China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and the construction land expansion in Liulin County should be restricted, and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality.

The soil organic carbon (SOC) pool in farmland is changing rapidly due to human activities, thereby greatly affecting the regional and global environment, as well as influencing soil fertility and crop yields. The present study investigated the effects of underground coal mining on the regional SOC pool in farmland in the Jiuli Mining Area of Xuzhou City in China as a typical coal mining region based on field sampling, chemical analysis, model construction, and spatial analysis using the software of ArcGIS. The results showed that in the mining subsidence area, spatial variations in the SOC content and soil bulk density were mainly caused by structural factors (mining subsidence, subsidence waterlogging, and other structural factors due to coal mining) at a regional scale. SOC storage in farmland soil decreased sharply in non-waterlogged subsidence farmland and seasonally waterlogged subsidence farmland in the areas with mining, whereas the SOC storage increased in waterlogged wetland after coal mining. The SOC was reduced by 102,882 tonnes (32.81%) compared with the original SOC stock as a consequence of coal mining, and thus the effect of underground coal mining on the regional SOC pool in farmland was characterized as a carbon loss process. Land-use changes, soil degradation and erosion contributed almost equally to the carbon loss process in the study area. The results of this study may facilitate evaluations of low-carbon land reclamation and ecological compensation in mining areas.

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

Soil water content is an important variable in hydrology and many related disciplines. It affects runoff from precipitation, groundwater recharge, and evapotranspiration. This research used the coal mining area of the Changhe River Basin in the Loess Plateau as a study and using SAR (Synthetic Aperture Radar) data, the surface soil water in 24 days (From Jan 25, 2018 to Dec 10, 2019) was estimated using a radar signal change detection algorithm. The temporal and spatial variation characteristics of surface soil water inside and outside the disturbed area were compared and analyzed. An empirical orthogonal function (EOF) analysis method was used to analyze the potential temporal and spatial variation of surface soil water, and to detect the regional soil water variation under coal mining disturbances to better understand the different potential modes of spatial variation of soil water in the unobserved time. The results showed that the average surface soil water content in the study area changed with season, showing a dry-wet-dry variation. Moreover, it was significantly affected by precipitation factors, and its response to precipitation had a hysteresis effect. From the perspective of spatial variation, the influence of coal mining disturbance on surface soil moisture was not obvious. From the perspective of time series change, moving from wet to dry conditions, the soil in the disturbed area dried faster than the soil in the undisturbed area after soil wetted. When moving from drying to wetting, the soil in the disturbed area was quickly wetted. The EOF analysis showed that most observed spatial variability of soil moisture was stable in time. The study was conducted in a disturbed area and an undisturbed area for single EOF analysis, and the results showed that the EOF mode of the disturbed area was closer to that of the whole study area. By comparing the two subregions and the entire study area, it was found that the changes of correlation values were related to soil texture, bulk density, altitude and slope, indicating that the soil texture of the two subregions may be different at different elevations, and may also be related to the change of the original soil structure in the disturbed area. Overall, the EOF mode of the disturbed area determined the EOF mode of the entire study area.