利用冠层光谱反射率数据( Rλ )，对处于开花期的7种果树的树种进行了辨识研究.通过光谱数据重采样、植 被指数求算等相关数据处理，比较了6种卫星传感器与4种植被指数对果树树种的辨识效能，并在优选数据形式、 优化模型参数的基础上，建立了辨识果树树种的BP神经网络模型.主要结论为:(1)6种卫星传感器辨识果树树种 的效能由强到弱的排列顺序为:MODIS、ETM+、QUICKBIRD、IKONOS、HRG、ASTER;(2)在4种植被指数中，RVI对 果树树种的辨识效能最强，其次是NDVI，SAVI与DVI的辨识效能相对较弱;(3)用MODIS或ETM+传感器的近红 外通道与蓝光通道上的反射率数据，求算的RVI与NDVI对果树树种的辨识效能相对较强;(4)在 R λ 及其22种变 换数据中，波长间隔设为9nm的d 1 ［log(1/ R λ ) ］，是建立BP神经网络模型的首选数据形式

By using the spectral reflectance data ( R λ ) of canopies， seven species of fruit trees were identified during flowering period. Firstly， the identification capacity of six kinds of satellite sensors and four kinds of vegetation index was compared on the basis of resampling the spectral data for six kinds of pre-defined filter function and calculating vegetation index. Then， a BP neural network model for identifying seven species of fruit trees was established on the basis of choosing the best transformation of R λ and optimizing the model parameters. The main conclusions are as follows:(1) the order of the identification capacity of six kinds of satellite sensors from power to weak is: MODIS、ETM+、QUICKBIRD、IKONOS、 HRG、ASTER， (2)among four kinds of vegetation index， the identification capacity of RVI is the most powerful， next is NDVI， and the identification capacity of SAVI or DVI is relatively weak， (3)the identification capacities of RVI and NDVI that are calculated with the reflectances of near-infrared and blue channels of ETM + or MODIS sensor are relatively powerful， (4)among R λ and it's 22 kinds of transformation data， d 1 ［log(1/ R λ )］(derivative gap is set as 9 nm) is the best transformation for setting up BP neural network model.