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Landscape in the middle and lower reaches of the Yellow River in China has undergone significant changes for thousands of years due to agricultural expansion. Lack of reliable long-term and high-resolution historical cropland data has limited our ability in understanding and quantifying human impacts on regional climate change, carbon and water cycles. In this study, we used a data-driven modeling framework that combined multiple sources of data (historical provincial cropland area, historical coastlines, and satellite data-based maximum cropland extent) with a new gridding allocation model for croplands distribution to reconstruct a historical cropland dataset for the middle and lower reaches of the Yellow River at a 10-km resolution for 58 time points ranging from the period 1000 to 1999. The cropland area in the study area increased by 2.3 times from 21.87 million ha in 1000 to 50.64 million ha in 1999. Before 1393, the area of cropland increased slowly and was primarily concentrated in the Weihe and Fenhe plains. From 1393–1820, the area of cropland increased rapidly, particularly on the North China Plain. Since 1820, cropland cover has tended to become saturated. Our newly reconstructed results agreed well with remotely sensed data as well as historical document-based facts regarding cropland distribution. [ABSTRACT FROM AUTHOR]

Quantifying long-term and large-scale changes in cropland cover is critical for evaluating the intensity of past human activity, climate change, biodiversity loss, food security, and carbon and water cycles. This study used multiple data sources, i.e., historical reconstruction, remote sensing, statistics, and their derived products, and a combined approach for quantitative reconstruction and gridding allocation to create the Chinese Historical Cropland Dataset+ (CHCD+). Our findings showed a substantial gross change of 30.82 × 107 ha in cropland cover, equivalent to 32 % of China's land area. This represents a 3.2-fold increase compared to the net change. Such a large-scale agricultural land cover change has not been identified previously. Traditional agricultural areas, predominantly in the Yellow River and Yangtze River basins, have established a fundamental pattern for the historical development of agriculture in China. The proportion of croplands in this region consistently exceeded 70 %, reaching 90 % before the mid-Qing dynasty. We identified a two-phase pattern of historical cropland cover changes in China. Before the Qing Dynasty, there was slow growth and a substantial change in the cropland area. This has been constrained by geographical conditions, tax systems, and population size, with the gross change being six times the net change. Since the Qing Dynasty, a rapid growth and expansion phase has been observed in cropland areas, which can be explained by tax system reforms, population expansion, and the introduction of cold- and drought-resistant crops. • Cropland areas in China exhibited a prevailing expansion trend throughout the last millennium. • China's cropland experienced a gross change 3.2 times greater than the net change in the millennium. • Pre-Qing Dynasty, traditional agriculture region covered 42 % of national land, exceeding 90 % in cropland proportion. • Land policy and population size shaped cropland dynamics, imposing constraints or acting as drivers. [ABSTRACT FROM AUTHOR]

To improve the accuracy of historical cropland data, we reconstructed cropland cover in the northern China's farming–pastoral zone during the Liao dynasty using historical literature and settlement relics. We first reconstructed the total cropland area using historical household data based on the cropland area per household. Next, we allocated the cropland area into 5′ grid cells weighted by settlement density to generate a cropland data. Our main findings were as follows: (1) Data on settlement relics enabled not only the identification of ancient farming areas but also the allocation of cropland cover within high resolution grids in the study area. (2) In the flourishing period of the Liao dynasty, the total cropland area was 0.39 × 104 km2. The cropland grids comprised 28.30% of the total grids, with average and maximum values of cropland fractions of 6.61% and 31.18%, respectively. (3) Compared with our result, we found that the PJ overestimate the cropland area of the study area in 1100 AD, whereas the HYDE 3.2 underestimate it. The area of anthropogenic land use in KK 10 was larger than the cropland area in this study too. None of the three global datasets revealed the mosaic spatial distribution pattern of cropland cover in this study area. (4) The orders of land suitability for cultivation during the Liao dynasty and the present period were almost the identical, even in the farming–pastoral zone of northern China, which has fragile ecological environment. [ABSTRACT FROM AUTHOR]

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.

To understand historical human-induced land use/cover change (LUCC) and its climatic effects, it is essential to reconstruct historical land use/cover changes with explicit spatial information. In this study, based on the historically documented cropland area at county level, we reconstructed the spatially explicit cropland distribution at a cell size of 1 km × 1 km for the Songnen Plain in the late Qing Dynasty (1908 AD). The reconstructions were carried out using two methods. One method (hereafter, referred to as method I) allocated the cropland to cells ordered from a high agricultural suitability index (ASI) to a low ASI, but they were all within the domain of potential cropland area. The potential cropland area was created by excluding natural woodland, swamp, water bodies, and mountains from the study area. The other method (hereafter, method II) allocated the cropland to cells in the order from high ASI to low ASI within the domain of cropland area in 1959. This method was based on the hypothesis that the cropland area domain in 1959 resulted from enlargement of the cropland area domain in 1908. We then compared these two reconstructions. We found that the cropland distributions reconstructed by the two methods exhibit a similar spatial distribution pattern. Both reconstructions show that the cropland was mostly found in the southern and eastern parts of the Songnen Plain. The two reconstructions matched each other for about 68% of the total cropland area. By spatially comparing the unmatched cropland cells of the two reconstructions with the settlements for each county, we found that unmatched cropland cells from method I are closer to settlements than those from method II. This finding suggests that reconstruction using method I may have less bias than reconstruction with method II. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no conflict of interest.

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. [ABSTRACT FROM AUTHOR]