Spectroscopy is regarded as a quick and nondestructive method to classify and quantitatively analyze many elements of the soil. Visible and Near-infrared reflectance spectroscopy offers a conductive tool for investigating soil heavy metal pollution. The main goal of this work is to obtain spectral optimized indices (RSI, NPDI and NDSI) related to soil heavy metal Arsenic (As), to estimate the As contents in soil based on geographically weighted regression model (GWR), and to investigate the plausibility of using these spectral optimized indices to map the distribution of heavy metal Arsenic in the soil of coal mining areas. The spectral optimized indices (RSI, NPDI and NDSI) derived from the original and transformed reflectance (the reciprocal (1/R), logarithm (lg^R), logarithm-reciprocal (1/lg^R) and root mean square method ($\sqrt{R}$) were used to construct the GWR models. Then, the variables (RSIs, NPDIs and NDIs) were applied in estimating the Arsenic (As) concentration and in the mapping of the As distribution in this study region. The NPDIs calculated by the original and transformed reflectance (R, 1/R, lg^R, 1/lg^R, and $\sqrt{R}$) indicated higher correlation coefficient values than NDSI and RSI. The highest correlation coefficient and lowest p-values (r≥0.73 and p=0.001) were found in thenear-infrared (NIR, 780–1100 nm) and shortwave infrared (SWIR, 1100–1935 nm). From the 4 prediction models (GWR) performances, it can be seen that Model-a (R) showed superior performance to the other three models (Model-b (1/R), Model-c ($\sqrt{R}$) and Model-d (lg^R)), and it has the highest validation coefficients (R² = 0.831, RMSE =4.912 μg/g, RPD=2.321) and lowest AIC (Akaike Information Criterion) value (AIC=179.96). NPDI_{1417 nm, 1246 nm} is more sensitive and potential hyperspectral index for As in the study area. Thus, the two band optimized index (NPDI_{1417 nm, 1246 nm}) might be recommended as an indicator for estimating soil As content. The hyperspectral optimized indices may help to quickly and accurately evaluate Arsenic contents in soil, and furthermore, the results provide theoretical and data support to access the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.

光谱法被认为是对土壤中许多元素进行分类和定量分析的一种快速且无损的方法。可见和近红外反射光谱法为研究土壤重金属污染提供了一种传导工具。这项工作的主要目的是获得与土壤重金属砷（As）相关的光谱优化指标（RSI，NPDI和NDSI），根据地理加权回归模型（GWR）估算土壤中的As含量，并研究使用这些光谱优化指标绘制煤矿区土壤中重金属砷分布的合理性。从原始和变换后的反射率（倒数（1 / R），对数（lg ^R），对数倒数（1 / lg ^R）得出的光谱优化指标（RSI，NPDI和NDSI））和均方根法（$\sqrt{\mathrm{[R}}$）用于构建GWR模型。然后，将变量（RSI，NPDI和NDI）应用于估算该研究区域中砷（As）的浓度以及绘制As分布图。由原始反射率和转换反射率（R，1 / R，lg ^R，1 / lg ^R和$\sqrt{\mathrm{[R}}$）表示相关系数值高于NDSI和RSI。最高相关系数和最低p -值（[R ≥0.73和p = 0.001）在thenear红外（NIR，780-1100纳米）被发现与短波红外（SWIR，1100年至1935年纳米）。从4个预测模型（GWR）的性能中可以看出，模型a（R）表现出优于其他三个模型（模型b（1 / R），模型c（$\sqrt{\mathrm{[R}}$）和Model-d（lg ^R）），且验证系数最高（R ² = 0.831，RMSE = 4.912μg/ g，RPD = 2.321），最低AIC（Akaike信息准则）值（AIC = 179.96）。NPDI _{1417 nm，1246 nm}对研究区域中的As更为敏感，并且具有潜在的高光谱指数。因此，两波段优化指标（NPDI _{1417 nm，1246 nm}）可能被推荐作为估算土壤As含量的指标。高光谱优化指标可以帮助快速，准确地评估土壤中的砷含量，而且，该结果为获取表层土壤中重金属污染的分布提供了理论和数据支持，促进了对采矿环境污染和可持续发展的快速有效调查。生态学。