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Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Author

Listed:
  • Mohammed Dabboor

    (Science and Technology Branch, Environment and Climate Change Canada, Dorval, QC H9P 1J3, Canada)

  • Ghada Atteia

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Rana Alnashwan

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R 2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R 2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

Suggested Citation

  • Mohammed Dabboor & Ghada Atteia & Rana Alnashwan, 2023. "Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques," Land, MDPI, vol. 12(10), pages 1-13, September.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1861-:d:1251068
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    References listed on IDEAS

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    1. Paul Desbordes & Su Ruan & Romain Modzelewski & Pascal Pineau & Sébastien Vauclin & Pierrick Gouel & Pierre Michel & Frédéric Di Fiore & Pierre Vera & Isabelle Gardin, 2017. "Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
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