Li Shu-Min, Li Hong, Sun Dan-Feng, Zhou Lian-Di
Institute of Agricultural Integrated Development, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Oct;29(10):2725-9.
The present paper selected Qing yundian town and Weishanzhuang town in Da Xing District, and Gaoling ying town in Shunyi District as test areas, using MODIS data and ASTER data in different scales. The feasibility of winter wheat LAI inversion by PROSAIL physical model, especially the stability of remote sensing data in different scales, was discussed, and the results from experience model inversion were compared with that from statistical methods. The values of all samples LAI inversion from experience model are close in a region, which means experience model is a reflection of general growing trend, ignoring spatial heterogeneity of the regional leaf area index. But the value of LAI inversion from physical model can be truer in reflecting spatial heterogeneity of the regional leaf area index. The value of LAI inversion from physical model is more real, compared with experience model. With the method of linear weighing, the scale conversion was accomplished, and the LAI inversion results from different remote sensing scale data were compared, and were found similar. The result shows that in the process of large-scale regional LAI inversion, physical model inversion is more valid.
本文选取大兴区青云店镇、魏善庄镇以及顺义区高丽营镇作为试验区，采用不同尺度的MODIS数据和ASTER数据，探讨了利用PROSAIL物理模型反演冬小麦叶面积指数（LAI）的可行性，尤其是不同尺度遥感数据的稳定性，并将经验模型反演结果与统计方法反演结果进行了比较。经验模型反演的所有样本LAI值在一个区域内相近，这意味着经验模型反映的是总体生长趋势，忽略了区域叶面积指数的空间异质性。但物理模型反演的LAI值能更真实地反映区域叶面积指数的空间异质性。与经验模型相比，物理模型反演的LAI值更真实。采用线性加权法完成了尺度转换，比较了不同遥感尺度数据的LAI反演结果，发现结果相似。结果表明，在大范围区域LAI反演过程中，物理模型反演更有效。
