The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km² using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R²) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m⁻², and a bias equal to −1.6 g C m⁻² between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R² = 0.74, RMSE = 57.8 g C m⁻², and bias = −0.1 g C m⁻²) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m⁻² (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

Vegetation is a crucial and intuitive index that can be used to evaluate the ecological status. Since the 20th century, land use has changed significantly in Yellow River Basin (YRB), along with great changes of vegetation, serious soil erosion, and gradual ecological deterioration. To improve the ecological environment in the YRB, China has carried out a series of ecological protection projects since the 1970s. Therefore, long-term sequence monitoring of vegetation in YRB is necessary to show the conservation effect and better support the further protection and restoration. This study analysed vegetation changes from 2000 to 2019 based on an annual mean fractional vegetation cover (FVC) dataset. The Theil–Sen median trend analysis method was used to analyse trends in FVC. The results showed that the vegetation in the YRB has improved significantly, with an average annual growth rate of 0.65%, and the ‘green line’ of vegetation has moved approximately 300 km westward. The influence of climate on vegetation is essential; therefore, this study also analysed the influence of temperature and precipitation on vegetation over time and space. Ecological control and afforestation are important anthropogenic factors that affect vegetation. The growth trend (0.6%/a) in key ecological function regions (KEFRs) was the fastest, and even though the protection measures are not strict, they provide space for afforestation. The China Ecological Conservation Red Line (CECRL) and the national nature reserves (NNRs) showed relatively flat trends. Ecological afforestation projects were closely correlated with the growth trend of the FVC. The correlation between FVC and the intensity of ecological engineering was significant in typical areas.

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo” approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.

Exploring the driving factors of changing ecosystem services is critical for supply capacity maintaining and ecological management zoning. Xinjiang of Northwest China, is considered one of the most fragile ecological environment areas. However, studies on how ecosystem services’ driving forces respond to the environmental conditions of Xinjiang are still insufficient, especially in sub–regions with considerable spatial heterogeneity. Based on 106 counties across Xinjiang, we employed models of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and Revised Wind Erosion Equation (RWEQ) to quantify four essential ecosystem services (carbon storage, habitat quality, and sand fixation and water yield). Then, we investigated the spatial distribution of four ecosystem services and drivers at the county scale in 2020 by using multi–scale geographically weighted regression (MGWR). The results showed that the spatial distribution of ecosystem services is higher in the north and lower in the south, and hotspots and high–value ecosystem services areas were consistent. Precipitation, temperature, and fractional vegetation cover were the dominant factors influencing the four ecosystem services. Therefore, regulating climate and increasing vegetation will maximize the improvement of regional ecosystem services in Xinjiang. Significant differences exist in the counties of the type, intensity, and direction of ecosystem services drivers. The correlation between carbon storage, habitat quality and fractional vegetation cover was more robust stronger in the south. Water yield was more closely related to fractional vegetation cover in southern Xinjiang. Under different ecological and social conditions, the impact of driving forces on ecosystem services showed different changing trends. Three suggestions for improving ecosystem services management were proposed based on our results. The comparative analysis of the driving factors of county ecosystem services in this study will help to formulate differentiated ecological protection policies and promote a sustainable supply of ecosystem services in Xinjiang. In the future, it is necessary to strengthen the long-term monitoring and evaluation of ecosystem services and the research on the interaction of multiple drivers.

In recent years, China has made remarkable progress in infrastructure construction, which has greatly contributed to the development of the regional economy. However, the impacts of construction on the ecological environment are of increasing concern. This study aimed to quantitatively assess the ecological environment of two expressways (the Chanliu Expressway and the Linghua Expressway) constructed during different time periods, to assess the impact of road construction on the ecosystem and the effectiveness of the Chinese government’s efforts in environmental protection. The pressure–state–response (PSR) model was adopted, which integrates a variety of remote sensing indicators. The ecological pressure, ecological state, and ecological response in the pre-, mid-, and post-construction periods of the road were assessed. The results reveal that the impacts of the construction of the Chanliu (1999–2002) and Linghua Expressways (2019–2023) on ecosystems are different. For the Chanliu Expressway, the ecological pressure continually increased, and the ecological state significantly declined during the construction period. When the road construction was finished, the environment continuously deteriorated. This was due to the lack of effective ecological protective measures during its construction. In contrast, the Linghua Expressway experienced reduced ecological pressure during the construction period, with the ecological state remaining relatively stable, as more protective measures were implemented. However, it later relied on natural recovery, which led to an increase in ecological pressure in the post-construction period. The results indicate that China’s ecological protective measures in road construction have achieved significant progress in recent years. In the future, it is essential to maintain long-term ecological health by strengthening ecological restoration management and continuous environmental monitoring.