全球变化知识资源中心

Long-term global land surface fractional vegetation cover (FVC) data are essential for global climate modeling, earth surface process simulations, and related applications. However, high quality and long time series global FVC products remain scarce, although several FVC products have been generated using remote sensing data. This study aims to use the previously proposed Global LAnd Surface Satellite (GLASS) FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data (denoted as GLASS-MODIS FVC) to generate a long term GLASS FVC product from advanced very high resolution radiometer (AVHRR) data (denoted as GLASS-AVHRR FVC) back to year 1981. The GLASS-AVHRR FVC algorithm adopted the multivariate adaptive regression splines method, which was trained using samples extracted from the GLASS-MODIS FVC product and the corresponding red and near-infrared band reflectances of the preprocessed AVHRR reflectance data from 2003 over the global sampling locations. The GLASS-AVHRR FVC product has a temporal resolution of eight days and a spatial resolution of 0.05 degrees. Through comparison of the GLASS-AVHRR and GLASS-MODIS FVC products from 2013, good temporal and spatial consistencies were observed, which confirmed the reliability of the GLASS-AVHRR FVC product. Furthermore, direct validation using field FVC measurement based reference data indicated that the performance of the GLASS-AVHRR FVC product (R-2 = 0.834, RMSE = 0.145) was slightly superior to that of the popular long term GEOV1 FVC product (R-2 = 0.799, RMSE = 0.174).