An understanding of the spatial variation and influence factors of soil nutrients in mining areas can provide a reference for land reclamation and ecological restoration. Daliuta was used as the study area. The spatial variability of soil nutrients was analyzed using traditional statistics and geostatistics. The effects of topography, mining history, and soil erosion were discussed. The results indicate that the soil pH of the Daliuta mining area is slightly acidic to slightly alkaline, and the soil organic matter, available nitrogen, available phosphorus, and available potassium belonged to the five levels (very low), six (extremely low), five (extremely low), and four (moderately low), respectively. The soil water and salt content indicated that the soil environment in the mining area is arid and has normal levels of salinity. The organic matter, available nitrogen, available phosphorus, available potassium, and soil salt varied moderately, and the pH did not change much, while the soil water varied strongly. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. Soil erosion and mining history are also important factors that lead to the spatial variation of soil nutrients.

Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R²) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

The soil water behavior of sandy soils was studied under semiarid conditions in the Shendong mining area (China). The soil water content (θ) was measured under different depths and topographies using an HH2 moisture meter. The infiltration process was studied using a Guelph soil permeameter. A set of hydrodynamic variables was calculated in the laboratory. The θ of the first 20 cm was the lowest and increased with depth. The content of soil water increased from the top slope to the bottom slope. The infiltration experiments showed that the steady state infiltration rate was >40 mm h⁻¹ in most cases. Owing to the higher contents of sand and soil macropores at the top of the slope and the top 0–20 cm of surface soil, the initial infiltration rate and steady infiltration rate were higher. The average available water capacity was 18.28%, which was consistent with the predominance of a sandy textural fraction. The results of a soil water retention curve and a rainfall simulation experiment showed that there was a low soil water retention capacity throughout the whole profile. This study contributes to the understanding of several aspects of the soil water behavior of sandy soils and provides key information for environmental management and land reclamation under semiarid conditions in the Shendong mining area.

Micro-organisms play important roles in promoting soil ecosystem restoration, but much of the current research has been limited to changes in microbial community structure in general, and little is known regarding the more sensitive and indicative microbial structures or the responses of microbial diversity to environmental change. In this study, based on high-throughput sequencing and molecular ecological network analyses, the structural characteristics of bacterial communities were investigated in response to four different ecological restoration modes in a coal mining subsidence area located in northwest China. The results showed that among soil nutrients, nitrate-nitrogen and fast-acting potassium were the most strongly associated with microbial community structure under different ecological restoration types. Proteobacteria, Actinobacteria, and Acidobacteria were identified as important phyla regarding network connectivity and structural composition. The central natural recovery zone was found to have the smallest network size and low complexity, but high modularity and good microbial community stability. Contrastingly, a highly complex molecular ecological network of soils in the photovoltaic economic zone existed beneath the photovoltaic modules, although no key species, strong bacterial competition, poor resistance to disturbance, and a significant increase in the relative abundance of Gemmatimonadetes were found. Furthermore, the reclamation zone had the highest soil nutrient content, the most complex network structure, and the most key and indicator species; however, the ecological network was less stable and readily disturbed.

Underground reservoir technology can mitigate water shortage and pollution problems in water shortage coal mining areas and has a good application prospect. While still a new technology, the theory and method of underground reservoirs need to be improved. This research focused on the hydrochemical characteristics of mine water and their significance for the site selection of underground reservoirs. With the Shendong coal mining area as a case study, the hydrochemical major ions, toxicological indexes, and stable isotopes of hydrogen and oxygen were tested for the mine water samples, and the water quality was quantitatively evaluated and the origins of over-limit variables were investigated by hydrogeochemical numerical simulation and ionic ratio analysis. The influencing factors of water quality were analyzed and the significance of mine water quality for the site selection of underground reservoirs was discussed. The results show that the main over-standard variables are Na⁺, F⁻, SO₄²⁻, TDS, and sodium ion adsorption ratio (SAR), and a strong positive correlation exists between F⁻ and SAR and a negative correlation exists between F⁻ and Ca²⁺. Na⁺ in mine water originates from the dissolution of halite and silicate rocks, as well as reverse cation exchange. F⁻ originates from reverse cation exchange and the displacement between OH⁻ in alkaline water and F⁻ adsorbed on the surface of minerals. On the whole, the mine water quality is better on the east than on the west of the WL River. The water–rock interactions in goaf increase the concentrations of F⁻ and Ca²⁺ and SAR. The areas where the mine water samples have low concentrations of Na⁺, F⁻, and low SAR values, such as the shallow coal seams at the SGT, DLT, and WL mines, are favorable sites for the underground reservoir. The outcomes may benefit the reasonable site selection of underground reservoirs in similar coal mining areas with water shortage.