stream boundary current, where the RMS difference exceeds 1.8C in Ta and 1.2 g kg1 in Qa. The buoy evaluation indicates that OAFlux represents an improvement over MIMR and GSSTF3. The global EOF-based intercomparison analysis indicates that OAFlux has a similar spatialtemporal variability pattern with that of three atmospheric reanalyses including MERRA, NCEP-1, and ERA-Interim, but that it differs from GSSTF3 and the Climate Forecast System Reanalysis (CFSR). | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ESSD-12-647-2020 | 2,020 | A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures | Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF)
comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor
Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to
December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense
Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the
hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure
ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been
reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data
processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight
intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available.
Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the
instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely
traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data
files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeT),
quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely
available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017). | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/AMT-8-4561-2015 | 2,015 | Quality assessment and improvement of the EUMETSAT Meteosat surface albedo climate data record | Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (19822006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS12233930 | 2,020 | Variational Retrievals of High Winds Using Uncalibrated CyGNSS | This study presents a new retrieval approach for obtaining wind speeds from CyGNSS level-1 observables. Unlike other existing approaches, (1) this one is a variational technique that is based on a physical forward model, (2) it uses uncalibrated bin raw counts observables, (3) the geophysical information content comes from only one pixel of the broader delay-Doppler map, finest achievable resolution in level-1 products over the sea, and (4) calibrates them against track-wise polynomial adjustments to a background numerical weather prediction model. Through comparisons with the background model, other spaceborne sensors (SMAP, SMOS, ASCAT-A/B), and CyGNSS wind retrievals by other organizations, the study shows that this approach has skills to infer wind speeds, including hurricane force winds. For example, the Pearsons correlation coefficient between these CyGNSS retrievals and ERA5 is 0.884, 0.832 with NOAA CyGNSS results, and 0.831 with respect to SMAP co-located measurements. Furthermore, the variational retrieval algorithm is a simplified version of the more general equations that are used in data assimilation, and the calibration scheme could also be integrated in the assimilation process. Therefore, this approach is also a good tool for analyzing the potential performance of ingesting uncalibrated level-1 single-pixel observables into NWP. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/2016GL068462 | 2,016 | Skill improvement of dynamical seasonal Arctic sea ice forecasts | We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic sea ice. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS14030497 | 2,022 | Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals | Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/AMT-15-4063-2022 | 2,022 | On the influence of underlying elevation data on Sentinel-5 Precursor TROPOMI satellite methane retrievals over Greenland | Abstract. The Sentinel-5 Precursor (S5P) mission was launched on October 2017 and has since provided data with high spatio-temporal resolution using its remote sensing instrument, the TROPOspheric Monitoring Instrument (TROPOMI). The latter is a nadir viewing passive grating imaging spectrometer. The mathematical inversion of the TROPOMI data yields retrievals of different trace gas and aerosol data products. The column-averaged dry-air mole fraction of methane (XCH4) is the product of interest to this study. The daily global coverage of the atmospheric methane mole fraction data enables the analysis of the methane distribution and variation on large scales and also to estimate surface emissions. The spatio-temporal high-resolution satellite data are potentially particularly valuable in remote regions, such as the Arctic, where few ground stations and in situ measurements are available. In addition to the operational Copernicus S5P total-column-averaged dry-air mole fraction methane data product developed by SRON, the scientific TROPOMI/WFMD algorithm data product v1.5 (WFMD product) was generated at the Institute of Environmental Physics at the University of Bremen. In this study we focus on the assessment of both S5P XCH4 data products over Greenland and find that spatial maps of both products show distinct features along the coastlines. Anomalies up to and exceeding 100 ppb are observed and stand out in comparison to the otherwise smooth changes in the methane distribution. These features are more pronounced for the operational product compared to the WFMD product. The spatial patterns correlate with the difference between the GMTED2010 digital elevation model (DEM) used in the retrievals and a more recent topography dataset, indicating that inaccuracies in the assumed surface elevation are the origin of the observed features. These correlations are stronger for the WFMD product. In order to evaluate the impact of the topography dataset on the retrieval we reprocess the WFMD product with updated elevation data. We find that a significant reduction of the localized features when GMTED2010 is replaced by recent topography data over Greenland based on ICESat-2 data. This study shows the importance of the chosen topography data for retrieved dry-air mole fractions. The use of a DEM that is as accurate and as up to date as possible is advised for all S5P data products as well as for future missions which rely on a DEM as input data. A modification based on this study is planned to be introduced in the next version of the WFMD data product. | [
{
"id": 10,
"name": "Greenhouse Gases"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2019JD031400 | 2,020 | Evaluation of OCO2 X Variability at Local and Synoptic Scales using Lidar and In Situ Observations from the ACTAmerica Campaigns | With nearly 1 million observations of column-mean carbon dioxide concentration (X<SUB>CO<SUB>2</SUB></SUB>) per day, the Orbiting Carbon Observatory 2 (OCO-2) presents exciting possibilities for monitoring the global carbon cycle, including the detection of subcontinental column CO<SUB>2</SUB> variations. While the OCO-2 data set has been shown to achieve target precision and accuracy on a single-sounding level, the validation of X<SUB>CO<SUB>2</SUB></SUB> spatial gradients on subcontinental scales remains challenging. In this work, we investigate the use of an integrated path differential absorption (IPDA) lidar for evaluation of OCO-2 observations via NASA's Atmospheric Carbon and Transport (ACT)-America project. The project has completed eight clear-sky underflights of OCO-2 with the Multifunctional Fiber Laser Lidar (MFLL)—along with a suite of in situ instruments—giving a precisely colocated, high-resolution validation data set spanning nearly 3,800 km across four seasons. We explore the challenges and opportunities involved in comparing the MFLL and OCO-2 X<SUB>CO<SUB>2</SUB></SUB> data sets and evaluate their agreement on synoptic and local scales. We find that OCO-2 synoptic-scale gradients generally agree with those derived from the lidar, typically to ±0.1 ppm per degree latitude for gradients ranging in strength from 0 to 1 ppm per degree latitude. CO<SUB>2</SUB> reanalysis products also typically agree to ±0.25 ppm per degree when compared with an in situ-informed CO<SUB>2</SUB> "curtain." Real X<SUB>CO<SUB>2</SUB></SUB> features at local scales, however, remain challenging to observe and validate from space, with correlation coefficients typically below 0.35 between OCO-2 and the MFLL. Even so, ACT-America data have helped investigate interesting local X<SUB>CO<SUB>2</SUB></SUB> patterns and identify systematic spurious cloud-related features in the OCO-2 data set. | [
{
"id": 10,
"name": "Greenhouse Gases"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/AMT-15-605-2022 | 2,022 | Differential absorption lidar measurements of water vapor by the High Altitude Lidar Observatory (HALO): retrieval framework and first results | Abstract. Airborne differential absorption lidar (DIAL) offers a uniquely capable solution to the problem of measuring water vapor (WV) with high precision, accuracy, and resolution throughout the troposphere and lower stratosphere. The High Altitude Lidar Observatory (HALO) airborne WV DIAL was recently developed at NASA Langley Research Center and was first deployed in 2019. It uses four wavelengths near 935 nm to achieve sensitivity over a wide dynamic range and simultaneously employs 1064 nm backscatter and 532 nm high-spectral-resolution lidar (HSRL) measurements for aerosol and cloud profiling. A key component of the WV retrieval framework is flexibly trading resolution for precision to achieve optimal datasets for scientific objectives across scales. An approach to retrieving WV in the lowest few hundred meters of the atmosphere using the strong surface return signal is also presented. The five maiden flights of the HALO WV DIAL spanned the tropics through midlatitudes with a wide range of atmospheric conditions, but opportunities for validation were sparse. Comparisons to dropsonde WV profiles were qualitatively in good agreement, though statistical analysis was impossible due to systematic error in the dropsonde measurements. Comparison of HALO to in situ WV measurements aboard the aircraft showed no substantial bias across 3 orders of magnitude, despite variance (R2=0.66) that may be largely attributed to spatiotemporal variability. Precipitable water vapor measurements from the spaceborne sounders AIRS and IASI compared very well to HALO with R2>0.96 over ocean. