Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Authors : Sachin SUTARİYA, Ankur HİRAPARA, Momin MEHERBANALİ, M.k. TİWARİ, Vijay SINGH, Manik KALUBARME

Pages : 65-77

Doi:10.30897/ijegeo.777434

View : 11 | Download : 6

Publication Date : 2021-03-07

Article Type : Research Paper

Abstract :This paper presents the potential for soil moisture insert ignore into journalissuearticles values(SM); retrieval using Sentinel-1 C-band Synthetic Aperture Radar insert ignore into journalissuearticles values(SAR); data acquired in Interferometric Wide Swath insert ignore into journalissuearticles values(IW); mode along with Land Surface Temperature insert ignore into journalissuearticles values(LST); estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform insert ignore into journalissuearticles values(SNAP); software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index insert ignore into journalissuearticles values(SMI); for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture insert ignore into journalissuearticles values(MS %); as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination insert ignore into journalissuearticles values(R2); of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas. The maps of spatial distribution of soil moisture in three talukas were generated using the respective regression equations of three talukas.
Keywords : Soil Moisture, Sentinel 1 SAR data, Land Surface Temperature, Soil Moisture Index SMI, , Backscattering coefficient, Landsat 8 OLI and TIRS data