水保所知识产出(1956---)知识库

无人机多光谱遥感技术可以快速、无损地监测农作物叶面积指数（LAI）。为研究水分胁迫条件下，
利用无人机多光谱植被指数估算夏玉米LAI的可行性，本研究基于无人机多光谱遥感系统，结合同时期实地
采集的夏玉米LAI，选择5种植被指数，包括归一化差值植被指数（NDVI）、土壤调节植被指数（SAVI）、增
强型植被指数（EVI）、绿度归一化植被指数（GNDVI）和抗大气指数（VARI），作为模型输入参数，使用随
机森林回归算法建立全生育期不同灌溉条件下大田玉米冠层植被指数与LAI之间的关系模型，并与一元线性
回归和多元线性回归算法建立的模型进行对比分析。结果表明，在充分灌溉条件下，植被指数的多元线性
回归模型可以较好地估算LAI（ R 2 = 0.83）；在水分胁迫条件下，植被指数的随机森林回归模型可以较好地估
算LAI（ R 2 = 0.74~0.87），水分胁迫因素对该模型影响较小，且NDVI和VARI对估算LAI的贡献最大。上述
结果表明基于无人机多光谱遥感技术，使用随机森林回归算法估算多种灌溉条件下的夏玉米LAI是可行的。
该研究为实现快速、准确地监测全生育期不同灌溉条件下的大田夏玉米LAI提供了技术和方法支持。

 Maize is an important food crop in China. In order to quickly and non-destructively estimate summer maize leaf area
index (LAI) under different water stress conditions, in this study, maize samples with multiple irrigation treatments throughout
the growth period were used for modeling analysis. Then, based on the unmanned aerial vehicle (UAV) multi-spectral remote
sensing technology, combined with the summer maize LAI collected in the field during the same period, five kinds of vegetation
indices, including the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegeta‐
tion index (EVI), green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI)
were selected in this research as model input parameters, and random forest regression algorithm was used to establish the rela‐
tionship between the field maize canopy vegetation indices and LAI under different irrigation conditions during the entire
growth period. The accuracies of the model were compared with that of the model established by the university linear regression
and multiple linear regression algorithms. The results showed that under sufficient irrigation condition, the vegetation index us‐
ing multiple linear regression model could well (R 2 = 0.83, RMSE = 0.05) estimate LAI; under water stress conditions, the vege‐
tation index using random forest regression model could well estimate LAI (R 2 = 0.74~0.87, RMSE = 0.02~0.10), water stress
factors had little effect on the random forest regression model, and NDVI and VARI contributed the LAI estimation model bet‐
ter. The spatial distribution map of LAI was generated based on the random forest regression algorithm. The above results
showed that it was feasible to use the random forest regression algorithm to estimate the summer maize LAI under various irri‐
gation conditions based on the UAV multi-spectral remote sensing technology. The results indicates that the model established
has a good applicability. This research can provide technical and method support for the rapid and accurate monitoring of field
summer maize LAI under different irrigation conditions during the entire growth period.