Convolutional neural networks estimate root-zone soil moisture from hyperspectral images

Maintaining proper water supply is crucial for agriculture and efficient plant cultivation. In-situ soil moisture measurements lack scalability, necessitating non-invasive systems to estimate soil moisture from remotely-sensed hyperspectral images (HSIs). We propose a root-zone soil moisture estimation system using deep architectures on HSIs acquired via unmanned aerial vehicles. Experiments conducted on a meticulously prepared dataset of in-situ measurements and HSIs, collected across multiple agronomic seasons with diverse plant varieties, soil profiles, and watering scenarios, revealed that our approach achieved a mean absolute error, mean squared error, and R² of 0.60, 0.64, and 0.80, outperforming sensor-based methods.