China’s rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China’s total urban area increased from 31,076 km² in 2001 to 80,887 km² in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km² of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km² or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai–Nanjing–Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km²) and 6.05% (2702 km²) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km²) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China’s rapid urbanization.

Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters.

Exploring carbon dioxide (CO₂) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO₂ emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO₂ emissions, few have systematically revealed the ability of nighttime lights for evaluating CO₂ emissions from different industries, such as service industry CO₂ emissions (SC), traffic CO₂ emissions (TC), and secondary industry CO₂ emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities.