融合无人机多光谱和纹理特征的马铃薯LAI估算

目的 研究融合无人机遥感影像多光谱信息和纹理特征估算马铃薯Solanum tuberosum叶面积指数(Leaf area index，LAI)方法，提高马铃薯LAI反演精度。方法 利用大疆P4M无人机采集2021年2－4月南方冬种马铃薯幼苗期、现蕾期、块茎膨大期多光谱影像，用LAI-2000冠层分析仪实测LAI数据。提取影像光谱、纹理等信息，分析植被指数、纹理特征与LAI的相关性，基于R²_adj的全子集分析优选特征变量。采用主成分分析，融合光谱和纹理特征，用PCA-MLR(Principal component analysis-multiple linear regression)模型估算马铃薯LAI。结果 从幼苗期到块茎膨大期，PCA-MLR估算模型优于T-MLR(Texture multiple linear regression)和VI-MLR(Vegetation index multiple linear regression)模型，R²分别为0.73、0.59和0.66。结论 本研究提出一种估算马铃薯LAI的PCA-MLR方法，为马铃薯的长势监测和田间管理提供数据支持。

Potato LAI estimation by fusing UAV multi-spectral and texture features

Objective Develop a method to improve the potato (Solanum tuberosum) leaf area index (LAI) estimation accuracy using the UAV multiple spectral wavebands and texture information.Method The DJI P4M drone was used to collect multispectral images of the southern winter potato at seedling period, budding period and tuber swelling period from February to April 2021. LAI data were measured by LAI-2000 canopy analyzer. The spectral and texture characteristics of images were extracted. The correlations between vegetation index, texture characteristics and LAI were analyzed. The selected characteristic variables were analyzed based on subset of adjusted R²_adj. The principal component analysis was used to fuse spectrum and texture features, and the principal component analysis-multiple linear regression (PCA-MLR) model was used to estimate potato LAI.Result From the seedling period to the tuber swelling period, the PCA-MLR estimation model was better than texture multiple linear regression (T-MLR) and vegetation index multiple linear regression (VI-MLR) model, with R² of 0.73, 0.59 and 0.66 respectively. Conclusion This study proposed a method of PCA-MLR to estimate the potato LAI and improve the levels of the potato growth monitoring and field management.