Modeling Landsat Clumping Index Basing On Modis And Field Data: A Machine Learning Approach

Clumping index (CI) is an important vegetation structure parameter in the estimation of leaf area index (LAI) and the modeling of ecological and meteorological process. With the development of surface process modeling and remote sensing technology, high resolution CI product is urgently needed but no appropriate high resolution multi-angle reflectance satellite data is currently available to produce such product. In recent years, random forest algorithm has been widely used in the derivation of high resolution products from remote sensing data. In this study, the random forest algorithm was used to estimate Landsat CI basing on MODIS and field data. The developed predictive model was validated using 26 field measurements and the predicted CI shown a good consistency with the field CI (R-2=0.63, bias=0.005, RMSE=0.10).