Abstract. Soybean, an essential food crop, has witnessed a steady rise in demand in recent years. There is a lack of high-resolution annual maps depicting soybean planting areas in China, despite China being the world’s largest consumer and fourth largest producer of soybeans. To address this gap, we developed a novel method called phenological- and pixel-based soybean area mapping (PPS) based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select the distinct features that differentiate soybeans most effectively from other crops across various regions. These features were then input for an unsupervised classifier (K-means), and the most likely type was determined by a post-classification method based on dynamic time warping (DTW). For the first time, we generated a dataset of soybean planting areas across China, with a high spatial resolution of 10 meters, spanning from 2017 to 2021 (ChinaSoyArea10m). The R2 values between the mapping results and the census data at both county- and prefecture-level were consistently around 0.85 in 2017–2020. Moreover, the overall accuracy of mapping results at the field level in 2017, 2018, and 2019 were 77 %, 84 % and 88 %, respectively. Compared with the existing 10-m crop-type maps in Northeast China (Cropland Data Layer, CDL) based on field samples and supervised classification methods, the mapping accuracy is significantly improved by 31 % (R2 increases from 0.53 to 0.84) according to their consistency with census data, particularly at the county level. ChinaSoyArea10m is spatially consistent well with the two existing datasets (CDL and GLAD maize-soybean map). ChinaSoyArea10m provides important information for sustainable soybean production and management, as well as agricultural system modeling and optimization. ChinaSoyArea10m can be downloaded from an open-data repository (DOI:https://zenodo.org/doi/10.5281/zenodo.10071426, Mei et al., 2023).
ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type soil-depth). This dataset is maize0-10. *** The data file is in “.tif" format *** Spatial extent: maize drylands across China *** Soil depth: 0-10cm *** Temporal Resolution: Daily *** Pixel size: 1000 m *** Projection information: GCS_WGS_1984 *** Filename convention: Year_DOY.tif *** Year: Values from 1993 to 2018
Our understanding of the structure and function of China's terrestrial ecosystems, particularly their responses to transient climate change on timescales of decades to centuries, can be further improved by dynamic vegetation models that include vegetation dynamics as well as biogeochemical processes. Here, the Lund‐Potsdam‐Jena dynamic global vegetation model was calibrated to reasonably capture the general patterns of vegetation distribution across China's terrestrial ecosystems. New parameter values were used for bioclimatic limits, and the model was validated against satellite, in situ, and inventory data for net primary production (NPP), leaf area index, and carbon storage simulations. Dynamic responses of China's terrestrial ecosystems to rising atmospheric CO 2 concentration ([CO 2 ]) and climate change in the 20th and 21st centuries were then simulated. Simulations were driven by eight climate scenarios consisting of combinations of two general circulation models and four emission scenarios from Intergovernmental Panel on Climate Change Special Report Emission Scenarios ranging from low emission to fossil intensive emission. We derived possible temporal and spatial change patterns of vegetation distribution, carbon flux, and carbon pools across China. Simulations showed that regional changes in temperature and precipitation would cause substantial vegetation changes in northern, northeastern, and western China. Such vegetation dynamics, and associated mechanisms such as responses of NPP and heterotrophic respiration to rising [CO 2 ] and regional climate change, would lead to corresponding changes in temporal and spatial patterns of carbon flux and carbon pools. As a result, some regional ecosystems could switch from carbon sinks to carbon sources or vice versa by the end of the 21st century. China's terrestrial ecosystems have an average carbon uptake of 0.12 to 0.20 Gt C yr −1 over the period of 1981–2100; however, the rate of increase in carbon sinks is projected to decrease after about 2040, particularly under the fossil intensive emission scenario.
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