Simultaneous Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo From Multiple-Satellite Data

Abstract

Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products.