基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演

大面积区域作物叶面积指数(LAI)遥感反演,对指导作物管理具有重要的意义。该文基于2008年5-7月在黑河流域开展的大型星-机-地遥感综合试验获取的多角度高光谱PROBA/CHRIS数据及地面同步观测数据,利用PROSAIL辐射传输模型和神经元网络方法反演春小麦LAI,并利用地面实测LAI进行验证和分析,结果表明:PROBA/CHRIS数据的最佳组合波段为band4(555.1nm)、band9(696.9nm)和band15(871.5nm),利用PROBA/CHRIS数据反演LAI时,3角度组合(0°、36°、55°)反演LAI精度最高(R2=0.854,RMSE=0.344;MAE=0....

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Published in农业工程学报 Vol. 27; no. 10; pp. 88 - 94
Main Author 杨贵军 赵春江 邢著荣 黄文江 王纪华
Format Journal Article
LanguageChinese
Published 北京农业信息技术研究中心,北京,100097 2011
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Summary:大面积区域作物叶面积指数(LAI)遥感反演,对指导作物管理具有重要的意义。该文基于2008年5-7月在黑河流域开展的大型星-机-地遥感综合试验获取的多角度高光谱PROBA/CHRIS数据及地面同步观测数据,利用PROSAIL辐射传输模型和神经元网络方法反演春小麦LAI,并利用地面实测LAI进行验证和分析,结果表明:PROBA/CHRIS数据的最佳组合波段为band4(555.1nm)、band9(696.9nm)和band15(871.5nm),利用PROBA/CHRIS数据反演LAI时,3角度组合(0°、36°、55°)反演LAI精度最高(R2=0.854,RMSE=0.344;MAE=0.213)。随着观测角度增加LAI反演精度相应提高,但超过3个角度后,多观测角度数据会带来较大不确定性,影响神经元网络建模,导致LAI反演精度下降。
Bibliography:Leaf area index(LAI) is an important parameter of vegetation ecosystems,which can represent the growth situation of vegetation.The PROBA(project for onboard autonomy)/CHRIS(compact high resolution imaging spectrometer)data acquired in June 4,2008 was used to inverse LAI of spring wheat combing with the radiative transfer model(PROSAIL) and ANN(artificial neural network),and to validate the results according to the in-situ measurements.The optimal bands were selected using segmented principal component analysis.Three bands(center wavelength 551.1 nm、696.9 nm and 871.5 nm,respectively) were finally used to inversion of LAI.The selected combination of three observation angles(0°,36° and 55°) shows high accuracy inversion LAI with R2=0.854,RMSE=0.344,MAE=0.213.The accuracy of inversion LAI can be improved with increasing the number of observation angle.However,if the number of angles is more than three,the accuracy will conversely decrease because of the uncertainty augment of multi-angle data.
11-2047/S
remote sen
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2011.10.016