Bagheri, Reyhaneh, Tabib Mahmoudi, Fatemeh, Gholamian, AmirHossein. (1404). Diverse Climatic Soil Moisture Estimation using an Enhanced GRU Model with Multi-Source Data Augmentation. فناوری آموزش, 14(2), 425-434. doi: 10.22061/jecei.2026.12695.903
Reyhaneh Bagheri; Fatemeh Tabib Mahmoudi; AmirHossein Gholamian. "Diverse Climatic Soil Moisture Estimation using an Enhanced GRU Model with Multi-Source Data Augmentation". فناوری آموزش, 14, 2, 1404, 425-434. doi: 10.22061/jecei.2026.12695.903
Bagheri, Reyhaneh, Tabib Mahmoudi, Fatemeh, Gholamian, AmirHossein. (1404). 'Diverse Climatic Soil Moisture Estimation using an Enhanced GRU Model with Multi-Source Data Augmentation', فناوری آموزش, 14(2), pp. 425-434. doi: 10.22061/jecei.2026.12695.903
Bagheri, Reyhaneh, Tabib Mahmoudi, Fatemeh, Gholamian, AmirHossein. Diverse Climatic Soil Moisture Estimation using an Enhanced GRU Model with Multi-Source Data Augmentation. فناوری آموزش, 1404; 14(2): 425-434. doi: 10.22061/jecei.2026.12695.903
Department of Geomatics Engineering, Faculty of Civil, Shahid Rajaee Teacher Training University, Tehran, Iran.
تاریخ دریافت: 19 آبان 1404،
تاریخ بازنگری: 29 بهمن 1404،
تاریخ پذیرش: 04 اسفند 1404
چکیده
Background and Objectives: Accurate soil moisture estimation is essential for various hydrological processes such as irrigation planning, and environmental monitoring; however, prediction accuracy is often limited by sparse in-situ measurements and uncertainties in remote sensing products. This study aims to develop an enhanced soil moisture prediction framework by integrating a Gated Recurrent Unit (GRU) deep learning model with multi source data augmentation techniques in order to evaluate its performance across diverse climatic conditions. Methods: This study proposed an enhanced GRU deep learning framework supported by multi source data augmentation to predict soil moisture across ten U.S. Climate Reference Network (USCRN) stations representing diverse climatic and ecological conditions. The proposed model integrates Conv1D layers, bidirectional GRUs, multi-head attention, and dense layers to capture short and long-term temporal dependencies while fusing multi source inputs including ISMN in-situ measurements, SMAP products, and GLDAS. Data augmentation strategies composed of noise injection, temporal warping, scaling, and window slicing were applied to expand the training dataset and reduce overfitting. Model performance was compared against a standard GRU using some of the evaluation metrics. Results: Results demonstrate that the augmented GRU model consistently outperformed the standard GRU across all stations, with notable improvements in R² (up to 0.912), RMSE, and MAE. Performance gains were particularly evident in humid continental and Mediterranean climates, while regions with complex forested or semi-arid environments also benefited from data augmentation. These improvements confirm that data augmentation enhances the model’s generalization under climatic variability and mitigates limitations associated with SMAP resolution and GLDAS uncertainty. Conclusion: The integration of multi-source datasets with an augmented GRU architecture provides a reliable framework for soil moisture estimation across diverse environments. The proposed approach offers strong potential for applications in environmental monitoring, precision agriculture, and water resource management.
