Sudhakara, B.Bhattacharjee, S.2026-02-0620242024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024, 2024, Vol., , p. -https://doi.org/10.1109/InGARSS61818.2024.10984286https://idr.nitk.ac.in/handle/123456789/29157In this study, we introduce a novel method for predicting soil moisture (SM) in the Bidar district of Karnataka by leveraging a combination of advanced Machine Learning (ML) techniques and remote sensing data. The study utilizes multi-source inputs, such as MODIS satellite data, soil properties, elevation, and precipitation, to predict the SM. Our methodology implements state-of-the-art graph neural networks (GNNs), particularly Graph Convolutional Networks (GCNs), in combination with Long Short-Term Memory (LSTM) networks to capture both spatio-temporal dependencies in the data. The proposed model is compared with LSTM and CNN (Convolutional Neural Network)LSTM models and experimental results demonstrate that the GCN-LSTM model outperforms other approaches, achieving an R2 value of 0.9152, a mean absolute error (MAE) of 0.0240 m3/m3, and a root mean square error (RMSE) of 0.0322 m3/m3. These findings highlight the potential of graph neural networks in enhancing the accuracy of SM predictions, providing valuable insights for agricultural and water resource management in the region. © 2024 IEEE.Graph Neural Networks (GNNs)satellite imagerySoil moisture (SM)temporal data