The estimation of gross primary productivity (GPP) and evapotranspiration (ET) would be effected by the spatial distribution of vegetation foliage, which can be described with the canopy clumping index (CI). And the CI is an important factor to characterize the terrestrial ecosystem and model land-surface processes. The multi-angle remote sensing data provide an effective way to produce long-term global CI data.
Currently, the CI data products have been produced based on the correlation between CI and the normalized difference between hotspot and darkspot (NDHD). But the global CI data products do not take into consideration of the influence of solar zenith angle (SZA) and the surface reflectance model, seriously affecting the accuracy of the CI. In addition, because of lacking for long-term CI data and ignoring the seasonal and interannual changes, the current CI data products cannot meet the demand the research of CI.
Aiming at the problems of current CI data products, Prof. Hongliang Fang and the research team, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), researched on the CI estimation algorithm and produced the new global CI data products (LIS-CI-A1). The research team analyzed the CI value on the different BRDF model and SZA using the field measured leaf area index and high spatial resolution image data and put forward the optimal BRDF model and SZA to estimate the CI, solving the problem of CI estimation in sparse vegetation areas and raising the global CI data product accuracy effectively.
The research team has produced the long term global CI data products, supplying global maps of CI at 8-days steps and 500-m spatial resolution for 2001-2016.
This study was supported by National Natural Science Foundation of China (41471295) and the National Key Research and Development Program of China (2016YFA0600201).
