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Abstract
Climate and land–atmosphere models rely on accurate land-surface parameters, such as Leaf Area Index (LAI). It is crucial that the estimation of LAI represents actual ground truth. Yet it is known that the LAI values retrieved from remote sensing images suffer from scaling effects. The values retrieved from coarse resolution images are generally smaller. Scale transformations aim to derive accurate leaf area index values at a specific scale from values at other scales. In this paper, we study the scaling effect and the scale transformation algorithm of LAI in regions with different vegetation distribution characteristics, and analyse the factors that can affect the scale transformation algorithm, so that the LAI values derived from a low resolution dataset match the average LAI values of higher resolution images. Using our hybrid reflectance model and the scale transformation algorithm for continuous vegetation, we have successfully calculated the LAI values at different scales, from reflectance images of 2.5 m and 10 m spatial resolution SPOT-5 data as well as 250 m and 500 m spatial resolution MODIS data. The scaling algorithm was validated in two geographic regions and the results agreed well with the actual values. This scale transformation algorithm will allow researchers to extend the size of their study regions and eliminate the impact of remote sensing image resolution.
Acknowledgements
This paper was supported by the Special Funds for Major State Basic Research Project (Grant No. 2007CB714402), the National Natural Science Foundation of China (Grant No. 40871186 and No. 40401036) and National High-Tech Development Program (Grant No. 2005AA133011XZ07). We would like to thank the MODIS team for making available the reflectance products to the public freely, the referees for helpful suggestions and Miss Huiran Jin for the earlier work. We also thank Drs Murrel and Coral for the English edit.