Land use change is an important factor in atmospheric carbon emissions. Most of the existing studies focus on modeling the land use pattern for a certain period of time in the future and calculating and analyzing carbon emissions. However, few studies have optimized the spatial pattern of land use from the perspective of the impact of carbon emission constraints on land use structure. Therefore, in this study, the effects of land use change on carbon emissions from 1990 to 2020 were modeled using a carbon flow model for Sanmenxia, Henan, China, as an example. Then, the land use carbon emission function under the low carbon target was constructed, and the differential evolution (DE) algorithm was used to obtain the optimized land use quantity structure. Finally, the PLUS model was used to predict the optimal spatial configuration of land use patterns to minimize carbon emissions. The study produced three major results. (1) From 1990 to 2020, the structural change of land use in Sanmenxia mainly occurred between cultivated land, forest land, grassland and construction land. During this period of land use change, the carbon emissions from construction land first increased and then decreased, but despite the decrease, carbon emissions still exceeded carbon sinks, and the carbon metabolism of land use was still far from equilibrium. (2) Between 2010 and 2020, the area of cultivated land began to decrease, and the area of forest land rapidly increased, and land-use-related carbon emissions showed negative growth. This showed that the structural adjustment of energy consumption in Sanmenxia during the period decreased carbon emissions in comparison with the previous period. (3) A comparison of predicted optimized land use patterns with land use patterns in an as-is development scenario showed a decrease in construction land area of 23.05 km² in 2030 with a steady increase in forest land area and a decrease in total carbon emission of 20.43 t. The newly converted construction land in the optimized land use pattern was concentrated in the ribbon-clustered towns built during urban expansion along the Shaanling basin of the Yellow River and the Mianchi–Yima industrial development area.

Traditional remote sensing spatio-temporal data fusion algorithms generally use up-sampled low-resolution images (MODIS) to be fused with high-resolution images (Landsat), this makes both images less spatially consistent and many hybrid image elements in low-resolution images, so uncertainty errors propagate into the fusion results. To address this issue, we propose a framework for combining deep learning-based super-resolution techniques with traditional spatio-temporal fusion methods. By reconstructing low-resolution images using super-resolution image reconstruction techniques, we obtain low-resolution images with more spatial details and better spatial consistency with high-resolution images. These reconstructed images are then fused using spatio-temporal fusion methods. In this study, we selected Flexible Spatio-Temporal Data Fusion (FSDAF) and Residual Channel Attention Network (RCAN) to carry out a detailed study to prove the effectiveness of this kind of framework. That is, a new RCAN-FSDAF model is developed. After testing, RCAN-FSDAF has the following advantages::(1) The band reflectance predicted by RCAN-FSDAF is closer to base reflectance than FSDAF, DMNet, and GAN-STFM, as shown by greater correlation and smaller error. (2) RCAN-FSDAF better decomposes image elements among heterogeneous features and more accurately identifies boundaries between different features and changes in land cover type. (3) High spatial and temporal resolution NDVI data obtained by the inversion of the prediction results of RCAN-FSDAF are more accurate. The framework developed in this study can be extended to other spatial and temporal data fusion applications.

The percentage precipitation anomaly was used to index the effect of drought on winter wheat grown in Henan Province for the years 2011–2020. Of interest was the effect of drought on winter wheat yield and the accurate assessment of the damage done to winter wheat by drought events in order to improve the risk management of winter wheat in the context of drought hazards. The spatial and temporal variability of winter wheat drought risk in Henan Province was determined by analysis of climate data, winter wheat yield, cultivated area, and socio-economic data across three dimensions: exposure or susceptibility to drought, economic–environmental sensitivity to drought, and capacity to resist drought. A drought vulnerability assessment model, based on the entropy value method and a comprehensive fuzzy evaluation, was developed to assess the drought vulnerability of winter wheat in Henan Province compared with the percentage precipitation anomaly model. (1) There were significant spatial differences in the frequency of the five drought categories devised. (2) Areas in which there was a high frequency of mild drought events were mainly in northern and western Henan and southwestern Henan, with the frequency ranging from 17% to 29%. (3) Areas in which there was a high frequency of moderate drought events were mainly in northwestern, central, and southeastern Henan. (4) Areas in which there was a high frequency of severe and extreme drought were mainly in Anyang in northern Henan, Zhengzhou in central Henan, and Xinyang and surrounding areas in southern Henan, with the frequency ranging from 7% to 9.70%. (5) Winter wheat drought vulnerability shows an overall annually increasing trend. The susceptibility dimension had the greatest influence of the three dimensions, followed by economic–environmental sensitivity and then drought resistance, which had the least impact. The model created in this study shows the influence of drought on winter wheat production more intuitively than a conventional fuzzy synthesis, and the results can inform decision-making in winter wheat drought risk assessment and management.

