As more satellite-derived land cover products used in the study of global change, especially climate modeling, assessing their quality has become vitally important. In this study, we developed a distance metric based on the parameters used in weather research and forecasting （WRF） to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme （IGBP） classification scheme. We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spectoradiometer （MODIS） products, and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model. Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes, while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes. The degree of disagreement varied significantly among seven regions of China. The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly. High accuracy and fuzzy agreement occurred in the following classes： water, grassland, cropland, and barren or sparsely vegetated. Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals. Some classes need to be improved further; the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling. Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.
Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes.Remote sensing-based models,which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics,improve spatially extended estimates of vegetation productivity with high accuracy.In this study,the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM),which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types.The field data were collected by coordinating observations at nine stations in 2008.The results indicate that in the region during the growing season GPP was highest in cropland sites,second highest in woodland sites,and lowest in grassland sites.VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas.Further,Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas,while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation.This study demonstrates the potential of satellite-driven models for scaling-up GPP,which is a key component for studying the carbon cycle at regional and global scales.
WANG He-SongJIA Gen-SuoFENG Jin-MingZHAO Tian-BaoMA Zhu-Guo
