土壤是自然生态系统的重要组成部分, 是人类赖以生存和农业生产的重要物质基础。 随着社会经济高速发展, 高强度的工农业生产活动导致重金属等各种污染物通过大气沉降、 污水灌溉等途径进入土壤, 并在土壤中不断富集造成土壤盐渍化和土壤重金属污染, 两者是导致全球荒漠化和土壤退化的主要诱因。 然而中国的耕地非常有限, 粮食安全尤为重要。 因此, 如何快速、 准确地大面积反演盐碱地的重金属含量是保障粮食安全的重要研究课题。 针对上述关键问题, 以吉林省镇赉县盐碱地为研究对象, 建立了盐碱地重金属元素锰(Mn)、 钴(Co)和铁(Fe)含量与土壤可见光-近红外光谱数据的定量反演模型。 首先对原始光谱数据分别进行了Savitzky-Golay平滑、 多元散射校正、 连续统去除变换处理; 然后基于预处理后的光谱数据构建了比值(RI)、 差值(DI)和归一化(NDI)三种光谱指数, 通过光谱指数与重金属含量的相关性分析确定模型训练样本, 利用径向基神经网络算法进行建模并反演盐碱地重金属含量; 最后通过相关系数等梯度循环建模的精度分析方法确定了光谱指数与锰、 钴和铁含量相关性显著的敏感波段组合, 建立了基于径向基神经网络算法的盐碱地重金属含量最优反演模型。 研究结果表明, Mn选取相关系数r>0.70, Co选取相关系数r>0.80, Fe选取相关系数r>0.80, 并选取敏感指数组合分别为108组、 690组和31组, 基于上述显著敏感指数组合建立的Mn, Co和Fe最优反演模型R²分别为0.703 4, 0.897 6和0.848 4, 均方根误差RMSE分别为53.007 3, 1.059 2和0.363 4, 平均相对精度达到88.64%, 90.36% 和91.78%。 该研究对盐碱地重金属含量的准确、 快速分析提供了一种有效的方法, 对实现土壤重金属污染治理具有重要的现实意义。

Soil is an important part of the natural ecosystem and an important material basis for human survival and agricultural production. With the rapid socio-economic development, the high-intensity industrial and agricultural production activities lead to various pollutants such as heavy metals entering the soil through atmospheric deposition and sewage irrigation and continuously enriching in the soil, causing soil salinization and soil heavy metal pollution, both of which are the main causes of global desertification and soil degradation. However, China has very limited arable land, and food security is especially important. Therefore, quickly and accurately invert the heavy metal content of saline land in a large area is an important research topic to ensure food security. This paper establishes a quantitative inversion model of the heavy metal content of manganese (Mn), cobalt (Co) and iron (Fe) in saline land with soil visible-near infrared spectral data in Zhenlai County, Jilin Province. Firstly, Savitzky-Golay smoothing, multiple scattering correction and continuous statistical de-transformation were performed on the raw spectral data respectively; then three spectral indices, namely, ratio (RI), the difference (DI) and normalized (NDI), were constructed based on the pre-processed spectral data, and the model training samples were determined by correlation analysis between the spectral indices and heavy metal contents. The radial basis neural network algorithm was used to model and invert the saline heavy metal contents. Finally, the sensitive band combinations with significant correlation between the spectral indices and the contents of Mn, Co and Fe were determined by the accuracy analysis method of the gradient cycle modeling such as correlation coefficient and the optimal inversion model based on the radial basis neural network algorithm was established for the heavy metal content of saline land. The results show that the correlation coefficients r>0.70 for Mn, r>0.80 for Co, and r>0.80 for Fe. The selected combinations of sensitivity indices are 108, 690, and 31 groups, respectively, and the optimal inversion models R² for Mn, Co, and Fe based on the above significant combinations of sensitivity indices are 0.703 4, 0.897 6. The RMSEs were 53.007 3, 1.059 2 and 0.363 4, and the average relative accuracies were 88.64%, 90.36% and 91.78%, respectively. This study provides an effective method for accurate and rapid analysis of heavy metal content in saline soils, which is of great practical importance for achieving soil heavy metal pollution control.