A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Accumulated carbon storage is a crucial indicator for assessing the health of mangrove ecosystems and can suitably reflect the changes in the carbon sequestration capacity of mangrove forests over time. Unlike carbon stock for a specific year, accumulated carbon storage measures the capacity for continuous carbon sequestration in mangroves; however, spatially explicit datasets and maps on mangrove accumulated carbon storage are yet lacking on a global scale. This study pioneered the development of a global gridded dataset of mangrove accumulated carbon storage (2000–2020) at a 1 km resolution, by utilizing the most recent high-precision mangrove distribution data from the Global Mangrove Watch. This dataset captures the spatiotemporal heterogeneity of mangrove accumulated carbon storage and pinpoints hotspots of accumulated carbon stock changes at both global and regional levels. The outcomes can help identify areas requiring protection and restoration efforts, as well as prioritize policy interventions, thereby promoting the sustainable management of mangrove ecosystems worldwide.

Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China’s crop patterns and can serve as critical input for biogeochemical and agricultural models.

The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that the production losses might not be as severe as previous estimates. By utilizing the adaptive threshold segmentation method to extract abandoned cropland from the Sentinel-2 high-resolution imagery and calibrating the spatial production allocation model’s gridded crop production data from Ukraine’s statistical data, this study explicitly evaluated Ukraine’s crop-specific production losses and the spatial heterogeneity. The results demonstrated that the estimated area of abandoned cropland in Ukraine ranges from 2.34 to 2.40 million hectares, constituting 7.14% to 7.30% of the total cropland. In Ukrainian-controlled zones, this area spans 1.44 to 1.48 million hectares, whereas in Russian-occupied areas, it varies from 0.90 to 0.92 million hectares. Additionally, the total production losses for wheat, maize, barley, and sunflower amount to 1.92, 1.67, 0.70, and 0.99 million tons, respectively, with corresponding loss ratios of 9.10%, 7.48%, 9.54%, and 8.67%. Furthermore, production losses of wheat, barley, and sunflower emerged in both the eastern and southern states adjacent to the conflict frontlines, while maize losses were concentrated in the western states. The findings imply that Ukraine ought to streamline the food transportation channels and maintain stable agricultural activities in regions with high crop production.