نشریه سنجش از دور و GIS  ایران

نشریه سنجش از دور و GIS ایران

Soil Erosion Susceptibility Mapping Using Sentinel-1 Interferometric SAR Coherence and Machine Learning (Study Area: Khuzestan Province)

نوع مقاله : مقاله پژوهشی

نویسندگان
Dep of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
چکیده
Soil erosion mapping is important to identify and investigate the spatial patterns of soil erosion to adopt sustainable development goals-based management strategies and soil conservation measures. The use of modern remote sensing approaches can be effective in improving soil erosion mapping. Changes in the coherence of Synthetic Aperture Radar Interferometry (InSAR) are related to surface characteristics due to soil erosion events. Accordingly, in this study, using a time series of Sentinel-1 imagery over 5 years (2015 to 2020) and the InSAR technique, multi-temporal coherence maps were generated for an area in Khuzestan Province. Using the Support Vector Machine (SVM) method, a machine learning-based classification scheme was developed. The ground truth soil erosion map of Khuzestan Province was used to train and validate the SVM. Subsequently, using features from Sentinel-1 coherence, Landsat 8-derived normalized difference vegetation index (NDVI), soil texture characteristics, and topography, soil erosion susceptibility maps were produced using different SVM kernels. The overall accuracy of the produced maps for the implemented kernels (linear, polynomial, radial basis function (RBF), and sigmoid) was 72.44%, 80%, 80.44%, and 52%, respectively. Consequently, the RBF kernel showed the highest performance in predicting different soil erosion susceptibility classes. Our results indicate that multi-temporal InSAR coherence can be used as a valuable data source for soil erosion mapping.
کلیدواژه‌ها

عنوان مقاله English

Soil Erosion Susceptibility Mapping Using Sentinel-1 Interferometric SARCoherence and Machine Learning (Study Area: Khuzestan Province)

نویسندگان English

Masoud Soleimani
Najmeh Samani Neysani
Sara Attarchi
Ali Darvishi Boloorani
Dep of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
چکیده English

Soil erosion mapping is important to identify and investigate the spatial patterns of soil erosion to adopt sustainable development goals-based management strategies and soil conservation measures. The use of modern remote sensing approaches can be effective in improving soil erosion mapping. Changes in the coherence of Synthetic Aperture Radar Interferometry (InSAR) are related to surface characteristics due to soil erosion events. Accordingly, in this study, using a time series of Sentinel-1 imagery over 5 years (2015 to 2020) and the InSAR technique, multi-temporal coherence maps were generated for an area in Khuzestan Province. Using the Support Vector Machine (SVM) method, a machine learning-based classification scheme was developed. The ground truth soil erosion map of Khuzestan Province was used to train and validate the SVM. Subsequently, using features from Sentinel-1 coherence, Landsat 8-derived normalized difference vegetation index (NDVI), soil texture characteristics, and topography, soil erosion susceptibility maps were produced using different SVM kernels. The overall accuracy of the produced maps for the implemented kernels (linear, polynomial, radial basis function (RBF), and sigmoid) was 72.44%, 80%, 80.44%, and 52%, respectively. Consequently, the RBF kernel showed the highest performance in predicting different soil erosion susceptibility classes. Our results indicate that multi-temporal InSAR coherence can be used as a valuable data source for soil erosion mapping.

کلیدواژه‌ها English

soil erosion
interferometric synthetic aperture radar (InSAR)
coherence
remote sensing
support vector machine
Sentinel-1

 

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