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Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Brian Barrett, Edward Dwyer, Pádraig Whelan
2009 Remote Sensing  
Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods.  ...  to achieve routine use of the proposed retrieval approaches.  ...  The programme is financed by the Irish Government under the National Development Plan (NDP) 2007-2013 and is administered by the Environmental Protection Agency (EPA).  ... 
doi:10.3390/rs1030210 fatcat:hshzlrafz5emvc5ib25iwaf2nm

An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals

Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, Patrick J. Starks
2020 Journal of Hydrometeorology  
Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2) Walnut Gulch Watershed in Arizona; 3) Little Washita  ...  and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm.  ...  The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U. S. Government.  ... 
doi:10.1175/jhm-d-19-0034.1 fatcat:dasatlfgmjglvcf5w5xqkolxli

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Iliana E. Mladenova, John D. Bolten, Wade T. Crow, Nazmus Sazib, Michael H. Cosh, Compton J. Tucker, Curt Reynolds
2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA.  ...  The performance of this SMAP-based assimilation system is evaluated using two approaches.  ...  Fig. 2 . 2 Time series of standardized ranked NDVI and soil moisture observations over the Little Washita watershed area (Lat. 34.93°, Long. 98.17°).  ... 
doi:10.1109/jstars.2019.2923555 fatcat:haoovftfzngahj6kc7rv3ur7ny

Thermal hydraulic disaggregation of SMAP soil moisture over the continental United States

Pang-Wei Liu, Rajat Bindlish, Peggy E Oneill, Bin Fang, Venkataraman Lakshmi, Zhengwei Yang, Michael Cosh, Tara Bongiovanni, Chandra Holifield Collins, Patrick Starks, John Prueger, David D. Bosch (+2 others)
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental  ...  Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model.  ...  This research included a contribution from the Long-Term Agroecosystem Research (LTAR) network which is supported by USDA.  ... 
doi:10.1109/jstars.2022.3165644 fatcat:tf5wo2t72neypig2uzj6xbvvju

Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission

Dugwon Seo, Tarendra Lakhankar, Reza Khanbilvardi
2010 Remote Sensing  
The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99).  ...  Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data.  ...  Jones from Cooperative Institute for Research in the Atmosphere, Colorado State University for useful discussion and suggestions.  ... 
doi:10.3390/rs2051273 fatcat:fg3yiwm2lvhrxcvvtilt37bxly

Assessment of the Temperature Effects in SMAP Satellite Soil Moisture Products in Oklahoma

Kim Oanh Hoang, Minjiao Lu
2021 Remote Sensing  
Most of these satellite observations are based on the dielectric properties of wet soil, and most soil moisture retrieval algorithms are calibrated or evaluated using in situ soil moisture.  ...  To achieve the goals of this study, we analyzed the temperature effects on surface soil moisture data provided by a SMAP mission in Oklahoma, the United States.  ...  Data Availability Statement: The data presented in this study are openly available in website of ARS (, accessed on 12 October 2021) and AppEEARS (  ... 
doi:10.3390/rs13204104 fatcat:twbewhx4nffnnbrwyjoxjf3pm4

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Jana Kolassa, Rolf Reichle, Qing Liu, Michael Cosh, David Bosch, Todd Caldwell, Andreas Colliander, Chandra Holifield Collins, Thomas Jackson, Stan Livingston, Mahta Moghaddam, Patrick Starks
2017 Remote Sensing  
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations.  ...  Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017.  ...  Kolassa was supported by an appointment to the NASA Postdoctoral Program at the ���  ... 
doi:10.3390/rs9111179 pmid:32655902 pmcid:PMC7351107 fatcat:rgs2h2mabvei7nebzlgbfzuhta

Assessing Disaggregated SMAP Soil Moisture Products in the United States

Liu Pang-Wei, Rajat Bindlish, Bin Fang, Venkataraman Lakshmi, Peggy E Oneill, Zhengwei Yang, Michael Cosh, Tara Bongiovanni, David D. Bosch, Chandra Holifield Collins, Patrick Starks, John Prueger (+2 others)
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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 to 1 km over  ...  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 U.S.  ...  This work was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture.  ... 
doi:10.1109/jstars.2021.3056001 fatcat:e23fjulwjjbxhe3xafg6fycgku

Uncertainty of reference pixel soil moisture averages sampled at SMAP core validation sites

