A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data

作者：Ilyas Nurmemet, Vasit Sagan, Jianli Ding, Ümüt Halik, Abdulla Abliz, Zaytungul Yakup · 发表于：Remote Sensing · 年份：2018 · DOI：10.3390/rs10040598 · 被引用次数：58 · 研究领域：Synthetic Aperture Radar (SAR) Applications and Techniques、Soil Moisture and Remote Sensing、Soil Geostatistics and Mapping

Timely monitoring and mapping of salt-affected areas are essential for the prevention of land degradation and sustainable soil management in arid and semi-arid regions. The main objective of this study was to develop Synthetic Aperture Radar (SAR) polarimetry techniques for improved soil salinity mapping in the Keriya Oasis in the Xinjiang Uyghur Autonomous Region (Xinjiang), China, where salinized soil appears to be a major threat to local agricultural productivity. Multiple polarimetric target decomposition, optimal feature subset selection (wrapper feature selector, WFS), and support vector machine (SVM) algorithms were used for optimal soil salinization classification using quad-polarized PALSAR-2 data. A threefold exercise was conducted. First, 16 polarimetric decomposition methods were implemented and a wide range of polarimetric parameters and SAR discriminators were derived in order to mine hidden information in PolSAR data. Second, the optimal polarimetric feature subset that constitutes 19 polarimetric elements was selected adopting the WFS approach; optimum classification parameters were identified, and the optimal SVM classification model was obtained by employing a cross-validation method. Third, the WFS-SVM classification model was constructed, optimized, and implemented based on the optimal match of polarimetric features and optimum classification parameters. Soils with different salinization degrees (i.e., highly, moderately and slightly salinized soils) were ...