The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.

Urban Surface Ecological Status (USES) reflects the structure and function of an urban ecosystem. USES is influenced by the surface biophysical, biochemical, and biological properties. The assessment and modeling of USES is crucial for sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities. The objective of this study is to present a new analytical framework for assessing the USES. This analytical framework is centered on a new index, Remotely Sensed Urban Surface Ecological index (RSUSEI). In this study, RSUSEI is used to assess the USES of six selected cities in the U.S.A. To this end, Landsat 8 images, water vapor products, and the National Land Cover Database (NLCD) land cover and imperviousness datasets are downloaded for use. Firstly, Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI) are derived by remote sensing methods. Then, RSUSEI is developed by the combination of NDVI, NDSI, Wetness, LST, and Impervious Surface Cover (ISC) with Principal Components Analysis (PCA). Next, the spatial variations of USES across the cities are evaluated and compared. Finally, the association degree of each parameter in the USES modeling is investigated. Results show that the spatial variability of LST, ISC, NDVI, NDSI, and Wetness is heterogeneous within and between cities. The mean (standard deviation) value of RSUSEI for Minneapolis, Dallas, Phoenix, Los Angeles, Chicago and Seattle yielded 0.58 (0.16), 0.54 (0.17), 0.47 (0.19), 0.63 (0.21), 0.50 (0.17), and 0.44 (0.19), respectively. For all the cities, PC1 included more than 93% of the surface information, which is contributed by greenness, moisture, dryness, heat, and imperviousness. The highest and lowest mean values of RSUSEI are found in “Developed, High intensity” (0.76) and “Developed, Open Space” (0.35) lands, respectively. The mean correlation coefficient between RSUSEI and LST, ISC, NDVI, NDSI, and Wetness, is 0.47, 0.97, −0.31, 0.17, and −0.27, respectively. The statistical significance of these correlations is confirmed at 95% confidence level. These results suggest that the association degree of ISC in USES modeling is the highest, despite the differences in land cover and biophysical characteristics in the cities. RSUSEI could be very useful in modeling and comparing USES across cities with different geographical, climatic, environmental, and biophysical conditions and can also be used for assessing urban sustainability over space and time.

Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical) approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.