With the development of a sustainable lifestyle, people are paying more and more attention to the comfort of their own living environment, including public space lighting, which is immediately accessible to residents. The demand on its quality and comfort has thus gained attention recently. However, there is still a lack of related research on public space lighting comfort evaluation models that combine nighttime light remote sensing data and field measurement data, and link lighting attributes with the comfort of residents. This research uses nighttime light remote sensing data to select typical test areas, measures the lighting data of the survey points on the spot, develops an intelligent WeChat applet that collects public perception data, analyzes different lighting parameters, and builds a public space lighting comfort model based on the structural equation model analysis method. The results show that the factor that the areas with high light intensity are more comfortable than the areas with low light intensity. In areas with high light intensity, people pay more attention to the uniformity, security and comfort of the light, while in areas with low light intensity or high blue light, people’s perception of glare will be more obvious. This research can provide a basis for the intelligent optimization of public space lighting from the perspective of public preference.

The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical.

Differences in cropland definitions, spatial resolution, and mapping strategies complicate product selection. We harmonized and evaluated eight 2020 cropland and land-cover products across mainland China using hierarchically labeled probability samples. Fixed map predictions enabled comparisons across common task-specific and product-definition-matched reference labels. Evaluation covered spatial agreement, design-weighted classification accuracy, design-based area bias, cropland-type responses, and cross-scale performance. Only 26.8% of pixels in the cropland union were classified as cropland by all products. Within the consensus-cropland evaluation domain, CropLayer achieved the highest overall accuracy (0.961) and F1 score (0.844). CCropLand30 led 30 m products in F1, whereas the Esri land-cover product (ESRI-LC) had national area bias closest to zero (−2.26%). Most 30 m products showed commission-dominated errors and positive area bias. Product responses varied across cropland types, and reference labels altered performance estimates. Switching from unified-agriculture to product-definition-matched labels reduced ESRI-LC’s F1 from 0.765 to 0.682 and shifted area bias from −8.08% to +14.88%. Spatial aggregation reduced F1 for some fine-resolution products without consistently changing local cropland-proportion error. These results show that classification accuracy and area bias yield different rankings, supporting product selection according to application, cropland definition, landscape context, evaluation metric, and spatial support.