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS13183581 | 2,021 | Digital elevation models: Terminology and definitions | Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earths surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.GLOENVCHA.2021.102291 | 2,021 | Scenario archetypes reveal risks and opportunities for global mountain futures | Mountain social-ecological systems (MtSES) are transforming rapidly due to changes in multiple environmental and socioeconomic drivers. However, the complexity and diversity of MtSES present challenges for local communities, researchers and decision makers seeking to anticipate change and promote action towards sustainable MtSES. Participatory scenario planning can reveal potential futures and their interacting dynamics, while archetype analysis aggregates insights from site-based scenarios. We combined a systematic review of the global MtSES participatory scenarios literature and archetype analysis to identify emergent MtSES archetypal configurations. An initial sample of 1983 rendered 42 articles that contained 142 scenarios within which were 852 futures states. From these future states within the scenarios, we identified 59 desirable and undesirable futures that were common across studies. These common futures were grouped into four clusters that correlated significantly with three social-ecological factors (GDP per capita, income inequality, and mean annual temperature). Using these clusters and their associated significant factors, we derived four MtSES scenario archetypal configurations characterized by similar key adaptation strategies, assumptions, risks, and uncertainties. We called these archetypes: (1) revitalization through effective institutions and tourism; (2) local innovations in smallholder farming and forestry; (3) upland depopulation and increased risk of hazards; and (4) regulated economic and ecological prosperity. Results indicate risks to be mitigated, including biodiversity loss, ecosystem degradation, cultural heritage change, loss of connection to the land, weak leadership, market collapse, upland depopulation, increased landslides, avalanches, mudflows and rock falls, as well as climate variability and change. Transformative opportunities lie in adaptive biodiversity conservation, income diversification, adaptation to market fluxes, improving transport and irrigation infrastructure, high quality tourism and preserving traditional knowledge. Despite the uncertainties arising from global environmental changes, these archetypes support better targeting of evidence-informed actions across scales and sectors in MtSES. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/W10070901 | 2,018 | Performance assessment of MOD16 in evapotranspiration evaluation in Northwestern Mexico | Evapotranspiration (ET) is the second largest component of the water cycle in arid and semiarid environments, and, in fact, more than 60% of the precipitation on earth is returned to the atmosphere through it. MOD16 represents an operational source of ET estimates with adequate spatial resolution for several applications, such as water resources planning, at a regional scale. However, the use of these estimates in routine applications will require MOD16 evaluation and validation using accurate ground-based measurements. The main objective of this study was to evaluate the performance of the MOD16A2 product by comparing it with eddy covariance (EC) systems. Additional objectives were the analysis of the limitations, uncertainties, and possible improvements of the MOD16-estimated ET. The EC measurements were acquired for five sites and for a variety of land covers in northwestern Mexico. The indicators used for the comparison were: root mean square error (RMSE), bias (BIAS), concordance index (d), and determination coefficient (R2) of the correlation, comparing measured and modelled ET. The best performance was observed in Rayon (RMSE = 0.77 mmday1, BIAS = 0.46 mmday1, d = 0.88, and R2 = 0.86); El Mogor and La Paz showed errors and coefficients of determination comparable to each other (RMSE = 0.39 mmday1, BIAS = 0.04 mmday1, R2 = 0.46 and RMSE = 0.42 mmday1, BIAS = 0.18 mmday1, R2 = 0.45, respectively). In most cases, MOD16 underestimated the ET values. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1080/01490419.2017.1333549 | 2,017 | Extension of satellite Altimetry Jason-2 sea level anomalies towards the red sea coast using polynomial harmonic techniques | Satellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 3050 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/AMT-11-4273-2018 | 2,018 | Parameterizing cloud top effective radii from satellite retrieved values, accounting for vertical photon transport: quantification and correction of the resulting bias in droplet concentration and liquid water path retrievals | Abstract. Droplet concentration (Nd) and liquid water path (LWP) retrievals from passive satellite retrievals of cloud optical depth () and effective radius (re) usually assume the model of an idealized cloud in which the liquid water content (LWC) increases linearly between cloud base and cloud top (i.e. at a fixed fraction of the adiabatic LWC). Generally it is assumed that the retrieved re value is that at the top of the cloud. In reality, barring re retrieval biases due to cloud heterogeneity, the retrieved re is representative of smaller values that occur lower down in the cloud due to the vertical penetration of photons at the shortwave-infrared wavelengths used to retrieve re. This inconsistency will cause an overestimate of Nd and an underestimate of LWP (referred to here as the penetration depth bias), which this paper quantifies via a parameterization of the cloud top re as a function of the retrieved re and . Here we estimate the relative re underestimate for a range of idealized modelled adiabatic clouds using bispectral retrievals and plane-parallel radiative transfer. We find a tight relationship between gre=recloud top/reretrieved and and that a 1-D relationship approximates the modelled data well. Using this relationship we find that gre values and hence Nd and LWP biases are higher for the 2.1 m channel re retrieval (re2.1) compared to the 3.7 m one (re3.7). The theoretical bias in the retrieved Nd is very large for optically thin clouds, but rapidly reduces as cloud thickness increases. However, it remains above 20 % for <19.8 and <7.7 for re2.1 and re3.7, respectively. We also provide a parameterization of penetration depth in terms of the optical depth below cloud top (d) for which the retrieved re is likely to be representative. The magnitude of the Nd and LWP biases for climatological data sets is estimated globally using 1 year of daily MODIS (MODerate Imaging Spectroradiometer) data. Screening criteria are applied that are consistent with those required to help ensure accurate Nd and LWP retrievals. The results show that the SE Atlantic, SE Pacific and Californian stratocumulus regions produce fairly large overestimates due to the penetration depth bias with mean biases of 3235 % for re2.1 and 1517 % for re3.7. For the other stratocumulus regions examined the errors are smaller (2428 % for re2.1 and 1012 % for re3.7). Significant time variability in the percentage errors is also found with regional mean standard deviations of 1937 % of the regional mean percentage error for re2.1 and 3256 % for re3.7. This shows that it is important to apply a daily correction to Nd for the penetration depth error rather than a timemean correction when examining daily data. We also examine the seasonal variation of the bias and find that the biases in the SE Atlantic, SE Pacific and Californian stratocumulus regions exhibit the most seasonality, with the largest errors occurring in the December, January and February (DJF) season. LWP biases are smaller in magnitude than those for Nd (8 to 11 % for re2.1 and 3.6 to 6.1 % for re3.7). In reality, and especially for more heterogeneous clouds, the vertical penetration error will be combined with a number of other errors that affect both the re and , which are potentially larger and may compensate or enhance the bias due to vertical penetration depth. Therefore caution is required when applying the bias corrections; we suggest that they are only used for more homogeneous clouds. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1609/AAAI.V34I01.5379 | 2,020 | Lightweight and robust representation of economic scales from satellite imagery | Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown. | [
{
"id": 11,
"name": "Habitat Conversion/Fragmentation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1088/1748-9326/AC4D4F | 2,022 | A 30 m global map of elevation with forests and buildings removed | Abstract
Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/SD.1647 | 2,017 | The Imperatives of Sustainable Development | AbstractThe United Nations sustainable development goals are under fire. By attempting to cover all that is good and desirable in society, these targets have ended up as vague, weak, or meaningless. We suggest a model for sustainable development based on three moral imperatives: satisfying human needs, ensuring social equity, and respecting environmental limits. The model reflects Our Common Future's central message, moral imperatives laid out in philosophical texts on needs and equity, and recent scientific insights on environmental limits. The model is in conflict with the popular threepillar model of sustainable development, which seeks to balance social, environmental, and economic targets. Rather, we argue that sustainable development constitutes a set of constraints on human behaviour, including constraints on economic activity. By identifying indicators, and thresholds, we illustrate that different regions or groups of countries face different challenges. Copyright 2016 John Wiley & Sons, Ltd and ERP Environment | [
{
"id": 11,
"name": "Habitat Conversion/Fragmentation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/GMD-13-1999-2020 | 2,020 | The first Met Office Unified ModelJULES Regional Atmosphere and Land configuration, RAL1 | Abstract. In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against, and it is the intention that from this point forward significant changes to the system will be documented in the literature. This reproduces the process used for global configurations of the UM, which was first documented as a science configuration in 2011. While it is our goal to have a single defined configuration of the model that performs effectively in all regions, this has not yet been possible. Currently we define two sub-releases, one for mid-latitudes (RAL1-M) and one for tropical regions (RAL1-T). The differences between RAL1-M and RAL1-T are documented, and where appropriate we define how the model configuration relates to the corresponding configuration of the global forecasting model. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1111/RSP3.12441 | 2,021 | Nighttimelights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl Oblast) | AbstractSecond home mobility is a wellknown phenomenon in many countries, but is widely prominent in Russia, where millions of city dwellers move to rural areas during the summertime. Combating longterm economic decline and depopulation, second home mobility creates a promising opportunity to revitalize the countryside. While this phenomenon is largely neglected by official statistics, we suggest using satellite imagery of nighttime lights to investigate its spatial and temporal patterns. We did this with the example of Yaroslavl Oblast in Russia. This region neighbors the Moscow Capital Region. It experiences a significant inflow of second home residents. By tracking the seasonal pixelwise changes of nighttime light radiance in monthly composites of satellite imagery from 2015 to 2019, we located hotspots of second homes and factors determining their spatial spread in rural areas. The results were evaluated with field research. Our results confirmed earlier conclusions that second homes locations in rural areas are largely determined by their proximity to Moscow, natural conditions, and transport accessibility. City dwellers often choose small and even fully abandoned villages for their second homes, which stresses the important role of second home mobility in preserving cultural landscapes. The proposed data and methods are limited by missing data for the northern regions during summer months and are more suitable for areas beyond the urban fringe where nighttimelights data are not biased by the overglow of large cities. | [
