Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Abstract

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) OPEN ACCESSRemote Sens. 2015, 7 8804 and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

“…Agriculture depends on water from the Keriya River, which is ultimately supplied by 430 glaciers in the Kurum Mountains [15]. After approximately 700 km of flow, the river disappears in the Derya Boyi ancient village [17,18]. Increasing population and economic development have driven the Oasis deep into the marginal ecotone frontier [25,26], Unsustainable planning of land and water resources in the Oasis has caused water shortages in some areas and caused soil salinization due to excessive water logging.…”

“…Electrical conductivity (EC) was measured using a Hydra probe II for verification of laboratory measurements. The total soluble salt content (g/kg) was calculated using a regression equation previously established between EC and total soluble salt [17].…”

“…We observed that the significant decrease in ecotone water logging between 1977 and 1999 caused a slight decrease in light salinization, but the increase in the 2000s caused a larger expansion in light salinization. These fluctuating correlations of light salinization and water logging are related to characteristics of spatial distribution of light salinization such as wide range ( Figure A2), relatively flat and closed terrain geomorphology [10,17], but also responded to the increased runoff and increased ratio of evapotranspiration/precipitation in the near term ( Figure A1c-e) and to flash flood induced drainage subsiding conditions. Therefore, the light salinization predictions show weak reversibility but indicate that changes are likely to occur.…”

“…The estimates for severe salinization are mainly related to geographical location. Severe salinized areas were mainly located in very flat and pit areas, such as the lower reaches of river banks and end parts of natural drainage channels ( Figure A2) [10,17], where there is sufficient underground seepage, a permanent shallow groundwater table, and a location far from the Oasis. These conditions made the severe salinized area less responsive to most of the disturbances from anthropogenic and natural systems, except for the floods.…”

“…The main crops are cotton, Figure 1. Topographic map of the sampling points of the study area (A); the Keriya Oasis (B); and the PRC and XUAR (C) [17].…”