Rapid and accurate estimation of soil nutrient content based on hyperspectral data is an optimal method for the monitoring of soil nutrient and inversion of soil physical and chemical characters. The relationship between soil nutrient content and spectral reflectance was analyzed with soil samples being collected from the loess hilly-gully region of northern Shaanxi Province. The prediction models of the content of soil organic matter, total nitrogen, total phosphorus and total potassium were constructed by the combination of three techniques, including partial least squares (PLS), multiple linear regression (MLR), and support vector machine (SVM). Then, the optimal model was selected by comparison analysis. The results showed good correlations between the content of soil nutrients and spectral reflectance in visible region (400-760 nm) and near infrared region (760-1100 nm). The maximum values of correlation coefficient located in both spectral regions. The SPA-SVM model had the best applicability and highest inversion accuracy for the contents of all soil nutrients, with simple and efficient modeling process. Our results provided a reference for applying machine learning algorithm in the construction of hyperspectral prediction model of soil nutrient content in the loess hilly-gully region.
基于高光谱数据快速准确估算土壤养分含量,可为土壤养分监测及土壤理化参数反演提供优化方法.本研究在陕北黄土丘陵沟壑区选取典型样地,分析土壤养分含量与光谱反射率的定量关系,采用连续投影算法提取其光谱特征波长,利用偏最小二乘法、多元线性回归法、支持向量机法分别对土壤有机质、全氮、全磷、全钾含量进行预测并对比分析,构建该区域土壤养分含量的最优高光谱预测模型.结果表明: 黄土丘陵沟壑区土壤养分含量与光谱反射率在可见光区(400～760 nm)和近红外区(760～1100 nm)相关性较高,相关系数最大值均位于这两个光谱区间.4种土壤养分含量的SPA-SVM模型的普适性好且反演精度高,建模过程简单高效,适用于小数据量试验.本研究结果可为采用机器学习算法构建黄土丘陵沟壑区土壤养分含量高光谱预测模型提供参考.
Keywords: feature wavelength; loess hilly-gully region; prediction model; soil nutrient.