Soil salinity mapping utilizing sentinel-2 and neural networks

Soil salinity is the most important soil property that affects the agriculture productivity. Periodical monitoring of its status is considered a crucial factor in the selection of appropriate agricultural practices to attain a sustainable production. The availability of remote sensing data processed by a some what novel method such as artificial neural networks (ANN) offer a potential solution that could easily and affordablyreplace the in-site monitoring methods. The aimof this work istouse high spectral resolution Sentinel-2 (S2) data for soil salinity prediction utilizing neural networks. The study evaluated three approaches in processing the S2 data for inclusion in the artificial neural network for soil salinity prediction. These approaches included S2 spectral reflectance data, spectral indices and principal component analysis (PCA) of the S2 data. The results revealed that acombination of these approaches including the reflectance data of band11(shortwaveinfrared band) of S2, the normalized differential vegetation index (NDVI) and the second PCA (dominated by the near infrared band) gave the best performance when used as input when designing the artificial neural networks to predict the soil salinity. The overall accuracy of this approach has a coefficient of determination (R²) of 0.94 between the actual and predicted soil salinity.