Coal mining has led to escalating ecological and environmental issues in significant coal and grain production areas, posing a severe danger to food security. This study examines the disturbance patterns of soil factors and microbial communities in coal and grain production areas, and attempts to understand the impact of subsidence and water accumulation stress on soil characteristics and microbial communities in coal mining subsidence areas with high subsidence levels. Five specific regions of Zhao Gu Yi Mine, situated in Henan Province and under the ownership of Jiaozuo Coal Group, were chosen. Aside from the control group (CK), the study blocks situated in the coal mining subsidence zones consisted of perennial subsidence ponding (PSP), seasonal subsidence ponding (SSP), the neutral zone (NZ), and the horizontal deformation zone (HDZ). The soil nutrient indices and the stoichiometric properties of soil C, N, and P were assessed on the surface of each block. The organization of the soil microbial community was identified using high-throughput sequencing. The findings indicate that: 1. Substantial disparities exist in soil properties and microbial community structure between the subsidence and non-subsidence zones. The levels of soil organic mater (SOM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP) all decrease to different extents in the subsidence area. Additionally, the coal mining subsidence waterlogged area exhibits higher levels compared to the coal mining subsidence non-waterlogged area. Conversely, the soil water content (SWC), C/N ratio, C/P ratio, and N/P ratio all increase to varying degrees. 2. Regarding the composition of the community, the presence of Proteobacteria is considerably greater in the non-water-logged area of coal mining subsidence (NZ, HDZ) compared to the water-logged area and control group (p < 0.05). The prevalence of Firmicutes in the subsidence water area was substantially greater compared to both the subsidence non-waterlogged area and the control group (p < 0.05). The prevalence of Gemmatimonadota is markedly greater in the waterlogged area of mining subsidence compared to the non-waterlogged area and CK (p < 0.05). The Ascomycota population reached its highest value in the neutral zone (NZ), which was significantly greater than the values observed in the seasonal subsidence ponding (SSP) and perennial subsidence ponding (PSP) regions (p < 0.05). On the other hand, the Rozellomycota population had its highest value in the SSP region, which was significantly greater than the values observed in the other regions (p < 0.05). 3. The abundance and variety of soil bacteria and fungi, as well as their important populations, are associated with different levels of soil characteristics. The primary elements that influence the alteration of microbial communities are soil nutrients and soil water content. The presence of coal mine subsidence and water accumulation has a notable impact on the properties of the soil in the surrounding area. This study offers a scientific foundation for reclaiming land affected by subsidence caused by coal mining in regions where coal and grain production are the dominant industries.

In recent years, with the implementation of the South-to-North Water Diversion Project, the land use problem and its ecological effects on the Danjiang River Basin (DRB), which is a water source in the project, have become some of the focal points of current research in ecology and environmental science. Selecting the DRB (Henan section) as the study area, an ecological vulnerability evaluation model based on the fuzzy comprehensive evaluation/catastrophe progression method was constructed to evaluate the ecological vulnerability of the study area. The spatiotemporal evolution patterns of ecological vulnerability in the study area were quantitatively analyzed, and the main evolutionary drivers were identified by using GeoDetector. The results showed that: (1) the ecological vulnerability of the DRB (Henan section) was mainly moderate and mild, with areas of 2535.26 km² and 2717.33 km², respectively, by 2020, accounting for 30.14% and 32.30%, respectively, of the total area of the basin, with an overall vulnerability distribution characteristic of “low in the north and high in the south”; (2) the ecological vulnerability indices of the DRB (Henan section) in 2000, 2010, and 2020 were 0.56, 0.61, and 0.58, indicating that the ecological quality first decreased and then increased; and (3) the influence of vegetation factors on ecological vulnerability was large, with explanatory power above 4%. The influence of economic pressures and surface factors on ecological vulnerability gradually increased. This study can provide a reference for ecological environmental protection in the water source of the middle route of the South-to-North Water Diversion Project.

Global warming has led to an increase in interest in river hydrology. Water surface slope is a basic parameter of river morphology and is critical in understanding riverine ecosystems. The Yarlung Zangbo River is an important international river. Scientific understanding of its hydrology has been severely limited by its harsh topography and the sparse distribution of measurement stations. This paper describes the first attempt to use ICESat-2 satellite altimetry data to calculate river water slope and introduces a new technique for studying river hydrology. Water level data for different stretches of the Yarlung Zangbo River were obtained from high spatial resolution ICESat-2 altimetry data. The data were analyzed to determine the water surface slope of the Yarlung Zangbo River, and variation in the water surface slope was examined for several river sections. The water surface slope of the Yarlung Zangbo River was greatest in summer and autumn and least in winter and spring. There may be seasonal cyclic changes in the water surface slope. The range for water surface slope was large and the difference in extremes is significant; the maximum average water surface slope was 1935.17 cm/km and the minimum water surface slope was 0.08 cm/km. The water surface slope of the Yarlung Zangbo River can be characterized as small in the upper and middle reaches and large in the lower reaches; it is related to dry and wet conditions determined by topography and climate. The upper and middle reaches of the Yarlung Zangbo River are semi-arid and the lower reaches of the Yarlung Zangbo River span three climate zones: semi-arid, semi-humid and humid. In addition, the water level information measured by ICESat-2 data is basically similar to the water level information measured by Hydroweb data, except that the water level information of individual virtual stations is quite different due to special reasons. It was found that ICESat-2 data had higher-accuracy water level data results and more river location information. Therefore, ICESat-2 satellite altimetry data can provide a baseline reference for the study of the Yarlung Zangbo River hydrology, and the technique used is applicable to other rivers.