Scale effect and error analysis of crop LAI inversion

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 underestimation 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.