Influence of land cover data on regional forest leaf area index inversion
地表覆盖分类数据对区域森林叶面积指数反演的影响

In this study, six different land cover datasets were employed in conjunction with MODIS 1km reflectance data to inverse LAI of forests using an algorithm based on the 4-scale geometrical optical model in Jian City, Jiangxi Province, China. Land cover datasets used in this study include five global land cover datasets (Three were produced by the United States Geological Survey (USGS), University of Maryland (UMD), and Boston University (BU), respectively. Two were constructed in Europe.) and a regional land cover map produced using Landsat TM images. For assessing the impact of land cover on the inversion of LAI, LAI images inversely produced with different land cover datasets were compared with LAI data sampled from a 30 m LAI map at 1 km and 4 km scales, respectively. The 30 m LAI map was produced with TM reflectance images and ground measurements of LAI. The results show that the land cover datasets of TM and GLOBCOVER which was created by European Space Agency are the best for the inversion of LAI in this study area. At 1 km scale, the R2 values of LAI inversed using TM and GLOBCOVER land cover datasets with TM LAI estimated using an statistical model are 0.44 and 0.40, respectively. At 4 km scale, these R2 values increase to 0.57 and 0.54. The MODIS land cover data of BU is the third best data for the inversion of LAI, the R2 values between LAI inversed using this land cover dataset and TM LAI are 0.38 and 0.51 at 1 km and 4 km scales, re-spectively. The land cover datasets of UMD and European GLC2000 resulted in large discrepancies between inversed LAI and TM LAI. The averages of LAI inversed using these two land cover datasets are about 20% lower than TM LAI at 1 km and 4 km scales. Sensitivity analysis shows that inversed LAI is sensitive to clumping index. This study proved that reliable land cover data is required for improving the accuracy of inversed LAI at regional/global scales.