Forest growing stock volume (GSV) is an essential aspect of ecological carbon stock monitoring. The successive launches of spaceborne microwave satellites have provided a broader way to use microwave remote sensing to monitor forest accumulation. Currently, the inversion parameterization models of active microwave remote sensing stock volume mainly include the interferometric water cloud (IWCM), BIOMASAR, and Siberia. Among them, the IWCM introduces backscattering and coherence, the BIOMASAR model only introduces backscattering, and the Siberia model only introduces coherence. Although these three models combine the backscatter coefficient and coherence of SAR to estimate volume accumulation, the performance of the models has not been evaluated at the same time in the same area. Therefore, this article starts from the perspective of the three combinations of coherence and backscattering, relies on three models that do not require measured data, and evaluates the accuracy of the models’ overall inversion of GSV. In addition, we combine precipitation meteorological information, vegetation types, and seasonal variation to separately explore model performance. The comparison results show that the IWCM model is relatively stable in the process of stock volume inversion and is more sensitive to the vegetation types of coniferous and deciduous forests. The influence of seasons and precipitation on the model is weak, and the accuracy of the multi-time-series model is slightly improved. The Siberia model has a good storage volume inversion effect in this study area, but the multiple time series did not improve the model accuracy. The BIOMASAR model is simple, and its performance was slightly inferior in this study area. Precipitation can negatively affect BIOMASAR. The model results for multiple time series outperform those for single time. In summary, the stability of IWCM is more suitable for research with unknown information. The BIOMASAR model is simple, does not require coherence calculations, and is ideal for the estimation of large-scale national or world-level storage distributions. The Siberian model performs better in small regions and smaller spatiotemporal baselines.

Changes in climate and rapid urbanization have aggravated the urban heat island effect, and a reasonable means to reduce temperature increases in the surface thermal environment is urgently needed. We integrated the research perspectives of patch and network, taking Yinchuan metropolitan region as the research area, and reduced the surface thermal environment through the rational allocation of ecological land. For patch, a correlation analysis and linear regression were used to study the impact of landscape composition and spatial configuration on the surface thermal environment. For network, the thermal source patches were determined based on the morphological spatial pattern analysis (MSPA) method, the thermal resistance surface was calculated based on the minimum cumulative resistance model, and the pinch points and corridors that prevented the surface thermal environment from circulating were determined based on circuit theory. Finally, ecological land with a cooling effect was deployed at the pinch point to prevent heat patch from spreading and thus connect to larger heat networks, and the regional cooling effect was estimated. The results were as follows: (1) The fitting precision of landscape factors and the surface temperature was in the order of area ratio of ecological land > shape index > fragmentation index. When the area ratio of ecological land was greater than 61%, the patch shape was simple, the degree of fragmentation was low, and the cooling effect was the most obvious. (2) Then, 34 corridors, 44 pinch points, and 54 grids of ecological land were identified for deployment. (3) After the deployment of ecological land, the simulated cooling effect was between 0.04 and 6.02 °C, with an average decline of 2.16 °C. This research case offers approaches for mitigating temperature increases in the surface thermal environment and improving the sustainable development of cities (regions), and it serves as a reference for improving the ecological environmental quality in arid and semiarid areas.

Analyzing human–environment coupling is important in understanding the mechanisms and developments of human–environment systems. However, the current frameworks and approaches evaluating the relationship between human activities and the ecological environment remain limited. Integrating the vegetation-impervious surface–soil–air framework, Mann–Kendall test, correlation analysis, two-step floating catchment area method, coupling analysis, and optimal parameters-based geographical detector, this study comprehensively evaluate the environmental changes and analyzes the coupling relationship between environment and human activities, mainly in terms of habitat quality, landscape pattern, and ecological services. The study area was the Qilian Mountain National Nature Reserve in Gansu province, China, an ecologically fragile region with an environment closely linked to human activities. Along with district and county census data, various remote-sensing products (e.g., MODIS, Landsat) were used to assess the ecological level and human–environment coupling state of the study site from 2003 to 2019. The main results show: (1) The remote sensing composite index, which integrates eight ecological sub-indices, effectively captures the spatial and temporal variations of the ecological environment in the study area, providing comprehensive and detailed environmental information. (2) Analysis using the Mann–Kendall-correlation classification, coupling degree, and two-step floating catchment area methods consistently demonstrates a gradual coordination between human activities and the ecological environment in the study area. (3) In comparison to spatially interpolated population data, the remote sensing human activity index more significantly represents the spatial impact of human activities on the ecological environment. (4) The environmental aspects most strongly associated with human activities include carbon fixation and oxygen release, vegetation, humidity, and soil. (5) The ecological environment level does not uniformly deteriorate with increasing population density, and a notable alignment is observed between changes in the ecological environment and the implementation of government environmental protection policies.