میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,119
تعداد مقالات76,512
تعداد مشاهده مقاله152,912,614
تعداد دریافت فایل اصل مقاله115,034,396
maleki, mohammad, Amini, Jalal, Younesi, Ali. (1405). Intelligent Methods For Soil Moisture Retrieval from Sentinel -1 SAR Data Based On a Designed Ground Sensor. , 9(2), -. doi: 10.22059/eoge.2026.407687.1193
mohammad maleki; Jalal Amini; Ali Younesi. "Intelligent Methods For Soil Moisture Retrieval from Sentinel -1 SAR Data Based On a Designed Ground Sensor". , 9, 2, 1405, -. doi: 10.22059/eoge.2026.407687.1193
maleki, mohammad, Amini, Jalal, Younesi, Ali. (1405). 'Intelligent Methods For Soil Moisture Retrieval from Sentinel -1 SAR Data Based On a Designed Ground Sensor', , 9(2), pp. -. doi: 10.22059/eoge.2026.407687.1193
maleki, mohammad, Amini, Jalal, Younesi, Ali. Intelligent Methods For Soil Moisture Retrieval from Sentinel -1 SAR Data Based On a Designed Ground Sensor. , 1405; 9(2): -. doi: 10.22059/eoge.2026.407687.1193

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

Earth Observation and Geomatics Engineering
دوره 9، شماره 2، اسفند 2025
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/eoge.2026.407687.1193
نویسندگان
mohammad maleki* 1؛ Jalal Amini2؛ Ali Younesi2
1Department of Civil Engineering, Razi University, Kermanshah, Iran
2School of Surveying and Geospatial Engineering, Faculty of Engineering, University of Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Soil moisture؛ Deep learning؛ Machine Learning؛ Sentinel-1؛ SAR Data
آمار
تعداد مشاهده مقاله: 24





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب