Hyperspectral Remote Sensing and Artificial Intelligence for High-Resolution Soil Moisture Prediction

Reliable field estimation of soil moisture supports hydrology and water resources management. This study develops a drone-based hyperspectral approach in which visible and near-infrared reflectance is paired one-to-one with gravimetric water content measured by oven drying, yielding 1000 matched samples. After standardization, outlier control, ranked wavelength selection, and light feature engineering, several predictors were evaluated. Conventional machine learning methods, including simple and multiple regression and tree-based ensembles, were limited by band collinearity and piecewise approximations and therefore failed to meet the accuracy target. Gradient boosting reached the target but used different trade-offs in variable sensitivity. An artificial neural network with three hidden layers, rectified linear unit activations, and dropout was trained using a feature count sweep and early stopping. With ten predictors, the model achieved a coefficient of determination of 0.9557, demonstrating accurate mapping from hyperspectral reflectance to gravimetric water content and providing a reproducible framework suitable for larger, multi date acquisitions and operational decision support.