基于岭回归的土壤全氮含量反演模型

全氮含量是土壤肥力的核心指标之一，快速、准确测定耕层土壤全氮含量对农业生产具有重要意义.以南京市江宁区典型水稻田为研究对象，采用棋盘式布点法选取了60个点位，每个点位均在0～30 cm表土层进行取样，利用大疆精灵4多光谱无人机同时获取了土壤样本分别在5个波段(450,560,650,730,840 nm)的光谱反射率，通过土壤全氮含量与光谱反射率多元线性分析，揭示了光谱反射率数据特有的多重共线性问题，构建了基于岭回归的无人机遥感影像反演土壤全氮含量预测模型.计算结果表明，岭回归系数取0.12时，其回归R²达到了0.408,方差膨胀因子均在10以下，且回归系数具有统计学意义.基于岭回归的反演模型可以较好兼顾反演精度与光谱数据多重共线性问题.研究成果可为无人机遥感土壤氮素营养诊断提供理论依据.

Inversion model of soil total nitrogen content based on ridge regression

Soil total nitrogen content is one of the core indexes of soil fertility. It is important for agricultural production to rapidly and accurately determine the soil total nitrogen content. A typical rice farmland in Jiangning District, Nanjing was taken as the research object. The checkerboard method was used to select 60 points, and each point was sampled in the 0-30 cm topsoil layer. The spectral reflectance of soil samples in the bands of 450, 560, 650, 730 and 840 nm was obtained by using DJI Phantom 4 Multispectral at the same time. Through the multivariate linear analysis of soil total nitrogen content and spectral reflectance, the unique multicollinearity problem characteristic of the spectral reflectance data was revealed. A prediction model for soil total nitrogen content retrieved from UAV remote sensing images based on ridge regression was constructed. The calculation results showed that when the ridge regression coefficient reached 0.12, the linear regression determination coefficient(R²)was 0.408, and the variance inflation factors were under 10, and the regression coefficients are significantly different. The inversion model based on ridge regression can give good consideration to both inversion accuracy and spectral data multicollinearity of spectral data. The research results can provide a theoretical basis for UAV remote sensing diagnosis of soil nitrogen nutrition.