{
"id": 16,
"name": "Sun-Earth Interactions"
},
{
"id": 12,
"name": "Heat"
},
{
"id": 11,
"name": "Habitat Conversion/Fragmentation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ISPRSJPRS.2020.01.011 | 2,020 | All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature | Remotely sensed surface longwave downward radiation (LWDR) plays an essential role in studying the surface energy budget and greenhouse effect. Most existing satellite-based methods or products depend on variables that are not readily available from space such as, liquid water path, air temperature, vapor pressure and/or cloud-base temperature etc., which seriously restrict the wide applications of satellite data. In this paper, new nonlinear parameterizations and a machine learning-based model for deriving all-sky LWDR are proposed based only on land surface temperature (LST), column water vapor and cloud-top temperature (CTT), that are relatively readily available day and night for most satellite missions. It is the first time to incorporate the CTT in the parameterizations for estimating LWDR under the cloudy-sky conditions. The results reveal that the new models work well and can derive all-sky global LWDR with reasonable accuracies (RMSE <23 W/m2, bias <2.0 W/m2). The convenience of input data makes the new models easy to use, and thus will definitely expand the applicability of remotely sensed measurements in radiation budget fields and many land applications. | [
{
"id": 16,
"name": "Sun-Earth Interactions"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.AGRFORMET.2021.108806 | 2,022 | Evaluation of evapotranspiration estimation under cloud impacts over | Evapotranspiration (ET) is an important component of the hydrological cycle and energy balance in a land-atmosphere system. Satellite remote sensing has been widely used to estimate regional and global ET, but most previous methods depend on optical measurements that are limited to cloud-free conditions. This makes ET estimation challenging under cloudy sky. Currently, evaluations of satellite ET estimation under various cloud conditions remain lacking at the regional scale. Owing to the ability to penetrate clouds, satellite passive microwave measurements are powerful tools for retrieving ET under clouds. This study evaluated a satellite microwave-based daily ET method under all sky conditions over the part of China between 18°N and 50°N from 2003 to 2010, using microwave emissivity difference vegetation index (EDVI) as the proxy of vegetation water content (VWC). Validations using the surface water balance method found that the estimated ET (EDVI-ET) had an overall small bias (6.18%) in eight river basins. EDVI-ET displayed consistent spatiotemporal patterns with global MOD16 ET, with high spatial correlation (R>0.71) and monthly temporal correlation (R>0.82) throughout four seasons. Their differences were also small (<0.56mmday<SUP>-1</SUP>) in forests, savannas, grass/shrubs, and croplands. Furthermore, cloud impacts on the regional ET were found to be significant and spatiotemporally heterogeneous. Both EDVI-ET and in-situ observations at seven flux towers indicated that cloud-induced reduction in daily ET could exceed 30% when the cloud cover increased by 60% (R<SUP>2</SUP> of 0.42, fitting line slope of 0.80, p<0.001). Under increased cloud conditions in summer, the changes in net radiation dominated the ET over dense vegetation in southern China, while the roles of air temperature and humidity increased over water-stressed barrens and short vegetation in northwest China. VWC affected EDVI-ET under clouds in temperate transitional zones from flatlands to highlands. This study highlighted the importance of cloud impacts in satellite ET estimation. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.AGRFORMET.2013.04.003 | 2,013 | An efficient method for global parameter sensitivity analysis and its applications to the Australian community land surface model (CABLE) | State-of-the-art global land surface models (LSMs) have a large number (i.e. a few hundred) of parameters. Many of those parameters are poorly constrained and are therefore very uncertain. Usually only a few of the parameters are responsible for changes in the model output of interest. Identifying those parameters that have a significant effect on the model output is an important step before applying parameter estimation methods using observations. However this has not been done systematically for any global LSMs yet, because of the computational costs involved. Here, we introduce a global sensitivity analysis method that is widely used in chemical engineering. This method includes two steps: a screening step that ranks all model parameters by their importance on model output in order to select the potentially important parameters and a second step that aims to quantify the contribution to the variance of model output by each of the pre-selected parameters and by their interactions. This method can be readily applied to any model. Here we apply this method to the Australian community land surface model (CABLE) as an example, and find that the two-step approach is efficient as only 690 model simulations are required to identify the few important parameters amongst the 22 parameters for each of the 10 plant functional types (PFTs) in the first step. Another 256 model simulations are required for the variance based analysis in the second step. We find that the leaf maximum carboxylation rate (vcmax) is by far the most important parameter for global annual gross primary productivity (GPP) across all PFTs. However, if focusing on annual latent heat flux (LE) the importance of the parameters is very much PFT dependent. We suggest that this two-step approach should be used to identify important parameters in global LSMs, so that observations to constrain parameters can be used more efficiently in a subsequent parameter estimation step. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/JCLI-D-18-0605.1 | 2,019 | Temperature and salinity variability in the SODA3, ECCO4r3, and ORAS5 ocean reanalyses, 1993–2015 | Abstract This study extends recent ocean reanalysis comparisons to explore improvements to several next-generation products, the Simple Ocean Data Assimilation, version 3 (SODA3); the Estimating the Circulation and Climate of the Ocean, version 4, release 3 (ECCO4r3); and the Ocean Reanalysis System 5 (ORAS5), during their 23-yr period of overlap (19932015). The three reanalyses share similar historical hydrographic data, but the forcings, forward models, estimation algorithms, and bias correction methods are different. The study begins by comparing the reanalyses to independent analyses of historical SST, heat, and salt content, as well as examining the analysis-minus-observation misfits. While the misfits are generally small, they still reveal some systematic biases that are not present in the reference Hadley Center EN4 objective analysis. We next explore global trends in temperature averaged into three depth intervals: 0300, 3001000, and 10002000 m. We find considerable similarity in the spatial structure of the trends and their distribution among different ocean basins; however, the trends in global averages do differ by 30%40%, which implies an equivalent level of disagreement in net surface heating rates. ECCO4r3 is distinct in having quite weak warming trends while ORAS5 has stronger trends that are noticeable in the deeper layers. To examine the performance of the reanalyses in the Arctic we explore representation of Atlantic Water variability on the Atlantic side of the Arctic and upper-halocline freshwater storage on the Pacific side of the Arctic. These comparisons are encouraging for the application of ocean reanalyses to track ocean climate variability and change at high northern latitudes. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ACP-22-245-2022 | 2,022 | OClO as observed by TROPOMI: a comparison with meteorological parameters and polar stratospheric cloud observations | Abstract. Chlorine dioxide (OClO) is a by-product of the ozone-depleting halogen chemistry in the stratosphere. Although it is rapidly photolysed at low solar zenith angles (SZAs), it plays an important role as an indicator of the chlorine activation in polar regions during polar winter and spring at twilight conditions because of the nearly linear dependence of its formation on chlorine oxide (ClO). Here, we compare slant column densities (SCDs) of chlorine dioxide (OClO) retrieved by means of differential optical absorption spectroscopy (DOAS) from spectra measured by the TROPOspheric Monitoring Instrument (TROPOMI) with meteorological data for both Antarctic and Arctic regions for the first three winters in each of the hemispheres (November 2017October 2020). TROPOMI, a UVVisNIRSWIR instrument on board of the Sentinel-5P satellite, monitors the Earth's atmosphere in a near-polar orbit at an unprecedented spatial resolution and signal-to-noise ratio and provides daily global coverage at the Equator and thus even more frequent observations at polar regions. The observed OClO SCDs are generally well correlated with the meteorological conditions in the polar winter stratosphere; for example, the chlorine activation signal appears as a sharp gradient in the time series of the OClO SCDs once the temperature drops to values well below the nitric acid trihydrate (NAT) existence temperature (TNAT). Also a relation of enhanced OClO values at lee sides of mountains can be observed at the beginning of the winters, indicating a possible effect of lee waves on chlorine activation. The dataset is also compared with CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) polar stratospheric cloud (PSC) observations. In general, OClO SCDs coincide well with CALIOP measurements for which PSCs are detected. Very high OClO levels are observed for the northern hemispheric winter 2019/20, with an extraordinarily long period with a stable polar vortex being even close to the values found for southern hemispheric winters. An extraordinary winter in the Southern Hemisphere was also observed in 2019, with a minor sudden stratospheric warming at the beginning of September. In this winter, similar OClO values were measured in comparison to the previous (usual) winter till that event but with a OClO deactivation that was 12 weeks earlier. | [
{
"id": 1,