Fan Chen, Wade T. Crow, Michael H. Cosh, Andreas Colliander, Jun Asanuma, Aaron Berg, David D. Bosch, Todd G. Caldwell, Chandra Holifield Collins, Karsten Høgh Jensen, Jose Martínez-Fernández, Heather McNairn (+3 others)
2019 Journal of Hydrometeorology  
Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals.  ...  Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed.  ...  This study is supported by Dr. Wade Crow's membership on the NASA Soil Moisture Active Passive mission.  ... 
doi:10.1175/jhm-d-19-0049.1 fatcat:5shsof3jojddxofarkslhx6qie

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

Chenyang Cui, Jia Xu, Jiangyuan Zeng, Kun-Shan Chen, Xiaojing Bai, Hui Lu, Quan Chen, Tianjie Zhao
2017 Remote Sensing  
The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network  ...  This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS)  ...  Acknowledgments: The work was supported by the Open Research Fund of Key Laboratory of Digital Earth  ... 
doi:10.3390/rs10010033 fatcat:3yjatu233vh75ba7v4ogxjdlye

Validation of Soil Moisture Data Products from the NASA SMAP Mission

Andreas Colliander, Rolf Reichle, Wade Crow, Michael H. Cosh, Fan Chen, Steven K. Chan, Narendra Narayan Das, Rajat Bindlish, Mario J Chaubell, Seungbum Kim, Qing Liu, Peggy E Oneill (+36 others)
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31,  ...  Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products  ...  for the SMAP soil moisture products and their validation.  ... 
doi:10.1109/jstars.2021.3124743 fatcat:jlp43jv4fjfnrntmaru7bwsmji

Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Rolf H. Reichle, Gabrielle J. M. De Lannoy, Qing Liu, Joseph V. Ardizzone, Andreas Colliander, Austin Conaty, Wade Crow, Thomas J. Jackson, Lucas A. Jones, John S. Kimball, Randal D. Koster, Sarith P. Mahanama (+26 others)
2017 Journal of Hydrometeorology  
The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and  ...  The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA  ...  We are grateful for the datasets and data archiving centers that supported this work and appreciate those who make the generation, dissemination, and validation of the L4_SM product possible, including  ... 
doi:10.1175/jhm-d-17-0063.1 fatcat:sai466viszhjnfroiy5uildhnm

Estimating surface soil moisture from SMAP observations using a Neural Network technique

J. Kolassa, R.H. Reichle, Q. Liu, S.H. Alemohammad, P. Gentine, K. Aida, J. Asanuma, S. Bircher, T. Caldwell, A. Colliander, M. Cosh, C. Holifield Collins (+6 others)
2018 Remote Sensing of Environment  
A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture  ...  Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based  ...  Finally, we assess the global error distributions of the SMAP NN, GEOS-5 and SMAP L2P products using a triple collocation (TC) analysis in conjunction with soil moisture retrievals based on observations  ... 
doi:10.1016/j.rse.2017.10.045 pmid:29290638 pmcid:PMC5744888 fatcat:ielfrlyd4jgjlks6ie4poheuim

A Hybrid Artificial Neural Network to Estimate Soil Moisture Using SWAT+ and SMAP Data

Katherine H. Breen, Scott C. James, Joseph D. White, Peter M. Allen, Jeffery G. Arnold
2020 Machine Learning and Knowledge Extraction  
In this work, we developed a data-driven framework to predict near-surface (0–5 cm) soil moisture (SM) by mapping inputs from the Soil & Water Assessment Tool to SM time series from NASA's Soil Moisture  ...  We evaluated the generalizability of the hybrid ANN using training datasets comprising several watersheds with different environmental conditions, examined the effects of standard and physics-guided loss  ...  Acknowledgments: This research was supported by a Graduate Teaching Fellowship from Baylor University, a summer 2019 NASA internship at Goddard Space Flight Center, and two fellowships at the Institute  ... 
doi:10.3390/make2030016 fatcat:2zordodk3bhcjf2cv6npywpq6u

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)

Panpan Yao, Hui Lu, Jiancheng Shi, Tianjie Zhao, Kun Yang, Michael H. Cosh, Daniel J. Short Gianotti, Dara Entekhabi
2021 Scientific Data  
The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m3/m3.  ...  This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002–2019).  ...  We use in situ soil moisture observations from 14 representative validation sites, including (a) 7 United States Department of Agriculture (USDA) watershed sites (Walnut Gulch, Little Washita, Fort Cobb  ... 
doi:10.1038/s41597-021-00925-8 pmid:34045448 fatcat:svcfyk22lbgkzflxfswamklxx4
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