基于高光谱成像分析的耕地土壤含水量反演模型研究

摘要：基于高光谱成像技术快速高效地预测土壤水分含量（SMC）可为制定精准农业方案（如林业灌溉和环境管理）提 供重要依据。为了建立高效的SMC反演模型，本文采集了Chair Hill地区117个栽培土壤样品，利用GaiaSorter高光谱 分选仪进行测试。利用小波变换对采集的土壤反射率数据集进行预处理，然后采用竞争自适应重加权采样算法和连续投 影算法（CARS-SPA）相结合对波段进行最优选择。选取695、711、736、747、767、778和796 nm七个波长作为SMC 反演模型的因子。采用流行的线性回归算法构建该模型。结果表明，多元线性回归算法建立的反演模型（预测的 R2 为 0.83，RMSE为0.0078）可行且精度较高，可在农业灌溉、作物产量远程监测等大面积耕地土壤SMC预测中发挥重要作 用。

关键词：高光谱成像、土壤水分含量、小波变换、CARS-SPA算法、反演模型

期刊：Agriculture 2020, 10, 292;

DOI：10.3390/agriculture10070292

设备：GaiaSorter

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Abstract：Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

Keywords：hyperspectral imaging; soil moisture content; wavelet transform; CARS-SPA algorithm; inversion model