"name": "Air Quality"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/AMT-13-2257-2020 | 2,020 | A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations | Abstract. We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 m), and the daytime model uses five Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 m) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland, grassland, snow and ice, barren desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models was compared against collocated CALIOP products from 2017. It is shown that, when using a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison to the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top three algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, particularly for pixels over snow and ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1109/CISCE55963.2022.9851159 | 2,022 | A Convolution Neural Network-based Method for Sea Ice Remote Sensing using GNSS-R Data | Sea ice remote sensing is of great significance to the understanding of polar climate change. At present, the global navigation satellite system reflector (GNSS-R) technology has been applied to the study of sea ice remote sensing and achieved good results. In this paper, a convolution neural network (CNN) based method for sea ice recognition (SIR) and estimation of sea ice concentration (SIC) using GNSS-R data is proposed. Specifically, a CNN model is designed to solve the classification problem of SIR and the regression problem of SIC estimation. In the stage of data set construction, first, the global GNSS-R data (TDS-l), in a certain period of time, is spatiotemporally matched with the relatively reliable sea ice data (NSIDC), and then the matched GNSS-R data is extracted to balance the amount of seawater data and sea ice data. In the stage of CNN model construction, the feature learning ability of the model is enhanced by adding convolution layer, pooling layer and full connection layer. Simulation results show that the proposed CNN -based scheme has a higher prediction accuracy of SIR and lower estimation error of SIC than other existing methods. | [
{
"id": 3,
"name": "Cryospheric Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ESSD-12-611-2020 | 2,020 | Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product | Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS14061418 | 2,022 | An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities | An algorithmic approach, based on satellite-derived sea-surface (skin) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (bulk) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/MWR-D-22-0145.1 | 2,022 | A Microphysics-scheme Consistent Snow Optical Parameterization for the Community Radiative Transfer Model | Abstract The satellite observational data assimilation community requires consistent hydrometer descriptionsincluding mass-size relation and particle size distributionto be used in both the forecast model and observation operator. We develop a microphysics scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass-size relation. Snow single-scattering properties are computed for four different mass-size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics scheme-consistent snow bulk optical properties are derived. The Thompson scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM) version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root mean square errors (RMSEs) of 4.3, 13.0, 1.8, and 3.3 K in the 166 GHz (vertical polarization), 166 GHz (horizontal polarization), 1833 GHz (vertical polarization), and 1837 GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics scheme-consistent snow optical parameterization in data assimilation applications. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1007/978-3-030-46133-1_40 | 2,020 | An aggregate learning approach for interpretable semi-supervised population prediction and disaggregation using ancillary data | Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infrastructure, development policies, etc. Disaggregating these census is a complex machine learning, and multiple solutions have been proposed in past research. We propose in this paper to view the problem in the context of the aggregate learning paradigm, where the output value for all training points is not known, but where it is only known for aggregates of the points (i.e. in this context, for regions of pixels where a census is available). We demonstrate with a very simple and interpretable model that this method is on par, and even outperforms on some metrics, the state-of-the-art, despite its simplicity. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2019WR026621 | 2,020 | Groundwater Storage Loss Associated With Land Subsidence in Western | Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km<SUP>3</SUP>/yr of groundwater storage was lost due to groundwater pumping-induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS12182933 | 2,020 | Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway | The ChinaPakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.GEOMORPH.2020.107451 | 2,021 | Major geomorphic events and natural hazards during monsoonal precipitation 2018 in the Kali Gandaki Valley, Nepal Himalaya | Highest geomorphic activity in the central Nepal Himalayas is mainly driven by monsoonal precipitation. In contrast, the northern flanks of the Nepal Himalaya lay in the relative dry rain shadow of the mountain range. During the monsoon 2018, major floods and geomorphic events occurred in the Kali Gandaki (KG) valley impacting both the monsoon-affected and the dry parts of the catchments. We analyze the events and its triggers based on field observations, multiple satellite image interpretation, climatological analysis using Global Precipitation Measurement and MODIS snow cover data, hydrological analysis and media analysis. The hydro-meteorological triggers are complex. Exceptional precipitation in April and May 2018 occurred in the entire study area, followed by a rather dry period. Precipitation in August was exceptional in the northern part whereas below average in the South. We argue that dynamics of snow accumulation and delayed melting contributed significantly to flooding and increased geomorphic activity in the southern part in August whereas flooding in the northern part was mainly triggered by rainfalls. We thus define 2018 as an abnormal (pre-)monsoon year with less rainfall than average but being more catastrophic. Sediment dynamics in the study area are still controlled by the Dhampu rock avalanche dam and the braided river floodplain north of this knickpoint, where sediment pulses delivered from tributaries are rarely connected from the main river. During the monsoon floods 2018 sediment connectivity was given for most tributaries due to the steepness of the catchments. The study area is subject to major human impact. Mostly in the south, numerous hillslopes have been undercut by road construction, leading to higher geomorphic sensitivity. Severe landslides might thus be triggered in future even by less intense rainfall events. Magnitude and frequency of such abnormal (pre-)monsoon precipitation are highly relevant for sediment flux and natural hazards studies. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 2,
"name": "Atmospheric/Ocean Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ECOINF.2021.101305 | 2,021 | Evaluating the effect of ecological policies from the pattern change of persistent green patches–A case study of Yan'an in China's Loess Plateau | Evaluation from landscape pattern change can comprehensively reflect the impact of ecological policies on the ecosystem service function. However, previous assessments based on landscape patterns only considered land cover type and ignored the quality of vegetation cover, which could hardly reflect regional differences in restoration durability and sustainability. Based on the temporal phase characteristics of the vegetation index, this study proposes the concept of Persistent Green Patches (PGPs). Taking Yan'an, a key area for the implementation of ecological projects in the Loess Plateau, as a study area, the effect of ecological policies was evaluated from the pattern change of PGPs from 2000 to 2017 through Morphological Spatial Pattern Analysis (MSPA). It is found that (1) the area of PGPs was increased from 14.45% to 44.26% in Yan'an since the implementation of the ecological projects; (2) the Grain for Green projects can hardly form a short-term increase of PGPs; while the Natural Forest Protection projects can quickly increase the PGPs area, its effect would tend to be saturated or even decrease in the long term; (3) the fragmentation and connectivity of the landscape show that the ecological projects promote the connection between different green patches, and improves the overall connectivity of the PGPs. This study provides a new perspective on evaluating the effect of ecological projects, which is expected to provide a reference for the future optimization of relevant ecological policies and regional sustainable development. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/GEA.21866 | 2,021 | Human-environment interactions in a Bahamian dune landscape: A | Investigations were undertaken following the discovery of two Lucayan burials in an Atlantic coast sand dune on Long Island, The Bahamas (site LN‑101), in the aftermath of Hurricane Joaquin in 2015. The dune burials were the first of their kind to be documented and systematically excavated, and they were associated with uncommon Atlantic coast Lucayan sites. We describe the first systematic archaeological prospection and investigation of coastal geomorphology in the region, applying grain‑size analysis to assess the dune's natural history; basic geochemical analysis to detect anthropogenic impacts and determine agricultural potential; radiocarbon dating as a chronological anchor for reconstructing dune development; drone mapping and georeferencing to document landscape trajectories; and the potential of clay‑like soils with respect to pottery production. Significantly, the dune was relatively stable during and after Lucayan occupation, before Hurricane Joaquin stripped about 10 m from the dune face. The results contribute to refined modeling of past and future impacts, especially those attributed to modern climate change, by linking changes in geomorphology to human activities that began over 1000 years ago. The study contributes to a growing body of Caribbean research into the deep‑time impacts of sea‑level change, coastal geomorphology, erosion, climate change, and hurricane activity. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/ESSD-14-4445-2022 | 2,022 | HMRFS-TP: long-term daily gap-free snow cover products over the Tibetan | Abstract. Snow cover plays an essential role in climate change and
the hydrological cycle of the Tibetan Plateau. The widely used Moderate
Resolution Imaging Spectroradiometer (MODIS) snow products have two major
issues: massive data gaps due to frequent clouds and relatively low estimate
accuracy of snow cover due to complex terrain in this region. Here we
generate long-term daily gap-free snow cover products over the Tibetan
Plateau at 500 m resolution by applying a hidden Markov random field (HMRF)
technique to the original MODIS snow products over the past two decades. The
data gaps of the original MODIS snow products were fully filled by optimally
integrating spectral, spatiotemporal, and environmental information within
HMRF framework. The snow cover estimate accuracy was greatly increased by
incorporating the spatiotemporal variations of solar radiation due to
surface topography and sun elevation angle as the environmental contextual
information in HMRF-based snow cover estimation. We evaluated our snow
products, and the accuracy is 98.29 % in comparison with in situ observations, and
91.36 % in comparison with high-resolution snow maps derived from Landsat
images. Our evaluation also suggests that the incorporation of
spatiotemporal solar radiation as the environmental contextual information
in HMRF modeling, instead of the simple use of surface elevation as the
environmental contextual information, results in the accuracy of the snow
products increases by 2.71 % and the omission error decreases by 3.59 %.
