Spatial and Quantitative Comparison of Satellite-Derived Land Cover Products over China |
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| Authors: | GAO Hao and JIA Gen-Suo |
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| Institution: | 1. Key Laboratory of Regional Climate-Environment for Temperate East Asia （RCE-TEA）, Institute of Atmospheric Physics, Chinese, Academy of Sciences, Bering 100029, China Graduate University of Chinese Academy of Sciences, Beijing 100049, China
2. Key Laboratory of Regional Climate-Environment for Temperate East Asia （RCE-TEA）, Institute of Atmospheric Physics, Chinese, Academy of Sciences, Bering 100029, China |
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| Abstract: | Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Different classification schemes are a key obstacle to the comparison of products and are considered the main factor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer (MODIS), Global Land Cover 2000 (GLC2000), and the National Land Cover/Use Datasets (NLCD) products, and the author assessed the products by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agreement or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shrubland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agreement could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal-texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling. |
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| Keywords: | land cover comparison spatial pattern quantitative agreement |
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