生态学杂志2025，Vol.44Issue(4)：1306-1313,8.DOI:10.13292/j.1000-4890.202504.003
基于无人机高光谱的冬小麦LAI估算及LAI遥感产品检验
Estimation of winter wheat leaf area index(LAI)and validation of LAI remote sensing products based on UAV hyperspectral imagery
摘要
Abstract
The variations of leaf area index(LAI)can be used to monitor growth status and estimate yields of win-ter wheat.However,there are several limitations in available studies,including scale mismatch between ground ob-servation data and satellite data leading to scale effect and the weak sensitivity of commonly used multispectral data relative to hyperspectral data.To effectively use the hyperspectral information and select the best bands,LAI esti-mation models were proposed with the aim of improving LAI estimation accuracy.In this study,we used unmanned aerial vehicle(UAV)hyperspectral data,serving as a bridge between ground observations and satellite data.We acquired ground-measured LAI data,UAV hyperspectral data,and GF-1 data during the greening period of winter wheat.Different forms of transformation and calculation of characteristic variables were conducted on the hyperspec-tral data,followed by establishing the area of interest.UAV image pixels with the same scale as ground observation were calculated.We further conducted correlation analyses between LAI and various spectral transformation forms as well as vegetation indices at the same scale.LAI sensitive bands or indices were filtered.LAI inversion at different scales based on UAVs and GF-1 satellites was developed.The results showed that the sensitive bands or indices of winter wheat LAI were 635,655,693,704,714,721,724,763,806,813,900,936 nm first derivative,714,717,763,767,784,806,813,900,903,936 nm second derivative,as well as the sensitive spectral indices SDy,DVI,MSAVI2,NLI,and SAVI.An inversion model for estimating LAI from UAV hyperspectral images was developed utilizing various techniques,such as stepwise regression,partial least squares,and ridge regression.Ac-cording to accuracy comparisons,ridge regression model was considered the optimal.Based on the upscaling method,a GF-1 winter wheat LAI estimation model was constructed.The simulation results were used as relative true values to validate the LAI remote sensing inversion products.The correlation coefficient between FY3_1KM_LAI products and GF_1KM_LAI products reached 0.787,indicating a strong correlation between FY3_LAI products and relative truth.This suggests that the results could be applied in daily operational services and research.In this study,we addressed the accuracy of inversion models under different data sources by upscaling,and discussed the ability of different remote sensing information sources in estimating LAI of winter wheat.Our results provided scientific guid-ance for crop management and theoretical basis for precision agriculture research.关键词
叶面积指数/高分一号卫星/无人机高光谱数据/真实性检验/岭回归Key words
leaf area index/GF-1/UAV hyperspectral data/validation/ridge regression引用本文复制引用
李军玲,李梦夏,熊坤,田宏伟,张渝晨,余卫东..基于无人机高光谱的冬小麦LAI估算及LAI遥感产品检验[J].生态学杂志,2025,44(4):1306-1313,8.基金项目
河南省科技研发计划联合基金项目(232103810089)、河南省自然科学基金青年基金项目(202300410531)、中国气象局·河南省农业气象保障与应用技术重点开放实验室基金项目(AMF202206和AMF202302)和安阳国家气候观象台开放研究基金(AYNCOF202407和AYNCOF202303)资助. （232103810089）
