Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km²with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R² = 0.84–0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5P mission offers nearly global coverage with a fine spectral resolution for reliable SIF retrieval. However, the present satellite-derived SIF datasets are accessible only at coarse spatial resolutions, constraining its applications at fine scales. Here, we utilized a weighted stacking algorithm to generate a high spatial resolution SIF dataset (500 m, 8-day) in China (HCSIF) from 2000 to 2022 from the TROPOMI with a spatial resolution at a nadir of 3.5 km by 5.6–7 km. Our algorithm demonstrated high accuracy on validation datasets (R² = 0.87, RMSE = 0.057 mW/m²/nm/sr). The HCSIF dataset was evaluated against OCO-2 SIF, GOME-2 SIF tower-based measurements of SIF, and gross primary productivity (GPP) from flux towers. We expect this dataset can potentially advance the understanding of fine-scale terrestrial ecological processes, allowing for monitoring of ecosystem biodiversity and precise assessment of crop health, productivity, and stress levels in the long term.

An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R² = 0.95. The root mean square error (RMSE) was 10.86 g/m² and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m² and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R² was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m², and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.