A new approach to soil moisture estimation based on correlation between SAR and multispectral indices and seasonal pattern of soil moisture dynamics: a case study of Lake Urmia Basin

Year 2026, Volume: 11 Issue: 2, 392 - 407

Maryam Sadeghi , Khalil Valizadeh Kamran , Sadra Karimzadeh , Abolfazl Ghanbari , Saeed Samadianfard

Abstract

Radar satellite imagery has been widely used to obtain soil moisture (SM) estimates of high accuracy. Accurate information on surface soil moisture content under scalable conditions is important for hydrological and climatological applications. the aim of this study, the integration of multi-sensor satellite data to investigate the importance of features in various products and estimate soil moisture, is conducted using two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), in the Lake Urmia basin. we used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data, Sentinel-2 and Landsat-8 optical-thermal imagery, soil property maps (SoilGrids), and climate variables (FLDAS). at first analysis of correlation and regression was done seasonally for the four-year period to examine the importance and effectiveness of their use in estimating soil moisture. Then, the implementation of the model was done in two stages, using all the features (22) and 10 that were determined based on the performance of the model. The results show that soil organic carbon (SOC250) and radar indices governed winter and spring moisture dynamics, whereas vegetation indices dominated summer and autumn predictions, reflecting vegetation-climate-soil interactions. The Rand Forest model using all features had the highest accuracy of 0.88 in spring and the lowest in summer, with an accuracy of 0.80, while the SVR model had the lowest accuracy in summer (50.539) and the highest accuracy (0.628) in autumn, with the SVR results using the 10 most important features increasing by 0.1 R2 across all the variables. This increase in accuracy was observed in the RF model from 0.1 to 0.3, with the highest increase in accuracy in the summer. RF outperforms the SVR model in all evaluation metrics, including Mean Squared Error (MSE), R², and Mean Absolute Error ( MAE), for both feature sets and in all seasons. The Normalized Vegetation Structural Difference Index (NVSDI) and Normalized Radar Vegetation Difference Index (NRVDI) indices have helped improve model accuracy by providing more combined and detailed information about specific soil and vegetation characteristics. normalized difference vegetation index (NDVI) has a specific focus on vegetation, while NVSDI and NRVDI provide more comprehensive and detailed environmental information. These findings demonstrate the potential of multi-sensor data integration seasonally, for soil moisture estimation, providing and critical insight for hydrological modeling, monitoring environmental and agricultural.

Keywords

Soil Moisture , SAR Indices , Machine Learning , lake Urmia basin

Ethical Statement

June 30, 2025 Editor-in-Chief Prof. Dr. Murat Yakar International Journal of Engineering and Geosciences (IJEG) Dear Professor Yakar, I am pleased to submit our original research manuscript entitled: "A new approach to soil moisture estimation based on correlation between SAR and multispectral indices and seasonal pattern of soil moisture dynamics: A case study of Lake Urmia basin" for your consideration for publication in the International Journal of Engineering and Geosciences (IJEG). This work has not been published previously, is not under consideration for publication elsewhere, and its publication has been approved by all authors as well as by the responsible authorities where the work was conducted. We confirm that, if accepted, the manuscript will not be published elsewhere in the same form in English or any other language, electronically or in print, without the prior written consent of the copyright holder. We believe that the findings of this study contribute significantly to the field of remote sensing and geosciences, particularly in the context of soil moisture monitoring using SAR and multispectral data. Thank you for your time and consideration. Sincerely, Dr. Maram Sadeghi Department of Remote Sensing and Geographic Information Systems Faculty of Planning and Environmental Science University of Tabriz, Tabriz, Iran Email: [e-mail]

Supporting Institution

The authors declare no conflicts of interest.

Thanks

The authors express their gratitude to the European Space Agency (ESA) for the freely available Sentinel-1 and multispectral data, which were crucial for this study.

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