以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。

Leaf area index (LAI) is an important bio-physical character of vegetation and can be effectively achieved through remote sensing technology. However the LAI inversion from low resolution data induces a scaling bias due to the heterogeneous of the surface and model non-linearity, which may cause the scale effect on the LAI estimate. In this work, the Yingke oasis of Heihe River is selected as the study area. Based on Hyperion data, a two-layer canopy reflectance model (ACRM) is introduced to calculate LAI. The low resolution LAI are then achieved in two ways: LAImean, the mean of LAI, is directly calculated from Hyperion; and the LAIp is computed from linear cumulative Hyperion data. Statistics shows that there is a serious underestima-tion of LAIp. On the basis of LAI-NDVI regresion equation, the Taylor Mean Value Theorem is applied to creat an error factor and to conduct scaling error correction. The result of error correction ( LAIr ) has a high relationship with LAImean, which shows that the method is effective and suitable for scale effect correction and can be used to correct other LAI product, such as MODIS LAI. Finally, the causes for scaling bias are discussed. It is found that the spatial heterogeneous is the key factor which may lead to the error in LAI inversion.