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GIS and remote sensing aided information for soil moisture estimation: a comparative study of interpolation techniques

Srivastava Prashant K., Pandey Prem Chandra, Petropoulos Georgios, Kourgialas Nektarios N., Pandey Varsha H.R., Singh Ujjwal

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URIhttp://purl.tuc.gr/dl/dias/4AFECADE-8504-4392-9575-5F0EB8F73A9D-
Identifierhttps://doi.org/10.3390/resources8020070-
Identifierhttps://www.mdpi.com/2079-9276/8/2/70-
Languageen-
Extent17 pagesen
TitleGIS and remote sensing aided information for soil moisture estimation: a comparative study of interpolation techniquesen
CreatorSrivastava Prashant K.en
CreatorPandey Prem Chandraen
CreatorPetropoulos Georgiosen
CreatorΠετροπουλος Γεωργιοςel
CreatorKourgialas Nektarios N.en
CreatorPandey Varsha H.R.en
CreatorSingh Ujjwalen
PublisherMDPIen
Content SummarySoil 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 DistanceWeighting (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.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-11-03-
Date of Publication2019-
SubjectGeographical information systemsen
SubjectGeoinformationen
SubjectMappingen
SubjectMonitoring soil moistureen
SubjectSoil water managementen
SubjectSpatial interpolationen
Bibliographic CitationP.K. Srivastava, P.C. Pandey, G.P. Petropoulos, N.N. Kourgialas, V. Pandey and U. Singh, "GIS and remote sensing aided information for soil moisture estimation: a comparative study of interpolation techniques," Resources, vol. 8, no. 2, Apr. 2019. doi: 10.3390/resources8020070en

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