- 1University of Trás-os-Montes e Alto Douro , School of Science and Technology, Engineering Department, Portugal (ana.c.teixeira@inesctec.pt)
- 2Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- 3Agronomy Department, School of Agrarian and Veterinary Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
- 4Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Vila Real, Portugal
- 5Department of Environmental Economy and Management, Faculty of Management and Business, University of Presov (UNIPO), Presov, Slovakia
- 6insar.sk Ltd, Presov, Slovakia
- 7Department of Forestry Sciences and Landscape Architecture, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
- 8Fundacao Coa Parque, Rua Museu, Vila Nova De Foz Coa, Portugal
Accurate estimation of soil moisture is vital for sustainable water management in agriculture, particularly in olive orchards where precise irrigation strategies are crucial for maintaining productivity and crop quality. Climate change intensifies water scarcity, intensifying the need for advanced methodologies to optimize agricultural water use. Remote sensing technologies, such as Synthetic Aperture Radar (SAR), have emerged as promising tools for monitoring soil moisture over large areas. When combined with in situ measurements and data-driven models like Artificial Neural Networks (ANNs), these technologies offer scalable solutions for addressing the challenges of soil moisture estimation in heterogeneous agricultural landscapes.
This study integrates Sentinel-1 SAR data with ANN models to estimate soil moisture in olive orchards located in the Vilariça Valley, northeastern Portugal. Soil moisture measurements were recorded at a depth of 10 cm every 30 minutes from July 2020 to December 2021. Sentinel-1 SAR images were acquired in dual polarizations (VV and VH), and synthetic bands were generated through arithmetic operations combining polarization and calibration metrics (Beta, Sigma, Gamma, Gamma TF), yielding 24 features per image. Two datasets were constructed to evaluate the impact of orbit geometry: (1) D1, containing 161 images from ascending orbits, and (2) D2, comprising 246 images from ascending and descending orbits.
The ANN regression model, comprising six hidden layers and K-fold cross-validation (20 splits), demonstrated greater performance with the D1 dataset, achieving a Root Mean Square Error (RMSE) of 2.78, a coefficient of determination (R²) of 0.69, and a Mean Absolute Percentage Error (MAPE) of 8.26%. In contrast, the D2 dataset showed reduced accuracy (RMSE: 3.96, R²: 0.59, MAPE: 12.41%), likely due to variability introduced by combining ascending and descending orbits. These findings underscore the importance of dataset homogeneity in SAR-based soil moisture modeling.
This study highlights the potential of integrating Sentinel-1 SAR data with ANN models for soil moisture estimation in olive orchards, contributing to the development of sustainable agricultural practices. Future work should focus on addressing dataset imbalances by expanding the range of observed conditions, incorporating topographic features, and exploring advanced data augmentation techniques to enhance model robustness and scalability.
Acknowledgments
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. DOI 10.54499/LA/P/0063/2020 https://doi.org/10.54499/LA/P/0063/2020 and a doctoral scholarship in a non-academic environment at Fundação Côa Parque (PRT/BD/154871/2023).
How to cite: Carvalhais Teixeira, A. C., Marques, P., Bakon, M., Fernandes-Silva, A., Lopes, D., and Sousa, J.: Sentinel-1 SAR Data and Artificial Neural Networks for Soil Moisture Estimation in Olive Orchards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19782, https://doi.org/10.5194/egusphere-egu25-19782, 2025.
