On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

Sustainable development in urban areas is at the core of the implementation of the UN 2030 Agenda and the Sustainable Development Goals (SDG). Analysis of SDG indicator 11.3.1—Land-use efficiency based on functional urban boundaries—provides a globally harmonized avenue for tracking changes in urban settlements in different areas. In this study, a methodology was developed to map built-up areas using time-series of Landsat imagery on the Google Earth Engine cloud platform. By fusing the mapping results with four available land-cover products—GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020—a new built-up area product (BTH_BU) was generated for the Beijing–Tianjin–Hebei (BTH) region, China for the time period 2000–2020. Using the BTH_BU product, functional urban boundaries were created, and changes in the size of the urban areas and their form were analyzed for the 13 cities in the BTH region from 2000 to 2020. Finally, the spatiotemporal dynamics of SDG 11.3.1 indicators were analyzed for these cities. The results showed that the urban built-up area could be extracted effectively using the BTH_BU method, giving an overall accuracy and kappa coefficient of 0.93 and 0.85, respectively. The overall ratio of the land consumption rate to population growth rate (LCRPGR) in the BTH region fluctuated from 1.142 in 2000–2005 to 0.946 in 2005–2010, 2.232 in 2010–2015 and 1.538 in 2015–2020. Diverged changing trends of LCRPGR values in cities with different population sizes in the study area. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region. The cities classed as either small or very small had the highest LCRPGR values; however, some of these cities, such as Chengde and Hengshui, experienced population loss in 2005–2010. To mitigate the negative impacts of low-density sprawl on environment and resources, local decision makers should optimize the utilization of land resources and improve land-use efficiency in cities, especially in the small cities in the BTH region.

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.

Extreme heat events, or heatwaves, exert significant impacts on human society, ecosystems, and the economy. The continuous development of remote sensing technology has facilitated the acquisition of high-quality data for assessing health risks associated with these extreme heat events. This study systematically reviews the evaluation factors and assessment framework for a spatially explicit assessment of heat-related health risks. The contribution of geospatial big data, with a particular focus on satellite observations, to these assessments was investigated. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat surface temperature (LST) are identified as the two most widely utilized data sources for mapping heat hazards. The incorporation of multi-sensor observations, along with the implementation of spatiotemporal fusion and downscaling techniques, enhances both the spatial resolution and temporal frequency of heat hazard characterization. It is essential to consider issues of justice and equality in heat-resilient planning and mitigation practices. Integrating heatwave risk assessment results with analyses of urban morphology, land use functions and infrastructure can provide critical information for government agencies to strategically plan urban layout, functions, and public service facilities while optimizing and enhancing urban green infrastructures.