Abstract

The accurate and rapid acquisition of the soil salt content and spatial distribution is of great significance for effectively monitoring cropland soil salinization. Using OLI/Landsat-8 images as data sources, shortwave infrared bands (SWIR₁, SWIR₂) are introduced to the traditional vegetation and salinity indices, and the enhanced indices are put forward to obtain an improved inversion effect. After enhancement, the correlation coefficient between the indices and soil salt content increased by 0.01–0.44, and the multicollinearity between indices significantly decreased. The prediction ability of the support vector machine salt inversion model improved from general to fairly good, and the predicted and measured values were close to a linear distribution. The predicted and measured values ranged from 0.39–36.38 g·kg⁻¹ and 0.35–38.09 g·kg⁻¹, respectively, and 63.25% of the regional salt status could be accurately predicted. The results showed that the accuracy of soil salinity inversion can be effectively enhanced by introducing shortwave infrared bands.

摘要

准确快速获取土壤盐分含量及空间分布对于有效监测农田土壤盐渍化具有重要意义。使用 OLI/Landsat-8 图像作为数据源，短波红外波段（SWIR ₁，SWIR ₂) 引入传统植被和盐度指数，并提出增强指数以获得改进的反演效果。增强后，指标与土壤盐分的相关系数增加了0.01~0.44，指标间的多重共线性显着降低。支持向量机盐分反演模型的预测能力由一般提高到较好，预测值和实测值接近线性分布。预测值和测量值范围为 0.39–36.38 g·kg ^-1和 0.35–38.09 g·kg ^-1，分别可以准确预测63.25%的区域盐分状况。结果表明，引入短波红外波段可有效提高土壤盐分反演精度。