Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R² = 0.84, p < 0.01), EOS (R² = 0.72, p < 0.01), and LOS (R² = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management.

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

The widely spread alpine grassland ecosystem in the Three River Headwaters Region (TRHR) plays an essential ecological role in carbon sequestration and soil and water conservation. In this study, we test the latest high spatial resolution hyperspectral (Zhuhai-1 OHS) remote sensing imagery to examine different alpine grassland coverage levels using Multiple Endmember Spectral Mixture Analysis (MESMA). Our results suggest that the 3-endmember (3-EM) MESMA model can provide the highest image pixel unmixing percentage, with a percentage exceeding 97% and 96% for pixel scale and landscape scale, respectively. The overall accuracy shows that Zhuhai-1 OHS imagery obtained the highest overall accuracy (83.7%, k = 0.77) in the landscape scale, but in the pixel scale, it is not as good as Landsat 8 OLI imagery. Overall, we can conclude that the hyperspectral imagery combined 3-EM MESMA model performs better in both pixel scale and landscape scale alpine grassland coverage mapping, while the multispectral imagery with the 3-EM MESMA model can satisfy requirements of alpine grassland coverage mapping at the pixel scale. The approaches and workflow to mapping alpine grassland in this study can help monitor alpine grassland degradation; not only in the Qinghai–Tibetan Plateau (QTP), but also in other grassland ecosystems.

Noxious weeds (NWs) are increasingly recognized as a significant threat to the native alpine grassland ecosystems of the Qinghai–Tibetan Plateau (QTP). However, large-scale quantification of their continuous fractional cover remains challenging. This study proposes a pixel-level estimation framework utilizing time-series Sentinel-2 imagery. A Dynamic Mask Non-Stationary Transformer (DMNST) model was developed and trained using multi-temporal multispectral data to map the spatial distribution of NWs in the Three-River Headwaters Region. The model was calibrated and validated using field data collected from 170 plots (1530 quadrats). The results demonstrated that both the dynamic masking module and the non-stationary normalization significantly enhanced the prediction accuracy and robustness, particularly when applied jointly. The model performance varied across different combinations of spectral bands and temporal inputs, with the optimal configurations achieving a test R² of 0.770, MSE of 0.009, and RMSE of 0.096. These findings underscore the critical role of the input configuration and architectural enhancements in accurately modeling the fractional cover of NWs. This study confirms the applicability of Sentinel-2 time-series imagery for modeling the continuous fractional cover of NWs and provides a scalable tool for invasive species monitoring and ecological risk assessment in alpine ecosystems.