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SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network

Author

Listed:
  • Dileep Kumar Gupta

    (Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India)

  • Prashant K. Srivastava

    (Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
    DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India)

  • Ankita Singh

    (Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India)

  • George P. Petropoulos

    (Department of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea, 17671 Athens, Greece)

  • Nikolaos Stathopoulos

    (Institute for Space Applications and Remote Sensing, National Observatory of Athens, BEYOND Centre of EO Research & Satellite Remote Sensing, 15236 Athens, Greece)

  • Rajendra Prasad

    (Department of Physics, Indian Institute of Technology (BHU), Varanasi 221005, India)

Abstract

Soil moisture (SM) is the primary variable regulating the soil temperature (ST) differences between daytime and night-time, providing protection to crop rooting systems against sharp and sudden changes. It also has a number of practical applications in a range of disciplines. This study presents an approach to incorporating the effect of ST for the accurate estimation of SM using Earth Observation (EO) data from NASA’s SMAP sensor, one of the most sophisticated satellites currently in orbit. Linear regression analysis was carried out between the SMAP-retrieved SM and ground-measured SM. Subsequently, SMAP-derived ST was incorporated with SMAP-derived SM in multiple regression analysis to improve the SM retrieval accuracy. The ability of the proposed method to estimate SM under different seasonal conditions for the year 2016 was evaluated using ground observations from the Wales Soil Moisture Network (WSMN), located in Wales, United Kingdom, as a reference. Results showed reduced retrieval accuracy of SM between the SMAP and ground measurements. The R 2 between the SMAP SM and ground-observed data from WSMN was found to be 0.247, 0.183, and 0.490 for annual, growing and non-growing seasons, respectively. The values of RMSE between SMAP SM and WSMN observed SM are reported as 0.080 m 3 m −3 , 0.078 m 3 m −3 and 0.010 m 3 m −3 , with almost zero bias values for annual, growing and non-growing seasons, respectively. Implementation of the proposed scheme resulted in a noticeable improvement in SSM prediction in both R 2 (0.558, 0.440 and 0.613) and RMSE (0.045 m 3 m −3 , 0.041 m 3 m −3 and 0.007 m 3 m −3 ), with almost zero bias values for annual, growing and non-growing seasons, respectively. The proposed algorithm retrieval accuracy was closely matched with the SMAP target accuracy 0.04 m 3 m −3 . In overall, use of the new methodology was found to help reducing the SM difference between SMAP and ground-measured SM, using only satellite data. This can provide important assistance in improving cases where the SMAP product can be used in practical and research applications.

Suggested Citation

  • Dileep Kumar Gupta & Prashant K. Srivastava & Ankita Singh & George P. Petropoulos & Nikolaos Stathopoulos & Rajendra Prasad, 2021. "SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6019-:d:563069
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    References listed on IDEAS

    as
    1. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
    2. George P. Petropoulos & Prashant K. Srivastava & Maria Piles & Simon Pearson, 2018. "Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management," Sustainability, MDPI, vol. 10(1), pages 1-20, January.
    3. Olutobi Adeyemi & Ivan Grove & Sven Peets & Tomas Norton, 2017. "Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation," Sustainability, MDPI, vol. 9(3), pages 1-29, February.
    4. Knox, J.W. & Kay, M.G. & Weatherhead, E.K., 2012. "Water regulation, crop production, and agricultural water management—Understanding farmer perspectives on irrigation efficiency," Agricultural Water Management, Elsevier, vol. 108(C), pages 3-8.
    5. Prashant Srivastava & Dawei Han & Miguel Rico-Ramirez & Deleen Al-Shrafany & Tanvir Islam, 2013. "Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5069-5087, December.
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