Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations
Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2024.1513620/full |
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| Summary: | Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwave Synthetic Aperture Radar (SAR) backscatter observations over the wheat fields. From the wheat fields, samplings were performed to collect the in situ datasets on three different dates concurrent to the Sentinel-1 overpasses. The backscattering coefficients were taken as the input variables and SM as the output variable for the training and testing of different models. The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. Specifically, the RF model exhibited outstanding accuracy and robustness in both the training [RMSE (%): 3.44, R-squared: 0.88, correlation: 0.95] and validation phases [RMSE (%): 7.06, R-squared: 0.61, correlation: 0.8], marking it as the most effective model followed by the CNN model with [RMSE (%): 3.9, R-squared: 0.84, correlation: 0.92] during training and [RMSE (%): 8.44, R-squared: 0.43, correlation: 0.67] for validation, highlighting challenges in the model generalisation. |
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| ISSN: | 2673-6187 |