The accuracy of our snow products is especially improved during snow
transitional period, and over complex terrains with high elevation and
sunny slopes. The new products can provide long-term and spatiotemporally
continuous information of snow cover distribution, which is critical for
understanding the processes of snow accumulation and melting, analyzing its
impact on climate change, and facilitating water resource management in
Tibetan Plateau. This dataset can be freely accessed from the National
Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272204
(Huang and Xu, 2022). | [
{
"id": 3,
"name": "Cryospheric Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.RSE.2020.111680 | 2,020 | Soil moisture experiment in the Luan River supporting new satellite mission opportunities | The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/JHM-D-15-0157.1 | 2,016 | Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System | Abstract The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2018JG004799 | 2,019 | Seasonal Precipitation Legacy Effects Determine the Carbon Balance of a Semiarid Grassland | Semiarid grasslands are water-limited ecosystems where precipitation (PPT) controls the onset and duration of the growing season; however, this variable does not fully explain interannual variability of productivity at temporal scales. We examined the relationship between PPT and carbon (C) fluxes in a semiarid grassland at both seasonal and interannual scales, as well as the role of lagged effects of PPT and asymmetric sensitivities of net ecosystem carbon exchange to PPT and its components (gross ecosystem exchange [GEE] and ecosystem respiration [ER]). Six years of continuous net ecosystem C exchange data measured with the eddy covariance technique and GEE estimated with 15 years of enhanced vegetation index and the gross primary productivity of Moderate Resolution Imaging Spectroradiometer were used. The semiarid grassland was a C source and a C sink among contrasting PPT years (114 to -107 g C·m<SUP>-2</SUP>·year<SUP>-1</SUP>). At seasonal scale, PPT relationship with the 15 years of GEE derived from enhanced vegetation index and gross primary productivity was sigmoidal. Moreover, PPT legacies of the previous dry season determined the C balance of the grassland by affecting the C uptake and ecosystem respiration of the following growing season, but productivity was more sensitive to PPT changes than respiration. Models of climate change for semiarid grasslands in North America predict up to 30% reduction of winter-spring PPT and slight summer PPT decrease. Thus, our results suggest that future changes in PPT may have a strong impact on the C uptake capacity of this ecosystem due to weakened legacy effects in summer C uptake. | [
{
"id": 6,
"name": "Ecosystems"
}
] |
null | null | null | null | null | null | null | null | null | 10.1590/0001-3765202220211352 | 2,022 | Landslides associated with recent road constructions in the Río Lucma catchment, eastern Cordillera Blanca, Peru | Abstract Extensive road construction works recently took place in the remote eastern part of the Peruvian Cordillera Blanca, aiming at a better connection of isolated mountain communities with regional administrative centres. Here we document and characterize landslides associated with these road construction efforts in the Rio Lucma catchment, Peru. We show that a total area of 321,332 m2 has been affected by landslides along the 47.1 km of roads constructed between 2015 and 2018. While landslides downslope the roads (48.2%) and complex landslides crossing the roads (46.4%) were the most frequent landslide types in relation to the position of the road; slide-type movement (60.7%) prevails over the flow-type movement (39.3%). Timewise, we found that 75.0% of landslides were observed simultaneously with road construction work, while the remaining 25.0% occurred up to seven months after the roads had been constructed. We plotted the lagged occurrence of these subsequent landslides against precipitation data, showing that 85.7% of them were observed during the wet season (November to April). We conclude that the majority of mapped landslides were directly associated with road constructions and that the road constructions also may set preconditions for landslides, which mainly occurred during the subsequent wet season. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS14153629 | 2,022 | Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types | In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems. | [
{
"id": 3,
"name": "Cryospheric Indicators"
},
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.RSE.2020.111668 | 2,020 | Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture | Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.710.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.650.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System. | [
{
"id": 3,
"name": "Cryospheric Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1007/S41748-021-00213-W | 2,021 | Investigating decadal changes of multiple hydrological products and land-cover changes in the Mediterranean Region for 2009–2018 | Land-cover change is a critical concern due to its climatic, ecological, and socioeconomic consequences. In this study, we used multiple variables including precipitation, vegetation index, surface soil moisture, and evapotranspiration obtained from different satellite sources to study their association with land-cover changes in the Mediterranean region. Both observational and modeling data were used for climatology and correlation analysis. Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Global Land Data Assimilation System (GLDAS) were used to extract surface soil moisture and evapotranspiration data. Intercomparing the results of FLDAS and GLDAS suggested that FLDAS data had better accuracy compared to GLDAS for its better coherence with observational data. Climate Hazards Group Infra-Red Precipitation with Station Data (version 2.0 final) (CHIRPS Pentad) were used to extract precipitation data while Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to extract the vegetation indices used in this study. The land-cover change detection was demonstrated during the 20092018 period using MODIS Land-Cover data. Some of the barren and crop lands in Euphrates-Tigris and Algeria have converted to low-vegetated shrublands over the time, while shrublands and barren areas in Egypts southwestern Delta region became grasslands. These observations were well explained by changing trends of hydrological variables which showed that precipitation and soil moisture had higher values in the countries located to the east of the Mediterranean region compared to the ones on the west. For evapotranspiration, the countries in the north had lower values except for countries in Europe such as Bosnia, Romania, Slovenia, and countries in Africa such as Egypt and Libya. The enhanced vegetation index appeared to be decreasing from north to south, with countries in the north such as Germany, Romania, and Czechia having higher values, while countries in the south such as Libya, Egypt, and Iraq having lower trends. Time series analysis for selected countries was also done to understand the change in hydrological parameters, including Enhanced Vegetation Index, evapotranspiration, and soil moisture, which showed alternating drop and rise as well as stagnant values for different parameters in each country. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS13173531 | 2,021 | Projecting Future Vegetation Change for Northeast China Using CMIP6 Model | Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (20412070) and 0.39 for the late century (20712100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS13030347 | 2,021 | Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS | Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 020 cm and p < 0.005 at 050 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (20102019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/LAND11060808 | 2,022 | Potential Variation of Evapotranspiration Induced by Typical Vegetation Changes in Northwest China | Evapotranspiration (ET), as a key eco-hydrological parameter, plays an important role in understanding sustainable ecosystem development. Each plant category has a unique functional trait on transpiration and photosynthesis, with ET implying that water cycle and energy transformation is linked with vegetation type. Changes in surface vegetation directly alter biophysical land surface properties, hence affecting energy and ET transfer. With the rapid increase in land surface changes, there is a need to further understand and quantify the effects of vegetation change on ET, especially over the vulnerable water-cycle region in the arid and semi-arid regions of Northwest China. We adopted the GlobalLand30 land cover and MOD16A2 in 2010 and 2020 to investigate, discuss the spatio-temporal characteristics of annual and seasonal ET of cultivated land, grassland, and forests in Northwest China, and quantify the impact on vegetation changes with absolute and relative changes from different climatic subecoregions on ET. Our results show the following: (1) Forest ET was generally the highest at 688 mm, followed by cultivated land and grassland with 200400 mm in arid climatic subecoregions. (2) Returning cultivated land to forests and cultivated land expansion potentially enhances ET by 90110 mm/10a, with the relative rate of change increasing by 22.1% and 45.8%, respectively, away from unchanged vegetation within identical subecoregions. (3) The ET of most investigated areas gains the highest value in summer, followed by spring, autumn, and winter. This study provides reference for sustainable ecosystem development and the reasonable utilization of limited water resources in Northwest China. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2021JG006568 | 2,022 | Quantification of Urban Forest and Grassland Carbon Fluxes Using Field Measurements and a Satellite‐Based Model in Washington DC/Baltimore Area | Cities are taking the lead on climate change mitigation with ambitious goals to reduce carbon dioxide (CO<SUB>2</SUB>) emissions. The implementation of effective mitigation policies will require accurate measurements to guide policy decisions and monitor their efficacy. Here, we present a comprehensive CO<SUB>2</SUB> inventory of an urban temperate forest and unmanaged grassland using field observations. We estimate the annual storage of CO<SUB>2</SUB> by vegetation and soils and place our biogenic flux estimates in the context of local fossil fuel (FF) emissions to determine when, where, and by how much biogenic fluxes alter net CO<SUB>2</SUB> flux dynamics. We compare our hourly estimates of biogenic fluxes in the forest site to modeled estimates using a modified version of Urban-Vegetation Photosynthesis and Respiration Model (Urban-VPRM) in Washington DC/Baltimore area presenting the first urban evaluation of this model. We estimate that vegetation results in a net biogenic uptake of -2.62 ± 1.9 Mg C ha<SUP>-1</SUP> yr<SUP>-1</SUP> in the forest site. FF emissions, however, drive patterns in the net flux resulting in the region being a net source of CO<SUB>2</SUB> on daily and annual timescales. In the summer afternoons, however, the net flux is dominated by the uptake of CO<SUB>2</SUB> by vegetation. The Urban-VPRM closely approximates hourly forest inventory based estimates of gross ecosystem exchange but overestimates ecosystem respiration in the dormant season by 40%. Our study highlights the importance of including seasonal dynamics in biogenic CO<SUB>2</SUB> fluxes when planning and testing the efficacy of CO<SUB>2</SUB> emission reduction polices and development of monitoring programs. | [
{
"id": 10,
