基于近地传感光谱协同的土壤重金属含量空间分布预测方法

Prediction Method for Soil Heavy Metal Content Based on Covariates over Proximally Sensed Spectra

摘要:

土壤重金属采样、分析与污染评价耗费大量人力和物力，借助易获取环境协变量信息对提高土壤重金属污染监测效率意义重大。近地光谱是土壤属性综合响应，在反映土壤重金属信息方面有着巨大的研究潜力。为考察近地光谱辅助预测土壤重金属含量的能力，测量了109个表层土样的近红外光谱，并提取与土壤镍密切相关的光谱信息；再以土壤机械组成及其与光谱信息的组合作为辅助变量建立协同克里格模型，并比较土壤镍空间预测制图精度。结果表明：以粉粒含量和光谱2 380 nm波段吸收率共同作为辅助变量的模型结果优于只以粉粒含量作为辅助变量的模型，交叉验证决定系数R2CV由0.49提高到0.68，交叉验证均方根误差(RMSECV)由11.3 mg/kg降至9.5 mg/kg。这说明近红外光谱作为一种易获取的辅助信息，可协同土壤机械组成构建空间预测模型以提高区域土壤重金属的调查精度。研究结果可为土壤重金属含量空间分布预测提供一种经济高效的解决方案。

Abstract:

Sampling, analyzing, and assessing soil heavy metal pollution requires significant manpower and resources. Access to easily obtainable environmental covariate information is crucial for enhancing the efficiency of soil heavy metal pollution monitoring. The spectra from proximal soil sensing are a comprehensive response of soil properties, and they have great potential to reveal heavy metal concentrations in soil. Near-infrared spectral characteristics of heavy metals in the surface soil of 109 samples were analyzed, and spectral information closely linked to soil Ni was extracted. This data was then utilized as auxiliary information to develop a co-Kriging model. Subsequently, co-Kriging models were constructed by using soil mechanical composition, and its combination with spectral information as auxiliary variables to compare the accuracy of spatial prediction mapping of Ni concentrations in the soil. The results indicated that the model incorporating silt concentrations in addition to the absorbance at 2 380 nm as auxiliary variables outperformed the model by using only silt concentrations. The cross-validated coefficient of determination R2CV was increased from 0.49 to 0.68, while the cross-validated root mean squared error (RMSECV) was decreased from 11.3 mg/kg to 9.5 mg/kg. These findings suggested that NIR spectra, as readily accessible auxiliary information, can be used with soil mechanical composition to develop spatial prediction models and further enhance the precision of regional soil heavy metal surveys. The research result can offer a cost-effective solution for the spatial prediction of heavy metals in soil.