Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks
Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capab...
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| Vydané v: | Procedia computer science Ročník 270; s. 1372 - 1380 |
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Elsevier B.V
2025
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| Abstract | Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capable of reconciling general modeling principles with the intricate variability of climatic factors to ensure reliable predictions. This study explores the application of Spiking Neural Networks (SNNs) as an advanced approach beyond conventional methodologies, utilizing array-sensed data from the ERA5 dataset. SNNs are distinguished by their ability to merge computational efficiency with biologically inspired dynamics, employing Leaky Integrate-and-Fire neurons to process spatial and temporal information effectively. The model’s adaptability and precision in handling large-scale climatic datasets were evaluated using an 80-20 data split, achieving a Mean Squared Error (MSE) of 0.0003, an R2 value of 0.8919, and a Pearson correlation coefficient of 0.9449, reinforcing its predictive capability and ability to capture intrinsic dependencies within soil moisture dynamics. This novel implementation of SNNs enhances prediction accuracy while offering a computationally efficient solution for soil moisture forecasting, addressing key challenges in environmental and agricultural applications. The findings provide a foundation for future research aimed at optimizing hydrological models through biologically inspired neural architectures. |
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| AbstractList | Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capable of reconciling general modeling principles with the intricate variability of climatic factors to ensure reliable predictions. This study explores the application of Spiking Neural Networks (SNNs) as an advanced approach beyond conventional methodologies, utilizing array-sensed data from the ERA5 dataset. SNNs are distinguished by their ability to merge computational efficiency with biologically inspired dynamics, employing Leaky Integrate-and-Fire neurons to process spatial and temporal information effectively. The model’s adaptability and precision in handling large-scale climatic datasets were evaluated using an 80-20 data split, achieving a Mean Squared Error (MSE) of 0.0003, an R2 value of 0.8919, and a Pearson correlation coefficient of 0.9449, reinforcing its predictive capability and ability to capture intrinsic dependencies within soil moisture dynamics. This novel implementation of SNNs enhances prediction accuracy while offering a computationally efficient solution for soil moisture forecasting, addressing key challenges in environmental and agricultural applications. The findings provide a foundation for future research aimed at optimizing hydrological models through biologically inspired neural architectures. |
| Author | Saadane, Rachid Chehri, Abdellah El Maachi, Soukaina |
| Author_xml | – sequence: 1 givenname: Soukaina surname: El Maachi fullname: El Maachi, Soukaina email: soukaina.elmaachi.cedoc@ehtp.ac.ma organization: Hassania School of Public Works, Casablanca, Morocco – sequence: 2 givenname: Rachid surname: Saadane fullname: Saadane, Rachid email: saadane@ehtp.ac.ma organization: Hassania School of Public Works, Casablanca, Morocco – sequence: 3 givenname: Abdellah surname: Chehri fullname: Chehri, Abdellah email: chehri@rmc.ca organization: Royal Military College of Canada, Kingston, Ontario, K7K 7B4, Canada |
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| Cites_doi | 10.3390/ijerph20021374 10.1109/VTC2024-Spring62846.2024.10683049 10.1175/JHM-D-21-0206.1 10.3390/rs14215584 10.3390/jrfm18030114 10.1371/journal.pone.0214508 10.5194/hess-28-917-2024 10.1029/2018RG000618 10.1016/j.suscom.2018.09.002 10.1145/3440840.3440854 10.1016/j.neunet.2018.12.002 |
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| Keywords | Climate change climate data Artificial Intelligence Spiking neural networks |
| Language | English |
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| References | Accessed on 01-01-2025 Celik, M., Isik, M., Yuzugullu, O., Fajraoui, N., & Erten, E., 2022. Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote. Sens., 14, pp. 5584. . El Maachi, S.; Saadane, R.; Chehri, A. Institutions as a Fundamental Cause for Long-Run Sustainability. J. Risk Financial Manag. 2025, 18, 114. Babaeian, E., Sadeghi, M., Jones, S., Montzka, C., Vereecken, H., & Tuller, M., 2019. Ground, Proximal, and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics, 57, pp. 530 - 616. Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L., & Xue, X., 2019. Research on soil moisture prediction model based on deep learning. PLoS ONE, 14. Paul, S., & Singh, S., 2020. Soil Moisture Prediction Using Machine Learning Techniques. Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems. S. E. Maachi, A. Chehri and R. Saadane, ”Efficient Hardware Acceleration of Spiking Neural Networks Using FPGA: Towards Real-Time Edge Neuromorphic Computing,” 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, 2024, pp. 1-5, doi Chatterjee, S., Dey, N., & Sen, S., 2020. Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustain. Comput. Informatics Syst., 28, pp. 100279. Wang, Y., Shi, L., Hu, Y., Hu, X., Song, W., & Wang, L., 2024. A comprehensive study of deep learning for soil moisture prediction. Hydrology and Earth System Sciences. Li, L., Dai, Y., Wei, S., Wei, Z., Wei, N., & Li, Q., 2022. Causality-Structured Deep Learning for Soil Moisture Predictions. Journal of Hydrometeorology. Copernicus Climate Change Service (C3S) (2022): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi Tavanaei, A., Ghodrati, M., Kheradpisheh, S., Masquelier, T., & Maida, A., 2018. Deep Learning in Spiking Neural Networks. Neural networks: the official journal of the International Neural Network Society, 111, pp. 47-63. Fu, R., Xie, L., Liu, T., Zheng, B., Zhang, Y., & Hu, S., 2023. A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland. International Journal of Environmental Research and Public Health, 20. 10.1016/j.procs.2025.09.258_bib6 10.1016/j.procs.2025.09.258_bib7 10.1016/j.procs.2025.09.258_bib4 10.1016/j.procs.2025.09.258_bib10 10.1016/j.procs.2025.09.258_bib5 10.1016/j.procs.2025.09.258_bib2 10.1016/j.procs.2025.09.258_bib3 10.1016/j.procs.2025.09.258_bib1 10.1016/j.procs.2025.09.258_bib12 10.1016/j.procs.2025.09.258_bib11 10.1016/j.procs.2025.09.258_bib8 10.1016/j.procs.2025.09.258_bib9 |
| References_xml | – reference: Babaeian, E., Sadeghi, M., Jones, S., Montzka, C., Vereecken, H., & Tuller, M., 2019. Ground, Proximal, and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics, 57, pp. 530 - 616. – reference: S. E. Maachi, A. Chehri and R. Saadane, ”Efficient Hardware Acceleration of Spiking Neural Networks Using FPGA: Towards Real-Time Edge Neuromorphic Computing,” 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, 2024, pp. 1-5, doi: – reference: Copernicus Climate Change Service (C3S) (2022): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi: – reference: Li, L., Dai, Y., Wei, S., Wei, Z., Wei, N., & Li, Q., 2022. Causality-Structured Deep Learning for Soil Moisture Predictions. Journal of Hydrometeorology. – reference: Paul, S., & Singh, S., 2020. Soil Moisture Prediction Using Machine Learning Techniques. Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems. – reference: Tavanaei, A., Ghodrati, M., Kheradpisheh, S., Masquelier, T., & Maida, A., 2018. Deep Learning in Spiking Neural Networks. Neural networks: the official journal of the International Neural Network Society, 111, pp. 47-63. – reference: Fu, R., Xie, L., Liu, T., Zheng, B., Zhang, Y., & Hu, S., 2023. A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland. International Journal of Environmental Research and Public Health, 20. – reference: . – reference: Wang, Y., Shi, L., Hu, Y., Hu, X., Song, W., & Wang, L., 2024. A comprehensive study of deep learning for soil moisture prediction. Hydrology and Earth System Sciences. – reference: (Accessed on 01-01-2025) – reference: Chatterjee, S., Dey, N., & Sen, S., 2020. Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustain. Comput. Informatics Syst., 28, pp. 100279. – reference: Celik, M., Isik, M., Yuzugullu, O., Fajraoui, N., & Erten, E., 2022. Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote. Sens., 14, pp. 5584. – reference: El Maachi, S.; Saadane, R.; Chehri, A. Institutions as a Fundamental Cause for Long-Run Sustainability. J. Risk Financial Manag. 2025, 18, 114. – reference: Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L., & Xue, X., 2019. Research on soil moisture prediction model based on deep learning. PLoS ONE, 14. – ident: 10.1016/j.procs.2025.09.258_bib5 doi: 10.3390/ijerph20021374 – ident: 10.1016/j.procs.2025.09.258_bib9 doi: 10.1109/VTC2024-Spring62846.2024.10683049 – ident: 10.1016/j.procs.2025.09.258_bib3 doi: 10.1175/JHM-D-21-0206.1 – ident: 10.1016/j.procs.2025.09.258_bib7 doi: 10.3390/rs14215584 – ident: 10.1016/j.procs.2025.09.258_bib11 doi: 10.3390/jrfm18030114 – ident: 10.1016/j.procs.2025.09.258_bib12 – ident: 10.1016/j.procs.2025.09.258_bib4 doi: 10.1371/journal.pone.0214508 – ident: 10.1016/j.procs.2025.09.258_bib1 doi: 10.5194/hess-28-917-2024 – ident: 10.1016/j.procs.2025.09.258_bib2 doi: 10.1029/2018RG000618 – ident: 10.1016/j.procs.2025.09.258_bib6 doi: 10.1016/j.suscom.2018.09.002 – ident: 10.1016/j.procs.2025.09.258_bib8 doi: 10.1145/3440840.3440854 – ident: 10.1016/j.procs.2025.09.258_bib10 doi: 10.1016/j.neunet.2018.12.002 |
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| Title | Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks |
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