基于无人机多光谱数据和氮素空间分异的玉米冠层氮浓度估算

郝琪, 陈天陆, 王富贵, 王振, 白岚方, 王永强, 王志刚

Estimation of canopy nitrogen concentration in maize based on UAV multi- spectral data and spatial nitrogen heterogeneity

HAO Qi, CHEN Tian-Lu, WANG Fu-Gui, WANG Zhen, BAI Lan-Fang, WANG Yong-Qiang, WANG Zhi-Gang

图8 随机森林回归和支持向量回归在玉米V9 (A, E)、V12 (B, F)、R1 (C, G)和R3 (D, H)在训练集估算冠层氮浓度的性能 NCpre_VI为直接基于多光谱植被指数的预测冠层氮浓度, NCpre_SNH为考虑氮素空间分异的预测冠层氮浓度。缩略词同表2。

Fig. 8 Performance of Random Forest Regression and Support Vector Regression for estimating canopy nitrogen concentrations in corn V9 (A, E), V12 (B, F), R1 (C, G), and R3 (D, H) in training set NCpre_VI was used to predict canopy nitrogen concentration based directly on multispectral vegetation index, and NCpre_SNH was used to predict canopy nitrogen concentration considering nitrogen spatial heterogeneity. Abbreviations are the same as those given in Table 2.