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Abstract

Within the past decade, several global land cover data sets derived from satellite observations have become available to the scientific community. They offer valuable information on the current state of the Earth's land surface. However, considerable disagreements among them and classification legends not primarily suited for specific applications such as carbon cycle model parameterizations pose significant challenges and uncertainties in the use of such data sets. This paper addresses the user community of global land cover products. We first review and compare several global land cover products, i.e. the Global Land Cover Characterization Database (GLCC), Global Land Cover 2000 (GLC2000), and the MODIS land cover product, and highlight individual strengths and weaknesses of mapping approaches. Our overall objective is to present a straightforward method that merges existing products into a desired classification legend. This process follows the idea of convergence of evidence and generates a 'best-estimate' data set using fuzzy agreement. We apply our method to develop a new joint I-km global land cover product (SYNMAP) with improved characteristics for land cover parameterization of the carbon cycle models that reduces land cover uncertainties in carbon budget calculations. The overall advantage of the SYNMAP legend is that all classes are properly defined in terms of plant functional type mixtures, which can be remotely sensed and include the definitions of leaf type and longevity for each class with a tree component. SYNMAP is currently used for parameterization in a European model intercomparison initiative of three global vegetation models: BIOME-BGC, LPJ, and ORCHIDEE. Corroboration of SYNMAP against GLCC, GLC2000 and MODIS land cover products reveals improved agreement of SYNMAP with all other land cover products and therefore indicates the successful exploration of synergies between the different products. However, given that we cannot provide extensive validation using reference data we are unable to prove that SYNMAP is actually more accurate. SYNMAP is available on request from Martin Jung. (c) 2006 Elsevier Inc. All rights reserved. [References: 38]