Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semiarid regions. The objectives of this study were to improve the inversion accuracy of soil salt content (SSC) in soils with spectral heterogeneity by using optimized spectral indices. Soil samples at a 0–20 cm depth were taken from Keriya Oasis (98 soil samples), Ugan-Kuqa Oasis (49 soil samples), and Ebinur Lake Basin (57 soil samples). SSC and spectral reflectance (SR) of all the 204 soil samples were determined. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate a normalized difference spectral index (NDSI) and ratio spectral index (RSI). Then, the relationships between the indices and SSC were examined, and the most robust relationships were demonstrated. The partial least squares regression (PLSR) method was utilized to develop a predictive model of SSC, and the variable importance in the projection (VIP) method was used during modeling. The results revealed that (i) the salinized soils in different regions had apparent differences in both reflectance and spectral curve morphology, but the optimized spectral indices method effectively overcame the regional heterogeneity of salinized soil hyperspectral characteristics, and the correlation with SSC was always kind, with correlation coefficients up to 0.748 at 0.001 level of significance; (ii) the VIP filtering method effectively selected the optimal independent model, and the modeling accuracy was better than the single optimization index (R²_Pre = 0.83 and RMSE_Pre = 2.31 g·kg⁻¹) by using the combination of two optimal indices; (iii) although the global modeling accuracy was significantly lower than the local modeling accuracy due to the inconsistent salt sensitivity bands of salinized soils in different regions, combined with cross-validation analysis, the global model had the ability to predict soil salinization accurately (R²_Pre = 0.69 and RMSE_Pre = 8.45 g·kg⁻¹). The methods developed in this study can be applied in other arid and semiarid areas. Besides, the study also provides examples for aerospace hyperspectral remote sensing of cross-regional soil salinization.

中文翻译：

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基于最佳光谱指数的区域异质性土壤盐分浓度估算

土壤盐分是全世界最具破坏力的环境问题之一，尤其是在干旱和半干旱地区。这项研究的目的是通过使用优化的光谱指数来提高具有光谱异质性的土壤中盐分（SSC）的反演精度。取自Keriya Oasis（98个土壤样品），Ugan-Kuqa Oasis（49个土壤样品）和Ebinur Lake Basin（57个土壤样品），取0-20 cm深度的土壤样品。测定了全部204个土壤样品的SSC和光谱反射率（SR）。为了全面分析现场收集的高光谱数据，使用各种波段组合来计算归一化差异光谱指数（NDSI）和比率光谱指数（RSI）。然后，检查了指标与SSC之间的关系，并展示了最稳健的关系。利用偏最小二乘回归（PLSR）方法开发了SSC的预测模型，并且在建模过程中使用了投影中的变量重要性（VIP）方法。结果表明：（i）不同地区的盐渍化土壤在反射率和光谱曲线形态上都有明显的差异，但是优化的光谱指数方法有效地克服了盐渍化土壤高光谱特征的区域异质性，并且与SSC的相关性始终是良好的，在0.001显着性水平下的相关系数高达0.748; （ii）VIP筛选方法有效地选择了最佳独立模型，并且建模精度优于单个优化指标（在建模过程中使用了投影中的可变重要性（VIP）方法。结果表明：（i）不同地区的盐渍化土壤在反射率和光谱曲线形态上都有明显的差异，但是优化的光谱指数方法有效地克服了盐渍化土壤高光谱特征的区域异质性，并且与SSC的相关性始终是良好的，在0.001显着性水平下的相关系数高达0.748; （ii）VIP筛选方法有效地选择了最佳独立模型，并且建模精度优于单个优化指标（在建模过程中使用了投影中的可变重要性（VIP）方法。结果表明：（i）不同地区的盐渍化土壤在反射率和光谱曲线形态上都有明显的差异，但是优化的光谱指数方法有效地克服了盐渍化土壤高光谱特征的区域异质性，并且与SSC的相关性始终是良好的，在0.001显着性水平下的相关系数高达0.748; （ii）VIP筛选方法有效地选择了最佳独立模型，并且建模精度优于单个优化指标（但是优化的光谱指数方法有效地克服了盐渍化土壤高光谱特征的区域异质性，与SSC的相关性始终是良好的，相关系数在0.001的显着性水平下高达0.748。（ii）VIP筛选方法有效地选择了最佳独立模型，并且建模精度优于单个优化指标（但是优化的光谱指数方法有效地克服了盐渍化土壤高光谱特征的区域异质性，与SSC的相关性始终是良好的，相关系数在0.001的显着性水平下高达0.748。（ii）VIP筛选方法有效地选择了最佳独立模型，并且建模精度优于单个优化指标（R ² _Pre  ＝ 0.83，RMSE _Pre  ＝ 2.31g·kg ^-1）通过组合两个最佳指标来进行。（iii）尽管由于不同地区盐渍化土壤的盐敏感性带不一致，导致全局建模精度明显低于局部建模精度，但结合交叉验证分析，该全局模型具有准确预测土壤盐渍化的能力（R ² _Pre  = 0.69和RMSE _Pre  = 8.45 g·kg ^-1）。本研究开发的方法可以应用于其他干旱和半干旱地区。此外，该研究还为航空高光谱遥感跨区域土壤盐渍化提供了实例。