The quality of global cropland products could affect our understanding of the impacts of cropland reclamation on global changes. With the advancement of remote sensing technology, several global land cover products and synergistic datasets have been developed in recent decades. However, there are still some disagreements among the global cropland datasets. In this paper, we proposed a new synergistic method that integrates the reliability of spatial distribution and cropland fraction on a pixel scale, and developed a modern (around 2000 C.E.) fractional cropland dataset with a 1 km × 1 km spatial resolution on the basis of the spatial consistency of cropland reclamation intensity derived from multi-sets of global land cover products. The main conclusions are shown as follows: (1) The accuracy of spatial distribution assessed by validation samples in this synergistic dataset reaches 87.6%, and the dataset also has a moderate amount of cropland pixels when compared with other products. (2) The reliability of cropland fraction on the pixel scale had been highly improved, and most cropland pixel has a higher fraction (over 90%) in this dataset. The “L” shape of the histogram of pixel numbers with different reclamation intensities is reasonable because it is consistent with the up-scaling results derived from satellite-derived products with high spatial resolutions and the expert knowledge on cultivation. (3) The cropland areas in this non-calibrated result are generally closer to that of FAOSTAT on scales from global to national when compared to other non-calibrated synergistic datasets and original satellite-derived products. (4) The reliability of the synergistic result developed by this method might be decreased to some degree in the regions with high discrepancies among the original multi-sets of cropland datasets.

Modern global cropland products have been widely used to assess the impact of land use and cover change (LUCC) on carbon budgets, climate change, terrestrial ecosystems, etc. However, each product has its own uncertainty, and inconsistencies exist among different products. Understanding the reliability of these datasets is essential for knowing the uncertainties that exist in the study of global change impact forced by cropland reclamation. In this paper, we propose a set of coincidence assessments to identify where reliable cropland distribution is by overlaying ten widely used global land cover/cropland datasets around 2000 AD. A quantitative assessment for different spatial units is also performed. We further discuss the spatial distribution characteristics of different coincidence degrees and explain the reasons. The results show that the high-coincidence proportion is only 40.5% around the world, and the moderate-coincidence and low-coincidence proportion is 18.4% and 41.1%, respectively. The coincidence degrees among different continents and countries have large discrepancies. The coincidence is relatively higher in Europe, South Asia and North America, while it is very poor in Latin America and Africa. The spatial distribution of high and moderate coincidence roughly corresponds to the regions with suitable agricultural conditions and intensive reclamation. In addition to the random factors such as the product’s quality and the year it represented, the low coincidence is mainly caused by the inconsistent land cover classification systems and the recognition capability of cropland pixels with low fractions in different products.