The accurate estimation of a regional ecosystem’s carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 10⁹ t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase.

Accurately understanding the distribution and changing trends of Center Pivot Irrigation (CPI) farmland in the Mu Us region and exploring the impact of CPI farmland construction on sandy land vegetation growth hold significant importance for local sustainable development. By using Landsat images to extract CPI farmland information and applying buffer zone analysis to explore the impact of CPI farmland construction on the surrounding vegetation growth, the results revealed the following key findings: (1) The number and area of CPI farmland units showed a continuous growth trend from 2008 to 2022. Spatially, Etoke Front Banner was the focal point of the CPI farmland unit construction, gradually expanding outward. In terms of scale, small-scale CPI farmland units (0–0.2 km²) dominated, while large-scale CPI farmland units (>0.4 km²) were primarily distributed in Yulin City (Mu Us). (2) The growth rate of CPI farmland units in Yulin City gradually slowed down, while that in Ordos City (Mu Us) continued to exhibit a high growth trend. Affected by water-resource pressure and policies, CPI farmland units in Ordos City may continue to increase in the future, while they may stop growing or even show a downward trend in Yulin City. (3) CPI farmland mainly came from the conversion of cultivated land, but over time, more and more grassland was reclaimed as CPI farmland. The absence of cover planting after crop harvesting and the lack of shelterbelt construction may exacerbate land desertification in the region. (4) Within the typical region, CPI farmland unit construction promoted vegetation growth within the CPI units and the 500 m buffer zone but had an inhibitory effect on vegetation growth within the 500–3000 m buffer zone and no significant effect on vegetation growth within the 3000–5000 m buffer zone. (5) The decrease in groundwater reserves caused by CPI farmland unit construction was the primary reason for inhibiting the vegetation growth within the 500–3000 m buffer zone of CPI farmland units in the Mu Us region. This study can provide a scientific basis for the sustainable development of CPI farmland in semi-arid areas.

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 10⁵ hm², with the predominant decline occurring in the 0–1 hm² and 1–3 hm² size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors.

Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for regional ecosystems based on disturbances. Focusing on the lower reaches of the Yellow River Basin (LR-YRB), we integrated the remote sensing ecological index (RSEI) with texture structural parameters, and applied the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to analyze the continuous changes in disturbances and recovery from 1986 to 2021, facilitating the quantification and evaluation of resistance, resilience, and temporal stability. The results showed that 72.27% of the pixels experienced 1–9 disturbances, indicating the region’s sensitivity to external factors. The maximum disturbances primarily lasted 2–3 years, with resistance and resilience displaying inverse spatial patterns. Over the 35-year period, 61.01% of the pixels exhibited moderate temporal stability. Approximately 59.83% of the pixels recovered or improved upon returning to pre-disturbance conditions after maximum disturbances, suggesting a strong recovery capability. The correlation among stability dimensions was low and influenced by disturbance intensity, underscoring the necessity for a multidimensional assessment of regional ecosystem stability based on satellite remote sensing.