China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces.

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e., the number of livestock per unit area). This study utilized census livestock data and a satellite-based vegetation index to develop the first Long-term High-resolution Grazing Intensity (LHGI) dataset of grassland in seven pastoral provinces in western China from 1980 to 2022. The LHGI dataset effectively captured spatial variations in grazing intensity, with validation at 73 sites showing a correlation coefficient (R²) of 0.78. The county-level validation showed an averaged R² values of 0.73 ± 0.03 from 1980 to 2022. This dataset serves as a vital resource for estimating grassland carbon cycling and livestock system CH₄ emissions, as well as contributing to grassland management.

Winter wheat is a major staple food grown worldwide. China contributes about 19% of the winter wheat production in the world. Therefore, quickly and accurately acquiring winter wheat distribution in China over long time periods is critical to achieve grain safety and understand the spatiotemporal patterns of winter wheat. However, there is a lack of widely available, high spatial resolution, long-term, and large-scale remotely sensed maps on winter wheat cultivation in China. In this study, we utilized a phenology-based algorithm to identify winter wheat location. The algorithm integrated three key growing features of winter wheat into one variable to calculate the planting probability of winter wheat. Here, we produced a long-term winter wheat map dataset at 30-m resolution for China from 2001 to 2020 using the fusion dataset. The resultant maps achieved high accuracy based on field survey samples, with user’s, producer’s, and overall accuracies of 91.17%, 90.92%, and 91.6%, respectively. Furthermore, the mapped winter wheat area aggregated to municipal- and county-levels exhibited good correlations with agricultural statistical data.

The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.

Restricted by the design of satellite sensors, the existing satellite-based normalized difference vegetation index (NDVI) cannot simultaneously have a high temporal resolution and spatial resolution, which substantially limits its applications. In recent years, several spatiotemporal fusion models have been developed to produce vegetation index datasets with both high spatial and temporal resolutions, but large uncertainties remain. This study proposes a spatiotemporal fusion model [i.e., Integrating ENvironmental VarIable spatiotemporal fusion (InENVI) model] based on a machine-learning method by incorporating environmental variables to reconstruct NDVI data. Over 14 study areas covering various vegetation types globally, the InENVI method was validated for reproducing spatiotemporal variations in NDVI. On average, the determining coefficients ( $R^{2}$ ) of the reconstructed NDVI compared with satellite-based NDVI observations were above 0.90, reflecting the spatiotemporal variations over all study sites. In addition, we compared the performance of the InENVI model with seven other fusion models over two cropland areas with high vegetation heterogeneity. The results showed that the newly developed InENVI method had the best performance, and the reconstruction error of the InENVI method decreased about 23.68%–59.63% on average over two study areas compared to the other seven methods. Our analyses also highlighted that the integration of environmental variables into spatiotemporal fusion is necessary to improve reconstruction accuracy. The InENVI model provides an alternative approach for reconstructing NDVI datasets with both high spatial and temporal resolutions over large areas.

Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China. The Hi-GLASS GPP v1 dataset is generated from 30-m Landsat data using a process based light use efficiency model. The Hi-GLASS GPP v1 model integrates a detailed map of maize plantations, a crucial C4 crop in China known for its higher photosynthetic efficiency compared to C3 crops. This inclusion helps correct the underestimation of GPP that typically occurs when all croplands are categorized as C3. The Hi-GLASS GPP v1 dataset demonstrates a robust correlation with GPP data derived from eddy covariance towers, thereby enabling a more accurate assessment of terrestrial carbon sequestration across China.