"name": "Greenhouse Gases"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.SCITOTENV.2022.158819 | 2,023 | Soil moisture drives the spatiotemporal patterns of asymmetry in vegetation productivity responses across China | Increasingly drastic global change is expected to cause hydroclimatic changes, which will influence vegetation productivity and pose a threat to the terrestrial carbon sink. Asymmetry represents an imbalance between vegetation growth and loss of growth during dry and wet periods, respectively. However, the mechanisms of asymmetric plant responses to hydrological changes remain poorly understood. Here, we examined the spatiotemporal patterns of asymmetric responses of vegetation productivity across terrestrial ecosystems in China. We analyzed several observational and satellite-based datasets of plant productivity and several reanalyzed datasets of hydroclimatic variables from 2001 to 2020, and used a random forest model to assess the importance of hydroclimatic variables for these responses. Our results showed that the productivity of >50 % of China's vegetated areas showed a more positive asymmetry (2.3 9.4 %) over the study period, which were distributed broadly in northwest China (mainly grasslands and sparse vegetation ecosystems). Negative asymmetries were most common in forest ecosystems in northeast China. We demonstrated that one-third of vegetated areas tended to exhibit significant changes in asymmetry during 20012020. The trend towards stronger positive asymmetry (0.95 % yr1) was higher than that towards stronger negative asymmetry (0.55 % yr1), which is beneficial for the carbon sink. We further showed that in China, soil moisture was a more important driver of spatiotemporal changes in asymmetric productivity than precipitation. We identified thresholds of surface soil moisture (2030 %, volume water content) and root-zone soil moisture (200350 mm, equivalent water height) that were associated with changes in asymmetry. Our findings highlight the necessity of considering the dynamic responses of vegetation to hydrological factors in order to fully understand the physiological growth processes of plants and avoid the possible loss of productivity due to future climate change. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/IJERPH18020416 | 2,021 | Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019 | Grassland covers 54% of the Tibetan Plateau (TP) and suffered overgrazing and degradation problems during past decades. To alleviate these problems, a series of policy measures have been implemented during recent two decades and inevitably caused changes of the grassland. To this end, this study quantitatively analyzed the grassland changes and the effects of reduced grazing intensity, and identified the hot plots of grassland degradation in the TP during 20002019. The grassland status was indicated by the Fractional Vegetation Cover in the green grass period (GP), i.e., FVCGP, and its changes and spatial variations were detected by analyzing the FVCGP trends and their distribution, using the MannKendal, Sens Slope, and ArcGIS buffering methods, and data of the MOD13Q1 Collection 6 products and other sources. The results showed that 62.12% of the grasslands were significantly increased in the FVCGP, and 28.34% had no apparent changes. The remaining 9.54% of the grassland significantly decreased in the FVCGP, mainly occurring in the areas nearby roads, rivers, and lakes, and distributed mostly in a point pattern. Of the total FVCGP decreased grassland area, 27.03% was clustered and identified as the hot plots of grassland degradation in six main regions. Decreased grazing intensity and increased precipitation contributed to the increase of grassland FVC in the TP, while local overgrazing could be the main cause of the FVC decrease. To strength the grassland restoration in the TP, the government supports and supervision should be enhanced to further mitigate the grassland pressure of animal grazing, particularly in the hot plot areas of degradation. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1175/JAMC-D-18-0219.1 | 2,019 | Variations in surface albedo arising from flooding and desiccation cycles on the Bonneville Salt Flats, Utah | Abstract Desert playas, such as those in northern Utah, form a landscape often in stark contrast to surrounding mountain ranges due to their minimal topographic relief, lack of vegetation, and saline soils. Dry highly reflective halite surfaces, which make up many of the desert playas in northern Utah, are generally characterized by a surface albedo over 40%. However, their albedo can be reduced abruptly to less than 20% by flooding due to rainfall, runoff from surrounding higher terrain, or surface winds transporting shallow water across the playas. A weather station installed during September 2016 to study the Bonneville Salt Flats (BSF) in northern Utah provides estimates of surface albedo that can be related to cycles of flooding and desiccation of the halite surface. The normalized difference water index (NDWI) derived from the MODIS MOD09A1 land surface reflectance product estimates the fractional coverage of surface water over the BSF. NDWI values computed over 8-day periods from 2000 to 2018 highlight year-to-year and seasonal variations in playa flooding events over the BSF. Periods of playa flooding were observed with both ground-based observations and NDWI with sharp reductions in albedo when the surface is flooded. | [
{
"id": 9,
"name": "Floods"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/SOIL-7-217-2021 | 2,021 | SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty | Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.14712/23361980.2022.5 | 2,022 | Current Global Land Systems Classifications: Comparison of Methods and Outputs | The anthropogenic impact on the functioning of natural systems and the concept of Anthropocene as a period of the human domination of the Earth has been widely discussed in literature in the past few decades. Consequently, several land systems classifications have been developed on a global scale to capture the diversity, intensity, and spatial distribution of the human suppression of natural stratification. This review presents the comparison of the most widely used complex global classifications, incorporating both natural conditions and the human influence on nature. Methods, input data, the number and type of output categories as well as their geographical extent and distribution are described and compared. The review will help potential users to find differences between available classifications and choose the right one for a particular use. | [
{
"id": 11,
"name": "Habitat Conversion/Fragmentation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/LDR.3494 | 2,020 | A novel similar habitat potential model based on sliding-window technique for vegetation restoration potential mapping | Vegetation restoration potential mapping (VRPM) is of great importance for regional ecosystem restoration planning after long‑term land degradation or short‑term disasters. However, there are some problems to be solved in the current models for evaluating the potential of vegetation restoration. First, the models for VRPM are mostly established based on a knowledge‑driven index system. Although this kind of model is logically rigorous, it relies too much on expert knowledge and is relatively inefficient, especially for large‑area vegetation restoration assessments. Second, because of the spatial heterogeneity, as well as the absence of important indicators, the traditional global‑based evaluation models are difficult to adapt to the entire study area. In this study, an improved data‑driven approach, that is, a sliding‑window based similar habitat potential model, is developed for VRPM. The advantages of the new model include: (a) it is more efficient in determining the importance of each influencing factor without resorting to expert knowledge; (b) it is more locally adaptive than the global model because it performs sample training, rule building, and vegetation restoration potential calculation in each current local window. We provide a case‑study to show the modeling process and result interpretation of the new model. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.5194/HESS-26-6311-2022 | 2,022 | Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil … | Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the LundPotsdamJena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated PriestleyTaylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m2 d1; ET: R2 = 0.73, ubRMSD = 0.61 mm d1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m2 d1; ET: R2 = 0.66; ubRMSD = 0.67 mm d1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (): 3.4 mm per month; estimated standard deviation of : 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/LDR.4338 | 2,022 | A novel framework for evaluating the effect of vegetation restoration via the grazing‐exclusion‐by‐fencing project: a case study from the Qinghai‐Tibet Plateau | Grazing exclusion by fencing (GEF) has been implemented to prevent the deterioration of grassland ecosystems in China since the beginning of this century; meanwhile, the effects have attracted widespread attention from the academic community. However, due to the simultaneity of different factors, it is difficult to separate the effect of different policies from natural resource endowments and identify the independent role of each policy. In this study, a novel framework was established based on spatiotemporal statistics. First, an indicator‑denoted vegetation restoration potential achievement degree (VRPAD) was introduced to lessen the impact of resource endowment conditions. Second, a double‑difference model of both space and time was developed to reflect the net improvement of VRPAD brought by GEF. The case study showed that 15 out of the 17 fenced‑off enclosures achieved positive VRPAD growth since the GEF implementation, while only 58.8% showed a much faster improvement or a slower degeneration than their adjacent areas. It was also found GEF effect presented apparent administrative differences in terms of spatial distribution. The number of effective fences accounted for 75% in Mami Town, but it was 0 in Chabu Town. It is concluded that the role of GEF may be overestimated, multi‑pronged approaches are more conducive to vegetation restoration, and strengthening grassroots management and self‑policing of fences is important to improve GEF efficiency. This research provides a novel framework for distinguishing policy effect and is expected to inspire new ideas and methodological support for policy formulation and evaluation. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1007/S12524-020-01195-4 | 2,020 | Increased Surface Broadband Emissivity Driven by Denser Vegetation on the Tibetan Plateau Grassland Area | Global changes are profoundly affecting the global terrestrial ecosystems, especially for the vegetation. Simultaneously, the affected vegetation gives feedback to the climates. The Tibetan Plateau (TP), one of the most sensitive areas to global changes, has undergone extraordinary changes on its ecosystem processes. In the multitudinous land surface ecosystem processes affecting the climate, the process of land surface energy balance affecting by vegetation activity is one of the most important and still has not been well recognized. The spatial and temporal patterns of the broadband emissivity (BBE) on the TP and its relations to the vegetation activity and land surface temperature were examined in this research. We find that elevated BBE is regulated by increasing vegetation activity for grasslands over the TP from 2000 to 2015. The spatial patterns of BBE and its interannual changes are highly correlated with vegetation activity. The BBE changing rate generally declines along rising elevation, due to the shrunk effects from vegetation activity. A greater sensitivity of BBE to vegetation activity occurs in the sparse vegetation area or high elevation zone than in the dense vegetation area or low elevation zone. Increasing BBE has a cooling effect on the land surface, especially at night. This cooling effect is related to wind speed. The growing season BBE trend as regulated by vegetation activity highlights the importance to take mounting notice of the growing season long-wave energy fluxes of surface energy balance studies in the future. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.SCITOTENV.2022.159064 | 2,023 | Spatial patterns and climatic drivers of leaf spring phenology of maple in eastern North America | The resurgent frequency of extreme weather events and their strongly distinctive spatial patterns lead to a growing interest in phenology as an indicator of tree susceptibility. Using a long-term chronology of observations collected in situ, we predicted and investigated the spatial patterns and environmental drivers of spring leaf phenology across maple stand polygons dominated by Acer saccharum Marsh. and/or Acer rubra L. in eastern North America for 20002018. Model calibration was based on Bayesian ordinal regressions relating the timing of the phenological events' observations to the MODIS vegetation indices EVI, NDVI and LAI. DAYMET data have been extracted to compute temperature and precipitation during spring phenology. Model accuracy increased as the season progressed, with prediction uncertainty spanning from 9 days for bud swelling to 4 days for leaf unfolding. NDVI and LAI were the best predictors for the onset and ending of spring phenology, respectively. Bud swelling occurred at the end of March in the early stands and at the onset of May in the late stands, while leaf unfolding was completed at the beginning of April for the early and in mid-June for the late stands. Early and late stands polarized towards a south-westnorth-east gradient. In the south-western regions, which are also the driest, total precipitation and minimum temperature explained respectively 73 % and 25 % of the duration of spring phenology. In the north-eastern regions, precipitation and minimum temperature explained 62 % and 26 % of the duration of spring phenology. Our results suggest high vulnerability to extreme weather events in stands located in the south-west of the species distribution. The increasing incidence of drought in these locations might affect spring phenology, decreasing net primary production in these stands. Warmer nights might expose the buds to late frosts, events that are expected to become more frequent in the coming years. | [
