顾及雄穗的玉米全生育期LAI和fAPAR无人机遥感反演方法

全生育期叶面积指数（LAI）和光合有效辐射吸收系数（fAPAR）是玉米生长诊断和监测的关键参数。目前基于冠层辐射传输模型的主流反演方法未考虑玉米雄穗的影响，导致抽穗期后LAI和fAPAR反演精度低。针对这一问题，本项目拟在前期玉米抽穗期中雄穗对冠层反射率影响研究的基础上，开展顾及雄穗的玉米全生育期LAI和fAPAR无人机遥感反演方法研究。（1）利用无人机多角度多光谱反射率数据和地面实测数据，定量评估不同生育期内雄穗对冠层反射率、LAI和fAPAR的影响；（2）耦合雄穗和三维模型，构建时间序列三维辐射传输模型，实现冠层辐射传输精准模拟；（3）通过构建的三维辐射传输模型，发展基于无人机多角度反射率数据的全生育期LAI和fAPAR反演方法，提高LAI和fAPAR反演精度。本项目的研究成果将提高玉米完整生育期内关键参数遥感反演精度，推动定量遥感在农业中的应用，具有重要的理论和应用价值。

Leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation by green vegetation (fAPAR) during the whole growth season are key traits to maize growth diagnosis and monitoring. Currently, the inversion of LAI and fAPAR is mainly from canopy radiative transfer model assuming canopy is composed of leaves, without consideration of male tassels. This leads to low accuracy of estimated LAI and fAPAR after tasseling stage. According to previous study about the impacts of tassels on canopy reflectance on one specific day during the male tasseling stage, this project aims to carry out LAI and fAPAR remote sensing inversion research during the whole growth season from Unmanned Aerial Vehicle (UAV) data. This project is designed to: (1) quantitatively analyze the impacts of male tassels on maize canopy reflectance, LAI and fAPAR from UAV multiangular and multispectral data and ground measurements; (2) build a time series three-dimension radiative transfer model (3D-RTM) by coupling male tassels to realize accurate canopy simulations; (3) explore the optimal inversion algorithm of LAI and fAPAR from UAV multiangular reflectance data on the basis of built 3D-RTM, in order to realize high precision retrieval. The implementation of this project will improve the remote sensing inversion accuracy of LAI and fAPAR during the whole growth season, enhance the applications of quantitative remote sensing in agriculture, and it has important theoretical and practical significances.