Remote Sensing Based Soil Moisture Estimation Using In-situ Probes in Varanasi District, India

Abstract

Soil moisture helps to determine the crop growth, disaster management, climatology and ecology. Soil moisture is highly influenced by vegetation cover. The current study was performed in Varanasi district of Uttar Pradesh on March 29, 2023, to estimate soil moisture using Landsat 8 data and biophysical parameters. This study also assessed the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) and their response to soil moisture. Additionally, it coupled NDVI-LST feature space-derived dry and wet edge best fit coefficient parameters with the Temperature Vegetation Dryness Index (TVDI) and Soil Moisture Index (SMI) equations to produce a spatial distribution of soil moisture availability. The LST and NDVI results help to extract Soil Surface Moisture (SSM) more effectively using an optical remote sensing approach. The soil moisture inversion model (TVDI) and SMI algorithm provide a better representation of the spatial distribution of soil moisture in Varanasi district. A linear and strong correlation exists between in-situ soil moisture data and TVDI (R2 = 0.6812), as well as a positive relationship between in-situ soil moisture data and SMI (R2 = 0.6848). It was found that LST and NDVI helped to generate the TVDI and SMI equations using the triangle model, and both equations demonstrated a strong correlation with in-situ data (R2 of TVDI = 0.6812, R2 of SMI = 0.6848).