Monitoring the intensity and size of a tropical cyclone (TC) is a challenging task, and is important for reducing losses of lives and property. In this study, we use Idai, one of the deadliest TCs on record in the Southern Hemisphere, as an example. Dual-polarization synthetic aperture radar (SAR) measurements from the Copernicus Sentinel-1 mission are used to examine the TC structure and intensity. The wind speed is estimated and compared using well known C-band model functions based on calibrated cross-polarization SAR images. Because of the relatively high noise floor of the Sentinel-1 data, wind speeds under 20 m/s from cross-polarization models are ignored and replaced by low to moderate wind speeds retrieved from co-polarization radar signals. Wind fields retrieved from the co- and cross-polarization model results are then merged together to estimate the TC size and the TC fullness scale, a concept related to the wind structure of a storm. Idai has a very strong wind speed and fullness structure, indicating that it was indeed a very intense storm. The approach demonstrates that open and freely available Sentinel-1 SAR data is a unique dataset to estimate the potential destructiveness of similar natural disasters like Idai.

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future.

In China and elsewhere, urban expansion is directly related to the important issues of social development, economic development, and the sustainable development of the ecological environment. Traditional statistical methods based on administrative regions lack geospatial information, which makes it difficult to analyze and explore in detail the development status and spatial differences of cities. In real time, nighttime light (NTL) remote sensing can reveal the spatial expansion change information of urban built-up areas (UB) on different scales, thus allowing for the analysis of urban spatial patterns and variations in urban development. Based on the long-time sequence NTL data from 1992 to 2020, this work studies the Xiamen Special Economic Zone by using the vegetation-water-adjusted NTL urban index (VWANUI) to extract the urban built-up areas and study the UB expansion patterns, the migration of the urban center of gravity, and intra-city differences. The result is a qualitative and quantitative temporal and spatial evaluation of Xiamen’s economic development characteristics. The results show that the UB of Xiamen expanded 349.219 km² from 1995 to 2020, mainly concentrated in the period 2005–2020, during which time 79.44% of the expansion of the whole study period occurred. Throughout the study period, the urban center of gravity of Xiamen city shifts 8757.15 m to the northeast at the rate of 350.29 m/year in the direction of 74.88° (the urban center of gravity shifted from the inner island to the outer island). The total brightness of nighttime lights in Xiamen is gradually increasing, indicating that the level of urban economic development continuously improved over the measurement period, that human social activities have strengthened, and that the cross-island development strategy has produced certain results. These results provide data that describe urban development and policy formulation in Xiamen.

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.