Accurate assessment of cropland maps is crucial for ensuring food security, effective agricultural management, and environmental monitoring. With the widespread application of high-resolution (≤10 m) remote sensing imagery and the advancement of machine learning techniques, numerous high-resolution cropland maps have been developed. However, comprehensive evaluations of their accuracy remain limited. We utilized 163,861 validation samples and national land survey statistical data to conduct a multi-scale comparison of the accuracy of seven cropland maps (one 1 m and six 10 m maps) in China. Additionally, five synergistic strategies were employed to generate more accurate fused cropland maps. Validation results showed that the overall accuracy (OA) of the seven maps ranged from 0.79 to 0.91, with ESA-WorldCover (ESA-WC) exhibiting the highest OA, followed by AI Earth China land cover classification dataset (AIEC), ESRI Land Cover (ESRI-LC), and Cropland Use Intensity in China (China-CUI), while Sino-LC1 showed the lowest performance. Spatially, ESA-WC achieved the highest accuracy in nearly 60% of provinces, followed by AIEC and ESRI-LC, each accounting for approximately 20%. AIEC performed best in western provinces, whereas ESRI-LC dominated in the middle and lower reaches of the Yangtze River. Area consistency assessments revealed that, on average, the seven maps overestimated cropland areas by 20% compared to statistical data. Among these, ESA-WC showed the highest proportion of provinces with relative errors within ±20%, but this proportion was only 50%. Moreover, the OA of the fused maps exceeded 0.92, with county-level R² values compared to statistical data reaching 0.98, significantly improving the reliability of cropland products in over 60% of provincial administrative regions. Based on these results, effective synergistic strategies for high-resolution cropland mapping are proposed.

Gross Domestic Product (GDP) serves as a core indicator for measuring the sustainable economic development of countries and regions. Accurate understanding of its spatio-temporal distribution is crucial for achieving the United Nations Sustainable Development Goals (SDGs). However, current grid-based GDP data for China’s regions predominantly consists of data from specific years, making it difficult to capture fine-grained changes in economic development. To address this, this study proposes a spatial GDP framework integrating cross-scale feature extraction (CSFs) with stacked ensemble learning. Based on China’s county-level GDP statistics and multi-source auxiliary data, it first generates a density-weighted estimation layer. This is then processed through dasymetric mapping to produce China’s Annual High-Resolution Gridded GDP Dataset (CA_GDP) from 2000 to 2021. Evaluation demonstrates the framework’s superior performance in density weight estimation, achieving an R² of 0.82 against statistical data. Compared to traditional single models like Random Forests (RF), it improves R² by 13–54%, reduces mean absolute error (MAE) by 2–26%, and lowers root mean square error (RMSE) by 19–39%, with these advantages remaining stable across time series. The dasymetric mapping of the CA_GDP dataset clearly depicts the economic development patterns and urban agglomeration effects in the southeastern coastal regions, as well as the relatively lagging economic development in western areas. Compared to existing public datasets, CA_GDP offers significant advantages in reflecting the fine-grained economic spatial structure within county-level units, providing a more reliable data foundation for identifying regional economic disparities, policy formulation and evaluation, and related research.

Accurately quantifying the sensitivity of alpine vegetation to climate change is a key prerequisite for formulating regional climate change adaptation policies. The sensitivity of the fragile alpine grasslands on the Tibetan Plateau to climate change has received widespread attention. However, the spatiotemporal dynamics and driving mechanisms of this sensitivity are still unclear under continuous warming and wetting. This study, based on MODIS_NDVI and meteorological data from 2000 to 2023, constructed a dynamic Vegetation Sensitivity Index (VSI) framework and integrated Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with Shapley Additive exPlanations (SHAP) attribution analysis to reveal the spatiotemporal evolution characteristics and driving mechanisms of vegetation sensitivity on the Tibetan Plateau. The results show that (1) the VSI of alpine grasslands exhibited a spatial pattern of higher values in the southwest and lower values in the northeast, with an overall upward trend. Specifically, 56.31% of the region showed an increase in the VSI, with the upward trend being more pronounced in the northern plateau. (2) The dominant role of different climate factors varied regionally; vegetation sensitivity to precipitation increased in the northern plateau, and temperature sensitivity decreased in the central plateau, while sensitivity to solar radiation significantly increased in the central plateau. (3) SHAP attribution analysis indicated that elevation was the core factor driving VSI differentiation, showing a higher sensitivity at higher elevations, with lower growth rates. These findings reveal the dynamic evolution of vegetation sensitivity under the warming and wetting climate trend and its elevation-regulated mechanism, providing important scientific insights for regional ecological adaptation management.

China’s age structure is undergoing profound demographic shifts, making accurate spatial information on age-stratified populations essential for policy-making, resource allocation, and risk assessment. However, census data are primarily aggregated by administrative units, offering coarse spatial resolution that constrains their integration and application with other gridded datasets. Using township-level population counts for four age groups (0–14, 15–59, 60–64, and ≥65 years) from the 2020 Seventh National Population Census across 38,572 townships, we developed an age-stratified downscaling framework. This framework integrates a random forest model with age-filtered Points of Interest (POI) data and other multi-source geospatial covariates to generate a 100 m resolution age-stratified population density weighting layer. Through township-level data dasymetric mapping, we produced the township-based 100 m Age-Stratified Population Grid Data (Township-ASPOP). Since township-level data represent the finest publicly available spatial unit of demographic statistics in China, we further validated the accuracy of Township-ASPOP by generating County-based 100 m Age-Stratified Population Grid Data (County-ASPOP) through dasymetric mapping using county-level age-stratified population data. The results demonstrate that County-ASPOP achieves superior predictive accuracy, with R² values of 0.95, 0.95, 0.85, and 0.86, and Root Mean Square Error (RMSE) values of 1743, 6829, 900, and 2033 persons per township for the four age groups, respectively—significantly outperforming the contemporaneous WorldPop dataset (R² = 0.69, 0.72, 0.64, and 0.60). The accuracy of Township-ASPOP is no less than that of County-ASPOP and effectively captures realistic spatial settlement patterns. This study establishes a reproducible framework for generating age-stratified population grid data and provides critical data support for policy formulation and resource allocation.