Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image

作者：Bing Guo, Wenqian Zang, Wei Luo, Ye Wen, Fei Yang, Baomin Han, Yewen Fan, Xi Chen, Zhen Qi, Zhen Wang, Shuting Chen, Xiao Yang · 发表于：Geomatics Natural Hazards and Risk · 年份：2020 · DOI：10.1080/19475705.2020.1721573 · 被引用次数：52 · 研究领域：Soil Geostatistics and Mapping、Geochemistry and Geologic Mapping、Soil erosion and sediment transport

The Yellow River Delta, with the most typical new wetland system in warm temperate zone of China, is suffering from increasingly serious salinization. The purpose of this study is to utilize five typical surface parameters, including Albedo (the surface Albedo), NDVI (vegetation index), SI (salinity index),WI (humidity index), and IFe2O3 (Iron oxide index), to construct 10 different feature spaces and, then, propose two different kinds of monitoring models (point-to-point model and point to line model) of soil salinization. The results showed that the inversion accuracy of the IFe2O3 feature space detection index based on the point-to-point model was the highest with R2=0.86. However, the inversion accuracy of Albedo-NDVI feature space detection index based on the point-to-point model is the lowest with R2=0.72. This is due to the fact that NDVI is not sensitive enough to indicate the status of vegetation grown in the region with low (disturbance of soil background) and high (influenced by the saturation effect) vegetation coverage. The chemical weathering is also a primary cause of soil salinization, during which Fe2O3 is formed by the reaction of oxygen present in the atmosphere with primary Fe2+ minerals in the soil .Therefore, the Albedo−IFe2O3 feature space detection index based on the point-to-point model has a stronger applicability to monitor the information of soil salinization in the Yellow River Delta. This above point-to-point detection model can be utilized as a ...