Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States

Fang, Bin; Lakshmi, Venkataraman; Bindlish, Rajat; Jackson, Thomas J.; Liu, Pang‐Wei

doi:10.1016/j.jhydrol.2020.125043

Cited by 36 publications

(27 citation statements)

References 92 publications

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“…2016), including a newly developed 1‐km SMAP soil moisture product (Fang et al. 2020; Liu et al. 2021, in revision), and Gravity Recovery and Climate Experiment (GRACE; Tapley et al.…”

Section: Methods: Overview Of Wldasmentioning

confidence: 99%

A High‐Resolution Land Data Assimilation System Optimized for the Western United States

Erlingis

Rodell

Peters‐Lidard

et al. 2021

J American Water Resour Assoc

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The Western Land Data Assimilation System (WLDAS) is a custom instance of the NASA Land Information System that combines land surface parameters, meteorological forcing data, and satellite products within a land surface model to produce daily estimates of the water and energy budget variables for the western United States. WLDAS was configured through discussions with partners, with the goal of groundwater sustainability planning for the state of California in mind. The publicly available output dataset has a 1‐km grid resolution and spans 1979–present, which makes it suitable for water resources assessments. The data are also able to contextualize the recent drought events in California. Assimilation of Leaf Area Index, which is demonstrated herein to improve simulation over agricultural areas in California, specifically in terms of evapotranspiration in irrigation regions, will be included along with other data assimilation in subsequent releases of WLDAS.

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“…2016), including a newly developed 1‐km SMAP soil moisture product (Fang et al. 2020; Liu et al. 2021, in revision), and Gravity Recovery and Climate Experiment (GRACE; Tapley et al.…”

Section: Methods: Overview Of Wldasmentioning

confidence: 99%

A High‐Resolution Land Data Assimilation System Optimized for the Western United States

Erlingis

Rodell

Peters‐Lidard

et al. 2021

J American Water Resour Assoc

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“…Fang et al [26]- [28] utilized vegetation index and land surface temperature (LST) together to downscale the SM products of AMSR-E, AMSR-2, and SMAP. And the downscaling results of SMAP were evaluated and validated over the Continental United States with in-situ data [29]. And there are also some similar studies [25], [30], [31].…”

Section: Introductionmentioning

confidence: 96%

Downscaling SMAP Soil Moisture Products With Convolutional Neural Network

Zhang

Long

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on convolutional neural network (CNN) is proposed to solve the problem. Furthermore, weight layer is designed for the input and residual SM is treated as the output of CNN to improve the accuracy. This method is applied to downscale SMAP SM products (i.e., 36-km L3 SM P and 9-km L3 SM P E) from 1st January 2018 to 30th December 2018. Compared with 9-km L3 SM P E, the 9-km downscaling result is satisfactory with obtained R, RMSE, and ubRMSE values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are all validated by the in-situ data which are collected by the 109 stations of Oklahoma Mesonet (OKM) SM monitoring network.

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“…In the proposed algorithm, models were built under various vegetation conditions based on NDVI for different months to consider the impacts of vegetation cover and temperature on the water fluxes. The approach has been modified and implemented at the watershed and continental domains to disaggregate SM measurements from various passive microwave sensors such as AMSR-E [57], SMOS [60], SMAP [26], [49], [61], and FengYun-3B [62].…”

Section: Introductionmentioning

confidence: 99%

Assessing Disaggregated SMAP Soil Moisture Products in the United States

Liu

Bindlish

Fang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the Soil Moisture Active Passive (SMAP) Enhanced product (SPL2SMP E) from 9 km to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP gridthis MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network (USDA-SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (NOAA-CRN). Results were also compared with the baseline SPL2SMP E and the SMAP/Sentinel-1 (SPL2SMAP S) 1 km product. Overall, the unbiased root mean square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 m 3 /m 3 , which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP S 1 km product by approximately 0.02 m 3 /m 3 . Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP E and SPL2SMAP S by about 0.01 and 0.02 m 3 /m 3 , indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

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Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States

Cited by 36 publications

References 92 publications

A High‐Resolution Land Data Assimilation System Optimized for the Western United States

A High‐Resolution Land Data Assimilation System Optimized for the Western United States

Downscaling SMAP Soil Moisture Products With Convolutional Neural Network

Assessing Disaggregated SMAP Soil Moisture Products in the United States

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