Evaluation of TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) through a Soil Moisture Simulation

Abstract Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.

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Disaggregation of SMAP Soil Moisture at 20 m Resolution: Validation and Sub-Field Scale Analysis

Remote Sensing ◽

10.3390/rs14010167 ◽

2021 ◽

Vol 14 (1) ◽

pp. 167

Author(s):

Giovanni Paolini ◽

Maria Jose Escorihuela ◽

Joaquim Bellvert ◽

Olivier Merlin

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Temporal Resolution ◽

Land Surface ◽

Spatial Scales ◽

Field Scale ◽

In Situ Data ◽

Novel Approach ◽

Spatio Temporal

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson’s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management.

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0108.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2537-2549

Author(s):

Trent W. Ford ◽

Steven M. Quiring ◽

Chen Zhao ◽

Zachary T. Leasor ◽

Christian Landry

Keyword(s):

United States ◽

Soil Moisture ◽

Land Surface ◽

Surface Model ◽

National Scale ◽

Model Soil ◽

Soil Moisture Data ◽

Spatial Coverage ◽

Soil Moisture Monitoring

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

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A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

10.5194/ismc2021-26 ◽

2021 ◽

Author(s):

Jingyi Huang ◽

Ankur Desai ◽

Jun Zhu ◽

Alfred Hartemink ◽

Paul Stoy ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

Field Scale ◽

Land Surface Parameters ◽

Global Surface

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m&#8722;3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08&#8211;0.10 m3 m&#8722;3). The global model was then applied to map SSM dynamics at 30&#8211;100m across a field-scale network (TERENO-W&#252;stebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (&#8220;spiking&#8221;), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.

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The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

10.5194/egusphere-egu2020-16493 ◽

2020 ◽

Author(s):

Daniel Aberer ◽

Irene Himmelbauer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Wouter Dorigo ◽

...

Keyword(s):

Quality Control ◽

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Measurement Techniques ◽

European Space Agency ◽

Web Portal ◽

Soil Moisture Data ◽

Spatial Coverage

The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&#160;In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 &#8211; up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).About 10&#8217;000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.

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Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6699-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 6699-6724 ◽

Cited By ~ 10

Author(s):

Y. Y. Liu ◽

R. M. Parinussa ◽

W. A. Dorigo ◽

R. A. M. de Jeu ◽

W. Wagner ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cumulative Distribution ◽

Surface Model ◽

Carbon Cycles ◽

In Situ Data ◽

Spatial Coverage ◽

Global Soil

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

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Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

10.5194/hess-2017-680 ◽

2018 ◽

Cited By ~ 3

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Neural Networks ◽

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatio Temporal

Abstract. Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3 days repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36 km and 9 km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0070.1 ◽

2015 ◽

Vol 16 (2) ◽

pp. 917-931 ◽

Cited By ~ 25

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Youfei Zheng ◽

Jicheng Liu ◽

Li Fang ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Radiation ◽

Radiation Budget ◽

Shortwave Radiation ◽

Surface Model ◽

Spatial Coverage ◽

In Situ Observations ◽

The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

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Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

Remote Sensing ◽

10.3390/rs11060656 ◽

2019 ◽

Vol 11 (6) ◽

pp. 656 ◽

Cited By ~ 4

Author(s):

Lei Fan ◽

A. Al-Yaari ◽

Frédéric Frappart ◽

Jennifer Swenson ◽

Qing Xiao ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

In Situ Measurements ◽

Mountainous Regions ◽

Elevation Data ◽

Agricultural Applications ◽

Moderate Resolution Imaging Spectroradiometer ◽

Topography Data

Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

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SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Scientific Data ◽

10.1038/s41597-021-01050-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Noemi Vergopolan ◽

Nathaniel W. Chaney ◽

Ming Pan ◽

Justin Sheffield ◽

Hylke E. Beck ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Temporal Correlation ◽

Ecosystem Dynamics ◽

Link Type ◽

Current State ◽

Current Monitoring ◽

Radiative Transfer Modeling

AbstractSoil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

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