NDMI-Derived Field-Scale Soil Moisture Prediction Using ERA5 and LSTM for Precision Agriculture

Accurate soil moisture prediction is fundamental to precision agriculture, facilitating optimal irrigation scheduling, efficient water resource allocation, and enhanced crop productivity. This study employs a Long Short-Term Memory (LSTM) deep learning model, integrated with high-resolution ERA5 remote sensing data, to improve soil moisture estimation at the field scale. Soil moisture dynamics were analyzed across six commercial potato production sites in Quebec—Goulet, DBolduc, PBolduc, BNiquet, Lalancette, and Gou-new—over a five-year period. The model exhibited high predictive accuracy, with correlation coefficients (R) ranging from 0.991 to 0.998 and Nash–Sutcliffe efficiency (NSE) values reaching 0.996, indicating strong agreement between observed and predicted soil moisture variability. The Willmott index (WI) exceeded 0.995, reinforcing the model’s reliability. The integration of NDMI assessments further validated the predictions, demonstrating a strong correlation between NDMI values and LSTM-based soil moisture estimates. These findings confirm the effectiveness of deep learning in capturing spatiotemporal variations in soil moisture, underscoring the potential of AI-driven models for real-time soil moisture monitoring and irrigation optimization. This research study provides a scientifically robust framework for enhancing data-driven agricultural water management, promoting sustainable irrigation practices, and improving resilience to soil moisture variability in agricultural systems.