基于无人机多光谱影像的水稻冠层SPAD值预测研究

比较筛选水稻冠层SPAD值估测模型，为无人机多光谱遥感反演水稻SPAD值提供依据。利用无人机获取水稻拔节期、抽穗期、乳熟期的冠层多光谱影像，选取7种常用的植被指数，利用3种回归方法建立基于植被指数的水稻叶片SPAD值反演模型。结果表明，在不同生育期与水稻叶片SPAD值相关系数最高的植被指数不相同，拔节期最高的是GNDVI，抽穗期最高的是CI_Green，乳熟期最高的是CI_Rededge。抽穗期是水稻叶片SPAD值反演的最佳时期，模型具有较好的建模精度和估测效果，其中多元线性回归的建模精度较高，偏最小二乘回归模型的估测效果最好。试验结果可为水稻长势的实时无损监测提供参考。

Prediction of Rice Canopy SPAD Value Based on UAV Multispectral Images

The aim of this study is to compare and screen SPAD estimation models of rice canopy, and to provide a basis for inversing SPAD value of rice by unmanned aerial vehicle (UAV) multispectral remote sensing. This paper used UAV to obtain the canopy multispectral images of rice at jointing stage, heading stage and milky maturity stage. Seven commonly used vegetation indices were selected and three regression methods were used to establish the SPAD value inversion model of rice leaves. The results showed that the vegetation indices with the highest correlation coefficient with SPAD value of rice leaves were different at different growth stages. GNDVI was the highest at jointing stage, CI_Green was the highest at heading stage, and CI_Rededge was the highest at milky maturity stage. The heading stage was the best inversion stage of SPAD value, and the model had good modeling precision and estimation effect. According to the accuracy test, multivariate linear regression model had relatively high modeling accuracy, and partial least squares regression had the best estimation effect. Thus, this research provides a new technology to supervise the growth information of rice and other crops. The experimental results can provide methods and reference for the real-time and nondestructive monitoring of rice growth.