Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands

The study developed an integrated reflectance model combining radiative transfer and geometric optical properties in order to inverse leaf area index (LAI) of semiarid natural grasslands. In order to better link remote sensing information with land plants, and facilitate regional and global climate change studies, the model introduced a simple but important geometrical similarity parameter related to plant crown shapes. The model revealed the influences of different plant crown shapes (such as spherical, cylindrical/cuboidal and conic crowns) on leaf/branch angle distribution frequencies, shadow ground coverage, shadowed or sunlit background fractions, canopy reflectance, and scene reflectance. The modeled reflectance data agreed with the measured ones in the three Leymus chinensis steppes with different degradation degrees, which validated the reflectance model. The lower the degradation degree was, the better the modeled data agreed with the measured data. After this reflectance model was coupled with the optimization inversion method, LAI over the entire study region was estimated once every eight days using the eight-day products of surface reflectance obtained by multi-spectral Moderate-Resolution Imaging Spectroradiometer (MODIS) during the growing seasons in 2002. The temporal and spatial patterns of inversed LAI for the steppes with different cover degrees, swamps, flood plains, and croplands agreed with the general laws and measurements very well. But for unused land cover types (sands, saline, and barren lands) and forestlands, totally accounting for about 10% of the study region, the reasonable LAI values were not derived by inversing, requiring further revising of the model or the development of a new model for them.