Intelligent Methods For Soil Moisture Retrieval from Sentinel -1 SAR Data Based On a Designed Ground Sensor

Document Type : Original Article

Authors

1 Department of Civil Engineering, Razi University, Kermanshah, Iran

2 School of Surveying and Geospatial Engineering, Faculty of Engineering, University of Tehran, Iran

10.22059/eoge.2026.407687.1193

Abstract

Soil moisture is an important variable for many studies, such as crop yield estimation, drought monitoring, evapotranspiration, agricultural and water resources management. Today, remote sensing data are widely used to estimate soil moisture. The main purpose of this study is to estimate soil moisture using Sentinel 1 radar data. In the first step, 8 features were extracted from Sentinel 1 data (dual polarized radar backscatter). The ground probe was used for field measurement of soil moisture. In the next step, soil moisture retrieval was done using machine learning methods. These data were used to train and validate machine learning methods. In this research, support vector regression (SVR), decision tree, random forest (RF) and neural network (ANN) methods were performed to estimate soil moisture. Evaluation of the accuracy of different methods was done using field measurement and comparing it with the estimated moisture. The random forest method has the highest accuracy with, Root Mean Square (RMSE=0.03kg/kg) and correlation coefficient (R2=98.6%). The results of this research showed that the use of different features extracted from Sentinel 1 data along with a suitable machine learning method can significantly increase the accuracy of soil moisture retrieval.

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Main Subjects

Earth Observation and Geomatics Engineering
Volume 9, Issue 2
December 2025

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