The relationship between the land cover (LC) characteristics and the land surface temperature (LST) is significant for surface urban heat island (SUHI) study and for sustainability research. To better understand how the land surface temperature (LST) responds to LC, two urban areas, Vienna and Madrid, with different climatic conditions are selected and compared, using Landsat-8 OLI data and urban atlas data. To determine a suitable scale for analyzing the relationship between LC and LST, a correlation analysis at different sizes of spatial analytical scales is applied. To demonstrate the LC composition effects on LST, a regression analysis of the whole study area and in the specific circumstance is undertaken. The results show that: (1) In the summer, Vienna presents high temperature in the urban areas and low temperature in the surrounding rural areas, while Madrid displays the opposite appearance, being relatively cooler in the urban areas as compared to the rural areas, with the main different factors affecting elevated urban LST; (2) Suitable analytical scales are suggested in studying the LC–LST relationship between different LC characteristics in the two study areas; (3) Negative effects on the LST appear when the area of cooling sources, such as water or urban greenery, reaches 10% at a 990 × 990 m² scale in Vienna. Built-up area is the main factor affecting elevated urban LST where such areas cover the majority at a 990 × 990 m² scale in Madrid. These findings provide a valuable view regarding how to balance the urban surface thermal environment through urban planning.

The Sentinel-1 constellation can provide numerous high-resolution C-band synthetic aperture radar (SAR) data with long-term continuity and freely, thus showing a cost-effective solution for the coastal monitoring at high or moderate spatial resolutions. The major goal is to improve estimates of shallow water depth for SAR applications. We present an algorithm that is based on the linear dispersion relation between water depth and swell parameters like swell wavelength, direction, and period to estimate shallow water depth using multitemporal SAR data with a short repeating cycle. This is accomplished via circular convolution and Kalman filter that provides both the estimates and a measure of their uncertainty at each location. The introduced algorithm is tested on four Sentinel-1 interferometric wide swath (IW) mode SAR images over the coastal region of Fujian Province, China. The retrieved water depth both from multitemporal SAR images and different single SAR images show general agreement with water depth from an official electronic navigational chart. All comparisons indicate that the proposed method is feasible and multitemporal SAR data have great potential in bathymetric surveying.

The long-term changes of the relationship between nighttime light and urbanization related built-up areas are explored using nighttime light data obtained from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS, data before 2013) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS, data after 2012) and information of the spatiotemporal heterogeneity of urban evolution. This study assimilates two datasets and diagnoses the spatial heterogeneity in administrative city scale based on built-up area tendencies, temporal heterogeneity in pixel scale based on nighttime light intensity tendencies, and GDP associated spatiotemporal variability over the Yangtze River Delta comparing the first two decades of this century (2001–2010 versus 2011–2019). The analysis reveals the following main results: (1) The built-up areas have generally increased in the second period with the center of fast expansion moving southward, including Suzhou-Wuxi-Changzhou, Hangzhou, Ningbo, Nanjing, and Hefei. (2) Urban development in the original city core has saturated and is spilling over to the suburbs and countryside, leading to nighttime light intensity tendency shift from a “rapid to moderate” and a “moderate to rapid” development (a “hot to cold” and a “cold to hot” spatial clustering distribution). (3) The tendency shifts of built-up area and nighttime light intensity occur most frequently in 2010, after which the urban development is transforming from light intensity growth to built-up area growth, particularly in the developed city cores. The urban agglomeration process with nighttime light intensity reaching saturation prior to the urban development spreading into the surrounding suburbs and countryside, appears to be a suitable model, which provides insights in addressing related environmental problems and contribute to regional sustainable urban planning and management.

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data.

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-\(\overline{cum}\) < 4) in Northwest China and (DHI-\(\overline{cum}\) > 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-\(\overline{min}\)) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-\(\overline{sea}\)); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-\(\overline{min}\)~0.05 and DHI-\(\overline{sea}\)~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China’s terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI.

Urbanization drives human social development and natural environmental changes and shows complex implications for sustainability and challenges of future development, particularly in emerging countries. While extensive studies focus on extracting urban areas more precisely, less attention has been devoted to understand megaregion evolution and its related socioeconomic processes, not by socioeconomic statistics, but by comparing remote sensing based spatiotemporal evolution and the related spillover effect. Three main megaregions (with large area, high population and total gross domestic product) in China are selected for the analysis of development changes in an urbanization (magnitude, development)-diagram, of growth pattern changes based on Gravity Center and weighted Standard Deviation Ellipses and of the megaregions’ spillover effect. Employing the spatiotemporally continuous lighted areas (DN ≥ 12) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime signal (1992–2013) to the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) leads to the following results: (i) Developments in the (magnitude, development)-diagram indicate 25.97%, 45.95%, and 39.10% of the first (high urbanization, fast development) class of the BTH, YRD, and PRD megaregions are rapidly developing into highly urbanized regions. The first class may slow down in the future like the second (high urbanization, slow development) class acting from 1992 to 2013, and the third (moderate urbanization, fast development) class shows potential to become the first class in the future. (ii) The original core function zones of YRD and PRD have highly developed till 1992 and expanding out with fast development from 1992 to 2013. Contrarily, BTH indicates more fast development toward the original core function zones while spatial expansion. (iii) The gravity distance evolution of the three megaregions shows a tendency towards the geometric distance 2013. However, YRD and PRD (BTH) indicate a light intensity expansion (concentration). This may relate to a positive spillover effect of YRD and PRD upon their neighbor cities, with the strongest signal in the early 21st Century and thereafter adjusting and followed by another positive spillover.