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[Display omitted] • The northwest area of the BTSSCP suffered severe dust-affected zones based on the dust flow trajectory. • The ESs in severe dust-affected areas were relatively poor. • ESs and the dust flow trajectory were integrated to identify ecological governance zones. • Governance suggestions were proposed for the ecological management zone combined with the driving forces. Achieving ecological conservation and governance based on ecosystem services (ESs) can balance regional ecological protection and development, which is of great significance for sustainable development. Existing research has mainly focused on the extraction of priority ecological conservation areas, thereby neglecting to consider the classification and management of ecological engineering areas. To improve regional ecological governance, we combine ESs and dust movement trajectory, using the Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) as the research object to generate watersheds' ecological governance zones. Six ESs were assessed to determine ecological zones. Trajectory simulation of dust movement, using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and potential source contribution function, was used to determined zones affected by dust. By combining these two aspects, five main ecological governance clusters were identified. We found that: (1) the area with low ESs in the northwest of the BTSSCP suffered from severe dust impacts; (2) slight dust-affected zones were mainly distributed in the eastern of the BTSSCP, and in the area, ESs gradually decreased from south to north; (3) ESs in the region was primarily influenced by natural factors and the influencing factors of ESs vary among different zones. These findings will not only contribute to the overall planning and governance of the BTSSCP, but can offer general guidance for the formulation of targeted ecological protection and restoration measures in other areas. [ABSTRACT FROM AUTHOR]

• The RSDI is suitable for monitoring aeolian desertification. • The areas with significant restoration (34.1%) exceeded those with significant deterioration (6%) in the NSPB. • In the areas with significant restoration, 57.4% of the land was mainly affected by human activities. The northern sand-prevention belt (NSPB) is the key area for sand control in China, and the various ecological projects conducted there are important to the Chinese strategy for ecological security. In this paper, a new remote sensing-based desertification index (RSDI) based on principal component analysis was constructed from four aspects of vegetation greenness, surface moisture, soil grain size, and surface radiation. The overall evaluation accuracy of the index was 89.2%, and the kappa coefficient was 0.80, indicating high sensitivity to different degrees of aeolian desertification and suitability for multiscale research. The coefficient of variation, Mann–Kendall test, Theil–Sen median trend analysis, and residual analysis were used to analyze the spatiotemporal changes and driving forces of the RSDI in the NSPB from 2000 to 2020. The RSDI was used to compare aeolian desertification in different subregions, land use types, and ecological project areas. The important results are as follows: (1) the trend of the average RSDI was downward, but it increased significantly in 2008–2009 and 2013–2014; (2) the RSDI was characterized by relatively high volatility in 28.9% and moderate volatility in 27.1% of the area; (3) the areas with significant restoration (34.1%) greatly exceeded those with significant deterioration (6%), whereas 59.9% of the total area was stable; and (4) within the area with significant restoration, 57.4% was primarily affected by human activities, and 42.4% was primarily affected by climate change; however, most of the area with significant deterioration (71.1%) was affected by human activities. In general, the degree of aeolian desertification in the NSPB has decreased in the past 20 years and its ecological quality has continued to recover. However, unreasonable human activities still need to be reduced, and the ecological management of areas under serious threat of desertification needs to be strengthened. [ABSTRACT FROM AUTHOR]

[Display omitted] • A selection strategy for base-period images of spatio-temporal fusion was proposed. • The spatio-temporal inconsistency between field samples and images was reduced. • The estimation accuracy of grassland aboveground biomass was improved. The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China. [ABSTRACT FROM AUTHOR]