{
"id": 6,
"name": "Ecosystems"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.SCITOTENV.2022.154761 | 2,022 | Sedimentary organic carbon storage of thermokarst lakes and ponds across Tibetan permafrost region | Sedimentary soil organic carbon (SOC) stored in thermokarst lakes and ponds (hereafter referred to as thaw lakes) across high-latitude/altitude permafrost areas is of global significance due to increasing thaw lake numbers and their high C vulnerability under climate warming. However, to date, little is known about the SOC storage in these lakes, which limits our better understanding of the fate of these active carbon in a warming future. Here, by combining large-scale field observation data and published deep (e.g., 0300 cm) permafrost SOC data with a random forest (RF) machine learning technique, we provided the first comprehensive estimation of thaw lake SOC stocks to 3 m depth on the Tibetan Plateau. This study demonstrated that combining multiple environmental factors with the RF model could effectively predict the spatial distributions of the thaw lake SOC density values (SOCDs). The model results revealed that the soil respiration, normalized difference vegetation index (NDVI), and mean annual precipitation (MAP) were the most influential factors for predicting thaw lake SOCDs. In total, the sedimentary SOC stocks in the thaw lakes were approximately 52.62 Tg in the top 3 m, with 53% of the SOC stored in the upper layers (0100 cm). The SOCDs generally exhibited high values in eastern Tibetan Plateau, and low values in mid- and western Tibetan Plateau, which were similar to the patterns of the land cover types that affected the SOCDs. We further found that the SOCDs of thaw lakes were generally higher than those of their surrounding permafrost soils at different layer depths, which could be ascribed to the erosion of soil particles or leaching solution from the thawing permafrost soils to lakes and/or enhanced vegetation growth at the lake bottom. This research highlights the necessity of explicitly considering the thaw lake SOC stocks in Earth system models for more comprehensive future projections of the carbon dynamics on the plateau. | [
{
"id": 3,
"name": "Cryospheric Indicators"
},
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1635/053.166.0118 | 2,020 | Deciphering the many maps of the Xingu River Basin–an assessment of land cover classifications at multiple scales | Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics and considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (19892018) for 1.3 million km2 encompassing the Xingu River Basin, Brazil, from published, freely accessible remotely sensed land cover classifications. While all showed a decrease in forest extent over time, the total deforested area reported by each ranged widely for all time periods. The greatest differences ranged from 9% to 17% (116,958 to 219,778 km2) deforestation of the total area for year 2000 and 20142018 time period, respectively. We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. Surface water classifications must be chosen carefully because sources differ greatly in location and mapped area of surface water. After operationalization of the Belo Monte dam complex, the large reservoirs are notably absent from several of the classifications illustrating land cover. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g., riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate classification for ecological studies, conservation, or policy making. The differences between the classifications examined here are not a failure of the technology, but due to different interpretations of forest cover and characteristics of the input data (e.g., spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed classifications and the need for users to familiarize themselves with the characteristics and limitations of each data set. | [
{
"id": 11,
"name": "Habitat Conversion/Fragmentation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1080/01431161.2019.1641758 | 2,019 | Spatio-temporal quality distribution of MODIS vegetation collections 5 and 6: implications for forest-non-forest separability | Moderate Resolution Imaging Spectroradiometer (MODIS) has been employed for continuous monitoring of land surface dynamics to facilitate the examination of spatial aspects of the environment. Periodical generation of MODIS products enables temporal analysis, and the interpretation of temporal patterns requires information about image quality. The MODIS Scientific Data Set (SDS) provides information on image properties. Some research has utilized the SDS to assist in analysis and interpretation, particularly in supporting time series forecasting and estimating invalid data from near-dates observation. Our research compares the usability and reliability information provided in the MODIS SDS for collections 5 and 6 to describe the spatio-temporal distribution of image quality. This research compared the ability of the MODIS collections to identify the extent of water and to differentiate forest from non-forest. Four sites representing tropical and temperate regions were selected in Brazil, Congo, Colorado (United States of America), and the European Alps. The robustness of usability and reliability information for assessing MODIS vegetation collections 5 and 6 was compared over these sites by using 16-day composite products over a year of observations (2015). The spatio-temporal distribution of invalid pixels and gaps derived from usability and reliability information were assessed by using TiSeG (Time series Generator) and GeoDa. Morans I indicated a large number of invalid pixels and temporal gaps were clustered in a few areas. Collection 6 appears more consistent in the identification of waterbodies, either for inland water or ocean, but the error detection of ice fractions in two tropical sites tends to increase. Masking data by using Quality Assurance (QA)-SDS information improved the separability between forest and non-forest. This research demonstrated that evaluating the quality of image products using the SDS assisted the selection of period and location to better differentiate forest and non-forest. The seasonal fluctuation of separability metrics showed the importance of exploring temporal pattern for better understanding of the dynamics of forest cover. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
},
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.CATENA.2018.05.008 | 2,018 | The spatial distribution of critical wind erosion centers according to the dust event in Hormozgan province (south of Iran) | Wind erosion and its consequent dust storms has become one of the environmental risks in today's world, which has annually caused non-compensable destruction in environment and human health. Since wind erosion is one of the main factors of desertification which could result in dust storms, studying and locating the wind erosion centers in southern parts of Iran is of crucial importance. The aim of this study is to determine the spatial distribution of wind erosion centers associated to local dust event in Hormozgan province (south of Iran). Different factors such as soil features, climate, surface roughness, vegetation cover, topography and the length wind exposure should be investigated for zoning the potential wind erosion regions. Studies showed that many of these factors are relatively uniform for the study region. Therefore, zoning based on all of the above-mentioned factors is not efficient. For this reason, soil texture along with vegetation cover and topography were studied in the current study to zone wind erosion. First, satellite data of soil texture, normalized differential vegetation index (NDVI), and topography were used to address the potential regions of wind erosion in warm and cold periods of the year by ArcGIS. Then, the data of 14 synoptic stations in Hormozgan province were utilized to plot the maps of dust event occurrences. Finally, by combination of the satellite and synoptic data, the map of land sensitivity toward wind erosion was provided, and the obtained results were compared with observations of Forest, Ranges and Watershed Organization (FRWO) of Hormozgan province. The results indicated that there are potential regions for wind erosion and dust sources in the study region. In a way that coastal areas have the highest probability to become wind erosion centers. In this regard, Jask, Bandarabbas and Bandar Lengeh are the first three regions in terms of wind erosion; while Abu Musa has the lowest priority in terms of possessing wind erosion centers. Also, it was revealed that sensitivity to wind erosion and dust storms was higher in warm periods of the year as compared with the cold seasons. The results of this study are in agreement with the observations of FRWO of Hormozgan province. Therefore, desert greening measures and actions to prevent wind erosion can control many of dust storms in the regions. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/2017JG004073 | 2,017 | A Newly Identified Role of the Deciduous Forest Floor in the Timing of Green-Up | Plant phenology studies rarely consider controlling factors other than air temperature. We evaluate here the potential significance of physical and chemical properties of soil (edaphic factors) as additional important controls on phenology. More specifically, we investigate causal connections between satellite-observed green-up dates of small forest watersheds and soil properties in the Adirondack Mountains of New York, USA. Contrary to the findings of previous studies, where edaphic controls of spring phenology were found to be marginal, our analyses show that at least three factors manifest themselves as significant controls of seasonal patterns of variation in vegetated land surfaces observed from remote sensing: (1) thickness of the forest floor, (2) concentration of exchangeable soil potassium, and (3) soil acidity. For example, a thick forest floor appears to delay the onset of green-up. Watersheds with elevated concentrations of potassium are associated with early surface greening. We also found that trees growing in strongly acidified watersheds demonstrate delayed green-up dates. Overall, our work demonstrates that, at the scale of small forest watersheds, edaphic factors can explain a significant percentage of the observed spatial variation in land surface phenology that is comparable to the percentage that can be explained by climatic and landscape factors. We conclude that physical and chemical properties of forest soil play important roles in forest ecosystems as modulators of climatic drivers controlling the rate of spring soil warming and the transition of trees out of winter dormancy. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1038/S41598-022-17787-8 | 2,022 | Abiotic and biotic factors controlling the dynamics of soil respiration in a coastal dune ecosystem in western Japan | In this study, we examined the abiotic and biotic factors controlling the dynamics of soil respiration (Rs) while considering the zonal distribution of plant species in a coastal dune ecosystem in western Japan, based on periodic Rs data and continuous environmental data. We set four measurement plots with different vegetation compositions: plot 1 on bare sand; plot 2 on a cluster of young Vitex rotundifolia seedlings; plot 3 on a mixture of Artemisia capillaris and V. rotundifolia; and plot 4 on the inland boundary between the coastal vegetation zone and a Pinus thunbergii forest. Rs increased exponentially along with the seasonal rise in soil temperature, but summer drought stress markedly decreased Rs in plots 3 and 4. There was a significant positive correlation between the natural logarithm of belowground plant biomass and Rs in autumn. Our findings indicate that the seasonal dynamics of Rs in this coastal dune ecosystem are controlled by abiotic factors (soil temperature and soil moisture), but the response of Rs to drought stress in summer varied among plots that differed in dominant vegetation species. Our findings also indicated that the spatial dynamics of Rs are mainly controlled by the distribution of belowground plant biomass and autotrophic respiration. | [
{
"id": 6,
"name": "Ecosystems"
},
