Algorithm of Leaf Area Index product for HJ-CCD over Heihe River Basin

Middle-resolution Leaf Area Index (LAI) data are of great importance to scientific research relating to atmospheric composition, climate and weather, and the hydrological cycle. This paper introduces a physically based LAI retrieval technique for HJ-CCD at 30-m resolution. The algorithm is based on a canopy BRDF model that characterizes the surface reflectance as a function of a series of parameters. There are three key factors that influence the LAI retrieval processes: 1) the preprocessing to estimate surface reflectance; 2) the quality of the input land cover data; 3) the accuracy of the input parameters. Accounting for these factors, a 30-m LAI product of Heihe River Basin in the whole year of 2012 is created utilizing the data from HJ-CCD. Then field measurements are used to evaluate the quality of the product. Results show that the algorithm has the ability to produce LAI products as expected. Future researches will focus on reducing the uncertainties brought by the input data and parameters and implementing this algorithm at national scale over China.