Estimation of top soil properties by Sentinel-2 imaging

Abstract

This study evaluated the feasibility of using free multispectral remote sensing data from Sentinel-2A satellites to predict soil properties in Northern Karnataka, India. Sentinel-2A images were downloaded for selected sites, covering Vertisol, Ultisol, and Alfisol soils. Multiple linear regression (MLR) models incorporated four Sentinel-2 bands and six spectral indices (NDVI, GNDVI, SAVI, TVI, EVI, and BI) as independent variables, with soil properties as dependent variables. Surface samples (0–15 cm depth) were collected from March to May 2022. The analysis showed significant correlations between individual bands and soil properties, with variations in Organic Carbon (OC) compared to sand, silt, clay, and pH. Sand positively correlated with all spectral indices, while silt, clay, and pH were negatively correlated. The red and Near-Infrared (NIR) bands showed a non-significant relationship with OC. No significant correlation was found between EVI and the soil properties. Strong regression coefficients were observed between Sentinel-2 predictions and laboratory measurements: sand (r² = 0.63), silt (r² = 0.73), clay (r² = 0.59), and pH (r² = 0.59). These results demonstrate the potential of Sentinel-2 data for predicting soil properties, offering a valuable tool for managing unsampled agricultural fields.

Keywords

• Sentinel-2
• spectral indices
• soil properties
• correlation