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GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

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Listed:
  • Prashant K. Srivastava

    (Institute of Environment and Sustainable Development and DST-Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India)

  • Prem C. Pandey

    (Center for Environmental Sciences and Engineering, School of Natural Sciences, Shiv Nadar University, Greater Noida, Gautam Buddha Nagar, Uttar Pradesh 201314, India)

  • George P. Petropoulos

    (Department of Soil & Water Resources, Institute of Industrial & Forage Crops, Hellenic Agricultural Organization, H.A.O. “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1 Theofrastou St., 41335 Larisa, Greece
    School of Mineral & Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Chania, Crete, Greece)

  • Nektarios N. Kourgialas

    (NAGREF-Hellenic Agricultural Organization (H.A.O.-DEMETER), Institute for Olive Tree Subtropical Crops and Viticulture, Water Recourses-Irrigation & Env. Geoinformatics Lab., 73100 Chania, Greece)

  • Varsha Pandey

    (Institute of Environment and Sustainable Development and DST-Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India)

  • Ujjwal Singh

    (Institute of Environment and Sustainable Development and DST-Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India)

Abstract

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km 2 . The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

Suggested Citation

  • Prashant K. Srivastava & Prem C. Pandey & George P. Petropoulos & Nektarios N. Kourgialas & Varsha Pandey & Ujjwal Singh, 2019. "GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques," Resources, MDPI, vol. 8(2), pages 1-17, April.
    • Handle: RePEc:gam:jresou:v:8:y:2019:i:2:p:70-:d:223956
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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. Prashant Srivastava & Tanvir Islam & Manika Gupta & George Petropoulos & Qiang Dai, 2015. "WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2267-2284, May.
    3. 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.
    4. Xueling Yao & Bojie Fu & Yihe Lü & Feixiang Sun & Shuai Wang & Min Liu, 2013. "Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-13, January.
    5. Dhruvesh Patel & Prashant Srivastava, 2013. "Flood Hazards Mitigation Analysis Using Remote Sensing and GIS: Correspondence with Town Planning Scheme," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2353-2368, May.
    6. Kourgialas, Nektarios N. & Karatzas, George P., 2016. "A flood risk decision making approach for Mediterranean tree crops using GIS; climate change effects and flood-tolerant species," Environmental Science & Policy, Elsevier, vol. 63(C), pages 132-142.
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    1. Małgorzata Biniak-Pieróg & Mieczysław Chalfen & Andrzej Żyromski & Andrzej Doroszewski & Tomasz Jóźwicki, 2020. "The Soil Moisture during Dry Spells Model and Its Verification," Resources, MDPI, vol. 9(7), pages 1-27, July.

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