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

1
Institute of Environment and Sustainable Development and DST-Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
2
Center for Environmental Sciences and Engineering, School of Natural Sciences, Shiv Nadar University, Greater Noida, Gautam Buddha Nagar, Uttar Pradesh 201314, India
3
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
4
School of Mineral & Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Chania, Crete, Greece
5
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
*
Authors to whom correspondence should be addressed.
Resources 2019, 8(2), 70; https://doi.org/10.3390/resources8020070
Received: 21 February 2019 / Revised: 1 April 2019 / Accepted: 15 April 2019 / Published: 18 April 2019
(This article belongs to the Special Issue Multiple-effect Water Resources Management)
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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 km2. 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. View Full-Text
Keywords: spatial interpolation; geoinformation; mapping; monitoring soil moisture; soil water management; geographical information systems spatial interpolation; geoinformation; mapping; monitoring soil moisture; soil water management; geographical information systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Srivastava, P.K.; Pandey, P.C.; Petropoulos, G.P.; Kourgialas, N.N.; Pandey, V.; Singh, U. GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques. Resources 2019, 8, 70.

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