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

Abstract

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 extracted. Finally, classification results were compared with the Wishart supervised classification and conventional SVM classification to examine the performance of the proposed method for salinity mapping. Detailed field investigations and ground data were used for the validation of the adopted methods. The overall accuracy and kappa coefficient of the proposed WFS-SVM model were 87.57% and 0.85, respectively that were much higher than those obtained by the Wishart supervised classification with values of 73.87% and 0.68, as well as those of the commonly applied SVM classification of 83.61% and 0.80. Accuracy of different salinized soil mapping was also enhanced with the proposed methodology. The results showed that the proposed method outperformed the Wishart and SVM classification, and demonstrated the advantages offered by the WFS-SVM classification and potentials of PolSAR data in the monitoring soil salinization.

Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

“…They pointed out that machine learning and the fused data are an effective tool in detecting soil salinization. In more recent research, Nurmemet, et al [21], reported that wrapper-based support vector machine can be used together with PolSAR Data for soil salinity mapping at semi-arid areas. In a newly research, Taghadosi, et al [22] showed that soil salinity mapping is viable for semi-arid areas with the used of Sentinel-1 SAR data (VV, VH, and their derived texture) and support vector regression.…”

Landscape Design as a Tool to Reduce Soil Salinization: The Study Case of Keriya Oasis (NW China)

“…The methodology included the use of landscape ecology, a hydro-geological model, the modern drainage system, the concept of the traditional drainage system and adaptive management strategies. The idea of this work was to combine landscape ecological principles and traditional knowledge for ecological design in the arid oasis [5,7,22,[45][46][47][48][49][50][51][52]. Our work suggests that traditional knowledge is the main intellectual source for modern sustainable ecological design.It is reported that, the groundwater table was significantly declined, for example, the Dunhuang oasis'sgroundwater table has been undergoing significant declined, owing to greater pumping, due to expanded irrigation and social-economic development [53].…”

Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

“…where b (1) and W (1) are the offset vector and the weight matrix, respectively, of the output layer to the hidden layer; s is the activation function of the layer; b (2) and W (2) are the offset vector and the weight matrix, respectively, of the output layer to the hidden layer; and G is the activation function of the layer [57].…”

“…Typically, salt soil and alkaline soil are mixed; hence, they are collectively referred to as salt-affected soil. Salt-affected soil is a prominent ecological and environmental problem in the world's dry farming areas [2][3][4]. The total area of the salt-affected soil resources in China is approximately 9.9 million km 2 , which are mainly distributed in the northeast plain, the arid and semi-arid areas in the northwest, the Huang-Huai-Hai plain, and the eastern coastal areas.…”

“…The second-largest is a coastal salt-affected soil region with an area of approximately 0.8 million km 2 . This region is mainly distributed along the coasts of the Yellow Sea, the Bohai Sea, and the East China Sea [5].…”