A simplified subsurface 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

Soil salinity has become a highly disastrous phenomenon responsible for crop failure worldwide, especially in countries with low farmer incomes and food insecurity. Soil salinity is often due to water accumulation in fields caused by improper flood irrigation whereby plants take up the water leaving salts behind. It is, however, the subsurface soil salinity that affects plant growth. This soil salinity prevents further water intake. There have been very few studies conducted for subsurface soil salinity estimation. Therefore our study aimed to estimate subsurface soil salinity (at 60 cm depth) for the 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 utilises SENTINEL-1 SAR (synthetic aperture RADAR) data for backscatter coefficient generation, SENTINEL-2A multispectral data for NDSI (normalised differential salinity index) generation and on-ground equipment for direct collection of soil electrical conductivity (EC). The data were collected for two dates in November and December 2019 and one date in 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 R-2-statistics of 0.99 and 0.958 in the training and testing phase and root mean square error (RMSE) of 1.92 and mean absolute error (MAE) of 0.78 in modelling for soil salinity estimation.

Automated summary

Soil salinity, particularly subsurface salinity, is a serious issue affecting plant growth and leading to crop failure globally. This study aimed to estimate subsurface soil salinity for early wheat crop growth using satellite data, achieving high accuracy through ordinary least squares regression modelling. The research fills a gap in understanding and monitoring subsurface soil salinity, providing valuable insights for agricultural practices.