OPTIMAL SAMPLING CONDITIONS FOR ESTIMATING GRASSLAND PARAMETERS VIA REFLECTANCE MODEL INVERSIONS

The sensitivity of grassland bidirectional reflectance to soil, vegeta tion, irradiance, and sensor parameters is assessed, Based on these re sults, a vegetation Bidirectional Reflectance Distribution Function (B RDF) model is inverted with ground reflectance data from the First ISL SCP Field Experiment (FIFE), Results suggest that leaf area index (LAI ) is most accurately retrieved from data gathered in near-infrared ban ds at low solar zenith angles (SZA), and leaf angle distribution is be st retrieved from data gathered in near-infrared bands at high SZA, Ge nerally, leaf optical properties are more accurately estimated from da ta acquired at high SZA. Canopy albedo and fraction of absorbed photos ynthetically active radiation (fAPAR) are also estimated and compared to measured values, Albedo estimates are accurate to about +/-0.01 (4% relative) when model parameters are determined from reflectance data gathered under preferred conditions, Estimates of fAPAR are less accur ate, These results provide a guide for efficiently sampling surface re flectance and accurately retrieving parameters for use in climate and ecosystem models.