摘要 : The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVIo) and full vege... 展开 The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVIo) and full vegetation NDVI (NDVI infinity). Usually it is assumed that NDVI, is close to zero (NDVIo similar to 0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI=0.2) and is highly variable (standard deviation = 0.1). We show that the underestimation of NDVI,, yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI, and NDVI infinity derived from global scenes yields overestimations of Fg that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2 < NDVIpixel < 0.4. When using contenninous U.S. scenes to derive NDVIo and NDVI infinity, the overestimation is less (0.10-0.17 for 0.2 < NDVIpixel < 0.4). As a result, parts of the contenninous U. S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI along with information on historical NDVIpixel values to compute a statistically most-likely estimate of Fg. Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVIo values estimated from whole scenes. At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance. (c) 2007 Elsevier Inc. All rights reserved. 收起