The PEEL_0 ("Partial Equilibrium Economic Land use model, time zero") initialization dataset.

The PEEL0 land cover dataset for the conterminous USA characterizes each of its five arc- minute cells in terms of sub-pixel area fractions for 15 land use and natural cover classes, with the fractions for each cell summing to unity. This dataset has three advantages relative to other products. First, the cover classification addresses distinctions important in studies of the economic dimensions of land use and land cover change, by distinguishing between land uses associated with human and natural processes and among different crops while simultaneously providing a complete representation of cover in each cell. Second, aggregates for the various cover classes in PEEL0 compare more favorably with national-level statistics from USDA Major Land Uses (MLU) census data for 2002 than do other sources. Aggregate cultivated land is within 0.8% of the MLU value, compared with 16.2% for the Modis Land Cover Type (MLCT) primary cover product, 1.8% for the National Land Cover Database (NLCD), and 1.2% for the Agricultural Lands in the Year 2000 (Agland2000) dataset. Aggregate water, natural, and urban cover classes are within 2.2, 1.0, and 6.1%, respectively, compared with deviations of 24.0, 0.01, and 69.2% for MLCT primary and 2.2, 1.35, and 6.1% for NLCD. Third, the spatial distribution of cultivated land is improved; PEEL0’s per cell subpixel fraction root mean square error for cropland relative to NLCD is 0.149 versus 0.175 for the 2001 MLCT primary classification, a 16% improvement. PEEL0’s improved performance is due to the method used in its construc- tion, by which information is combined from multiple sources, including spatial datasets (here, 2001 MLCT, 2001 NLCD, and Agland2000) and agricultural production statistics, to guide both aggregation of multiple fine-scale land use/land cover classifications, decomposition of hybrid classes to separate their constitutive land uses and natural covers, and corrections to classifications to address systematic biases. This procedure is adaptable to other regions, datasets, and requirements. References: Best, N. (2011), "Synthesis of a complete land use/land cover data set for the conterminous United States emphasizing accuracy in area and distribution of agricultural activity", Master’s thesis, Northeastern Illinois University. Best N., et al. (forthcoming), "Synthesis of a complete land use/land cover dataset for the conterminous United States", RDCEP Working Paper Series and elsewhere