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.3390/RS11242949 | 2,019 | Analysis of groundwater and total water storage changes in Poland using GRACE observations, in-situ data, and various assimilation and climate models | The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programmes Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1002/VZJ2.20163 | 2,021 | A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone | Hard rock aquifers (HRAs) in West Africa (WA) are located within a thick regolith layer. The representation of thick tropical regolith in integrated hydrological models lacks consensus on aquifer geometries and parameter ranges. Our main objective was to determine the knowledge requirements on saturated hydraulic conductivity (Ks) to model the critical zone (CZ) of HRAs in WA. A parametric sensitivity analysis with a focus on the representation of the Ks heterogeneity of the regolith was conducted with a critical zone model (Parflow‑CLM [Community Land Model]) of the Upper Ouémé catchment in Benin (14,000 km2) at a 1‑ × 1‑km2 resolution. The impact of parameter changes in the near subsurface (0.3‑to‑5‑m depth) and in the deeper regolith aquifer (24‑ and 48‑m maximum depth) was assessed in five modeling experiments. Streamflow was largely dependent on Ks and on clay distribution in the near subsurface and less on the properties of the deeper subsurface. Groundwater table depths and amplitudes were controlled by vegetation and topography as observed on instrumented hillslopes and for Ks within the literature range. Experiments with higher Ks suggested a Ks threshold where dynamics become less determined by one‑dimensional vertical and more determined by lateral processes. Such heterogeneity impacts from smaller scales need to be accounted for when hydrological models are upscaled to larger domains (1‑ × 1‑km2 resolution or coarser). Our findings highlight the need for a new conceptual approach to represent clay distribution in order to develop catchment‑scale CZ models of HRAs in WA that capture the observed processes.Core Ideas Simulated water balance components for a catchment in West Africa were confirmed by observations. Subsurface (0.3‑to‑5‑m depth) exerts stronger control on streamflow than deeper regolith. Ks magnitude determines transition from topography‑ to recharge‑controlled water table dynamics. We identified a limit of Ks where the impact of one‑dimensional processes on water table dynamics ceases. A high‑permeability fissured zone at the bottom shows little impact on the simulations. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2018EF000937 | 2,018 | Future Global Soil Respiration Rates Will Swell Despite Regional Decreases in Temperature Sensitivity Caused by Rising Temperature | Between 1960 and 2014, the global soil respiration (R<SUB>SG</SUB>) flux increased at a rate of 0.05 Pg C year<SUP>-1</SUP>; however, future increase is uncertain due to variations in projected temperature and regional heterogeneity. Regional differences in the sensitivity of soil respiration (R<SUB>S</SUB>) to temperature may alter the overall increase in rates of R<SUB>S</SUB> because the R<SUB>S</SUB> rates of some regions may decelerate while others continue to rise. Using monthly global R<SUB>S</SUB> data, we modeled the relationship between R<SUB>S</SUB> and temperature for the globe and eight climate regions and estimated R<SUB>SG</SUB> between 1961and 2100 using historical (1961-2014) and future (2015-2100) temperature data [Representative Concentration Pathways (RCP2.6 and RCP8.5)]. Importantly, our approach allowed for estimation of regional sensitivity, where respiration rates may peak or decline as temperature rises. Estimated historical R<SUB>SG</SUB> increase (0.05 Pg C year<SUP>-1</SUP>) was similar to the R<SUB>SG</SUB> increase of previous estimates. However, under the RCP8.5 scenario, which estimates approximately 3 °C of warming globally, the forecasted acceleration of R<SUB>SG</SUB> increased to an average of 0.12 Pg C year<SUP>-1</SUP>. Under RCP8.5, the temperature sensitivity of R<SUB>S</SUB> declined in the arid, winter-dry temperate, and tropic. These regional declines were offset by increased R<SUB>S</SUB> sensitivity and fluxes from the boreal and polar regions. In contrast, under RCP2.6 R<SUB>SG</SUB> decelerated slightly from current rates. If rising greenhouse gas emission remains unmitigated, future increases in R<SUB>SG</SUB> will be much faster than current and historical rates, thereby possibly enhancing future losses of soil carbon and contributing to positive feedback loops of climate change. | [
{
"id": 10,
"name": "Greenhouse Gases"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ISPRSJPRS.2020.11.019 | 2,020 | Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review | Vegetation phenology is considered an important biological indicator in understanding the behaviour of ecosystems and how it responds to environmental cues. Changes in vegetation dynamics have been strongly linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems, such as the land surface-atmosphere exchange of water and carbon, energy flows and interaction between different species. Land surface phenology (LSP) is the study of seasonal patterns in plant phenophases based on time series from vegetation indices (VI) or biophysical variables derived from satellite data, and has played an essential role in monitoring the response of terrestrial ecosystems to environmental changes from local to global scales. The goal of this systematic literature review is to provide a detailed synthesis of the main contributions of the global LSP research to the development of environmental knowledge and remote sensing science and technology, identifying possible gaps that could be addressed in the coming years. This systematic review found that the number of LSP studies has grown exponentially since the 1980s, although the analysis of phenological metrics or phenometrics derived from satellite data (i.e. proxies for the biological phenophases of plants) has focused specifically on ecosystems located in the mid- and high-altitude in the Northern Hemisphere (e.g. boreal forest/taiga, evergreen, deciduous or mixed temperate forest). LSP studies use different satellite dataset and methods to estimate phenometrics. These studies identified an advance in spring and a delay in autumn phenophases as general trends. Although these trends were associated mainly to changes in temperature and precipitation, phenological cycle dynamics might be related to other drivers, such as photoperiod, soil moisture or organic carbon content, among others. Therefore, this interaction between different climatic and non-climatic drivers make phenology modelling a difficult task. Hence, in the coming years, a greater integration of LSP data into ecological process modelling could provide a more complete overview on the terrestrial ecosystems functioning. Furthermore, different technical and methodological aspects (e.g. greater temporal coverage of recent high-spatial-resolution satellites, advances in remote-sensing technology or improved efficiency in the computational processing of geospatial data) may also contribute to improve our understanding of Earths ecosystem dynamics and their environmental drivers. | [
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1029/2021WR031565 | 2,022 | Leveraging Pre‐Storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins | Despite long-standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium-scale (200-10,000 km<SUP>2</SUP>) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and the storm-scale runoff coefficient (RC; the fraction of storm-scale precipitation accumulation converted into streamflow). In humid and semi-humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin-wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean-absolute error. | [
{
"id": 17,
"name": "Validation"
}
] |
null | null | null | null | null | null | null | null | null | 10.1126/SCIENCE.AAA7185 | 2,016 | Empirical evidence of contrasting soil moisture–precipitation feedbacks across the United States | The effects of rainfall on rainfall
Soil moisture, which is controlled in part by past rainfall, can affect the probability of future rainfall over large areas. This is because the water contained in soils helps determine how sunlight is converted into latent heat (evaporation) and sensible heat (which increases overlying air temperatures). Tuttle and Salvucci used data collected for the contiguous United States over 10 years to study this relationship. The feedback between soil moisture and rainfall is generally positive in the western United States but negative in the east. This regional dependence could be a function of large-scale differences in aridity.
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Past rainfall can affect the probability of future rainfall over large areas.
,
Soil moisture influences fluxes of heat and moisture originating at the land surface, thus altering atmospheric humidity and temperature profiles. However, empirical and modeling studies disagree on how this affects the propensity for precipitation, mainly owing to the difficulty in establishing causality. We use Granger causality to estimate the relationship between soil moisture and occurrence of subsequent precipitation over the contiguous United States using remotely sensed soil moisture and gauge-based precipitation observations. After removing potential confounding effects of daily persistence, and seasonal and interannual variability in precipitation, we find that soil moisture anomalies significantly influence rainfall probabilities over 38% of the area with a median factor of 13%. The feedback is generally positive in the west and negative in the east, suggesting dependence on regional aridity. | [
{
"id": 2,
"name": "Atmospheric/Ocean Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.JHYDROL.2022.128096 | 2,022 | Revising precipitation water storages vegetation signatures with GRACE-based data assimilation | The availability of freshwater is highly influenced by climate change, extreme climate events and by anthropogenic use. Countries where a large part of the population depends on the agricultural sector, such as South Africa, are strongly affected by changes in climate, which emphasizes that water is an essential source for food production and drinking water. To analyze changes in surface and subsurface water, model simulations and in situ data are commonly used. However, both have limitations, for example, the models rely on potentially erroneous forcing data and insufficient process representations and the in situ data do not represent the larger-scale weather and climate due to spatial and temporal heterogeneity. This can be mitigated by assimilating remote-sensed satellite data into models. In this research, we build a realistic picture of the water and its propagation (measured between peak times) from fluxes as precipitation, to its way through the storages and its impact on vegetation at the 50 km scale by using observation-based data. The observations are derived from MODIS remote-sensing and integrating GRACE total water storage anomaly (TWSA) observations into a hydrological model via data assimilation. Our objective is to identify shortcomings in model simulations by confronting them with the (synthesized) observations. Moreover, we demonstrate the importance of integrating observations into the models. We base these comparisons on signatures or sub-signals (e.g., temporal lags and annual amplitudes) that we derive via regression analysis, principal component analysis, sensitivity analysis and correlation analysis from the synthetic data and the model output. Our main results show that correlations and signatures in real observations are found weaker as compared to what is simulated in the model, e.g. for the contribution of precipitation to groundwater. Lag times between precipitation and surface and groundwater storage peaks are observed to be longer than in the model. The observed propagation of soil water from storages to vegetation is often shorter than in the model, while for groundwater it is longer. We believe our findings will be highly relevant for modelers; the gained knowledge can be used to improve models. In addition, we feel our study underlines the potential of GRACE assimilation into hydrological models. | [
{
"id": 17,
"name": "Validation"
},
{
"id": 13,
"name": "Land Surface/Agriculture Indicators"
}
] |
null | null | null | null | null | null | null | null | null | 10.1016/J.ENVSOFT.2022.105467 | 2,022 | Multi-modal temporal CNNs for live fuel moisture content estimation | Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal convolutional neural networks that learn from year-long time series of meteorological and reflectance data, and a few auxiliary inputs including the climate zone. LFMC estimation models are designed for two training and evaluation scenarios, one for sites where historical LFMC measurements are available (within-site), the other for sites without historical LFMC measurements (out-of-site). The models were trained and evaluated using a large database of LFMC samples measured in the field from 2001 to 2017 and achieved an RMSE of 20.87% for the within-site scenario and 25.36% for the out-of-site scenario. | [
{
"id": 20,
"name": "Wildfires"
}
] |
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