AN APPROACH ON IMPROVING MODIS ALBEDO PRODUCT BY USING THE INFORMATION FROM MODIS LAI PRODUCT

Surface albedo is an important parameter in modeling climate processes as it determines the energy budget of the earth's surface. Operational surface albedo products are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. However, due to the factors associated with weather, sensors and algorithms, albedo products from satellite observations often have many gaps. In this paper, we reformed the semi-empirical kernel-driven model to express the relation between the bidirectional reflectance distribution function (BRDF) and the Leaf Area Index (LAI). Within a small region, the soil properties under plant canopies are similar, therefore the reflectance is unanimous. When the land cover types are identical and the LAI values are same, the canopy top reflectance should be same. Thus, within a small region, the pixels of same properties have the same reflectance. For one pixel, E if the MODIS albedo product has no retrieval but MODIS LAI product has high quality retrieval, E the LAI information can be used to filled the MODIS albedo data gaps. A comparison indicates that the filled data conform well with the in-situ measurements albedo.