A simplified sub-surface soil salinity estimation using synergy of sentinel-1 SAR and sentinel-2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India

Abstract

Soil salinity has today become a highly disastrous phenomenon that is responsible for crop failure worldwide and specially in countries with low farmer incomes and food insecurity. Soil salinity is often caused due to water accumulation in fields due to unscientific flood irrigation wherein plants intake the water leaving salts behind. It is, however, the sub-surface soil salinity that affects the plant growth. These salts in sub-surface soil get trapped in root nodules of plants and prevent further water intake. There have been very few studies conducted for sub-surface soil salinity estimation. Hence this study aims to estimate sub-surface soil salinity (at 60 cm depth) for early stage of wheat crop growth in a simplified manner using freely available satellite data, which is a novel feature and prime objective in this study. The study utilizes Sentinel-1 SAR (Synthetic Aperture RADAR) data for backscatter coefficient generation, Sentinel-2 multispectral data for NDSI (Normalised Differential Salinity Index) generation and on ground equipment for direct collection of soil electrical conductivity. The data were collected for two dates in November and December 2019 and one date for January 2020 during the early stage of wheat crop growth. The dates were selected keeping in mind the satellite pass over the study area of Rupnagar on the same day. Ordinary Least Squares regression was used for modelling which gave R2-statistics of 0.99 and 0.958 in training and testing phase and root mean square error of 1.92 in modelling for soil salinity estimation.