Predicting the spatio-temporal evolution characteristics and trade-off/synergy relationships of ecosystem service value (ESV) under different policy scenarios is of great significance for realizing regional sustainable development. This study established a framework and used the geographical simulation and optimization systems-future land use simulation (GeoSOS-FLUS) model and bivariate local autocorrelation analysis to stimulate and predict the impact of land use change on the ESV of Anyang City from 1995 to 2025. We also explored the trade-offs and synergy among ecosystem services under three policy scenarios (natural evolution, cultivated land protection, and ecological protection) in 2025. Results show that (1) the land use change in Anyang from 1995 to 2025 was significant, and the degree of land use change under the cultivated land and ecological protection scenarios was more moderate than that under the natural evolution scenario; (2) The total ESV decreased between 1995 and 2015, amounting to losses of 1126 million yuan, and the decline from 2015 to 2025 under the natural evolution scenario was more significant than those under the cultivated land protection and ecological protection scenarios; and (3) an obvious synergy was observed between various ecosystem services in Anyang City under different scenarios in 2025, and the most significant synergy was observed under the natural evolution scenario. In terms of spatial distribution, the agglomeration of “high–high” synergy in the west and “low–low” synergy in the central region was significant. Local areas showed “high–low” and “low–high” trade-off relationships scattered between their built land and woodland or cultivated land. The proposed framework can provide certain scientific support for regulating land use and ecosystem services in rapidly urbanized areas.

Urban form plays a critical role in shaping the spatial differentiation of land surface temperature (LST). However, limited research has investigated the underlying driving forces and interactions of multidimensional urban form, specifically considering two-dimensional (2D) urban land use and three-dimensional (3D) buildings, on LST. Furthermore, their multi-scale outcomes remain unclear. Taking the main urban area of Wuhan City as an example, a total of nine indicators—the proportion of administration land (PA), the proportion of commercial land (PB), the proportion of industrial land (PM), the proportion of residential land (PR), the proportion of water area (PE), the building density (BD), the building height (BH), the floor area ratio (FAR), and the sky view factor (SVF)—were selected; this paper used the geographic detector model to investigate the driving force of LST spatial differentiation in winter and summer, as well as the interaction of various influencing factors from a multi-scale perspective. The results showed that (1) the average LST in industrial land was higher than that in commercial land, both in summer and winter. The LST in administration land was higher than that in residential land, while in winter, it is the opposite. (2) The spatial differentiation of summer LST was mainly dominated by 3D buildings, while the spatial differentiation of winter LST was mainly dominated by 2D land use. (3) BD was the leading driving force of LST spatial differentiation in summer, and the interaction between BD and any other indicator showed the most significant explanatory power, which is the same for PM in winter. (4) As scale increased, the explanatory power of 2D urban land use for LST spatial differentiation gradually increased both in winter and summer, while the explanatory power of PE on LST spatial differentiation decreased. The explanatory power of BD, FAR, and SVF on LST spatial differentiation remains basically unchanged. The explanatory power of BH on summer LST spatial differentiation decreases with increasing scale, while the explanatory power of BH on winter LST spatial differentiation remains in a stable state. (5) The interaction among all urban form factors primarily increases as the scale increases, except for the interaction between BH and 2D urban land use in summer, and the interaction between PE and PR in winter. The research can provide scientific decision-making support for the collaborative optimization of multiscale urban forms to improve the urban thermal environment.

Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the flood-affected area, promoting post-disaster farmland restoration, and providing an auxiliary decision-making basis for flood prevention and disaster relief departments. Taking the flood event in Henan and Shanxi Provinces as example, based on the characteristics of the variations in radar data and optical data before and after the disaster, we propose an extent information extraction method for the flood inundation area and the flood-affected area of agricultural land. This method consists of change detection, threshold extraction, and superposition analysis, which weakens the negative impact of the radar data speckle noise and cloud contamination of the optical data on the extraction of the agricultural land flooding to a certain extent. The method was developed based on a flood event in Henan Province and validated in Shanxi Province. The results show that the production of this method have a clear boundary and accurate extent, and the overall precisions of the flood inundation area and flood-affected area extraction are 0.87 and 0.92, respectively. The proposed method combines the advantages of both radar and optical remote sensing data in extracting the specific extents of the flood inundation area and the flood-affected area in large spatial scale. Finally, the impact of time window size to the performance of the method is further analyzed. In the application of the proposed method, the Google Earth Engine (GEE) platform provides a low-cost, fast, and convenient way to extract flood information from remote sensing data. The proposed scheme provides a scientific data basis for restoring production of agricultural land after a flood disaster, as well as for national post-disaster damage assessment and disaster relief decision making.

Identifying the relationships between ecosystem services (ESs) and land use change is crucial for ES management and sustainable regional development. The Manas region in China has witnessed dramatic reclamation activities in its desert areas that resulted in ecological problems. The changes in eight ESs, including crop production (CP), livestock production (LP), soil conservation (SC), water yield (WY), sand fixation (SF), carbon sequestration (CS), habitat quality (HQ), and nature landscape recreation (NLR), were investigated by using biophysical and questionnaire methods. At the regional scale, provisioning services (i.e., CP and LP) showed some performance improvements, whereas most of the regulating services (i.e., WY, CS, and HQ) along with NLR showed a performance decline. Five ES bundles—Upper Mountain, Foothill, Oasis, Oasis–Desert Transition, and Desert bundle—were identified at the township scale via k-means clustering. From 2000 to 2015, the Oasis bundle sprawled as a result of oasisization, whereas the Oasis–Desert Transition and Foothill bundles decreased. We performed a questionnaire survey and a statistical analysis to identify the causes behind the performance improvement/decline of these ESs and found that the land use changes in the Manas region had a significant impact on these services. More than 50% of the survey respondents identified land use changes as the primary driver of the changes in some ESs (i.e., CP, CS, HQ, and NLR). In the correlation and partial correlation analyses, oasisization was significantly and positively correlated with CP but was negatively correlated with WY, CS, HQ, and NLR. We enhanced the reliability of our conclusions by integrating biophysical and sociocultural methods into our investigation of ES and land use change. In view of the huge losses in regulating and cultural services, the Manas region should limit its desert reclamation activities to control the expansion of its oasis and to improve the quality of its cropland. Our results can help formulate effective ES management and land use decisions in the Manas region or similar areas.

The return of rural migrant workers through increasing agricultural income by expanding farming scale is significant for rural sustainable development without rural population loss. This paper selected six representative counties in Henan Province, China’s major grain-producing province, to conduct a questionnaire survey, investigated the incomes of farmers from farming and migrant workers, calculated moderate farming scale under different opportunity costs, and also estimated the agricultural population that can be supported by arable land resources. Results are as follows: (1) Under the traditional planting mode, annual per capita income of farmers in farming was USD 342.18, which was substantially lower than USD 5255.63 in migrant workers. This huge income gap has led to continuous rural population loss. (2) Under the opportunity cost of farming income equal to migrant workers income, moderate farming scales of the six selected counties were 1.39, 1.17, 1.22, 1.08, 1.34, and 1.01 ha, respectively. Under the 0.8x and 0.6x opportunity cost, corresponding moderate farming scales were 1.11, 0.94, 1.11, 0.86, 1.07, and 1.34 ha; and 0.84, 0.70, 0.73, 0.65, 0.80 and 1.01 ha, respectively. (3) On the basis of the three moderate farming scales and status quo of arable land resources, agricultural populations that can be supported by rural Henan Province were 8.0386 million, 10.0479 million and 13.3942 million, respectively. Findings can guide the formulation of rural revitalization strategic measures and the preparation of village territorial spatial planning.

Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color.