Reduction of the Vegetation and Soil Moisture Effects to Improve Topsoil Properties Retrieval Accuracy from Prisma Images

Temporal changes in soil moisture (SM) and green vegetation affecting the spectral reflectance can heavily reduce the accuracy of topsoil properties estimation from satellite imaging. To minimize these effects on the soil organic carbon (SOC), sand, silt and clay estimations, an external parameter orthogonalization (EPO) model developed using laboratory based measured spectra was tested on PRISMA hyperspectral satellite data. The estimation of soil properties was performed using different machine learning algorithms. The results show that as compared to the uncorrected spectra, removing the effects of both green vegetation and SM (EPO SM+GV ) from the reflectance spectra leads to 18%, 13%, 10%, and 24% improvement in the R 2 for clay, silt, sand and SOC retrieval, respectively. The Gaussian Process Regression (GPR) algorithm provides the best results for all of the soil properties with an RMSE of 9.5%, 14.2%, 6.9% and 0.68% for clay, silt, sand and SOC retrievals, respectively.