Hybrid CNN-LSTM-ECOC Model for Enhanced Soil Moisture Estimation

Abstract Soil moisture plays a critical role in agricultural productivity, water resource management, and environmental monitoring, making its accurate estimation essential for sustainable practices. Reliable soil moisture estimation is vital for optimizing irrigation, predicting droughts, and managing flood risks, especially in the face of global climate challenges. However, traditional methods are limited by sparse spatial coverage and the inability to effectively integrate diverse data sources, while standalone models struggle to capture both spatial and temporal dependencies. To address these challenges, this study proposes a novel hybrid CNN-LSTM-ECOC model that integrates remote sensing, in-situ, and meteorological data, leveraging advanced deep learning architectures to predict soil moisture with high precision. The model uniquely combines spatial feature extraction, temporal sequence modeling, and robust multi-class classification, offering unprecedented accuracy and robustness in soil moisture estimation. Experimental results demonstrate the proposed approach outperforms traditional models, achieving an MAE of 0.12, RMSE of 0.18, and an R² of 0.92, establishing its effectiveness for real-world applications in precision agriculture and environmental management.