Around 90% of the oceanic and inland waters’ reflectance registered in satellite detectors comes from the atmospheric contribution. Hence the water-leaving radiances in the Near-InfraRed (NIR) region are above the zero value over inland waters because of sediments and dissolved organic particles, this radiance cannot be ignored. To accurately retrieve water quality parameters from water-leaving reflectance, atmospheric correction is the most important step. This study evaluated five reliable atmospheric correction algorithms (AC) known as: (ACOLITE, C2RCC, iCOR, 6SV, and Sen2Cor) against optical in-situ measurements collected above the water in Qiandao Lake, China using Sentinel-2 Multi-Spectral Imager. 60 in-situ water samples and optical measurements (range 400–900 nm) above the water were collected at different points in Qiandao Lake. The spectra measurements were used to validate the atmospheric correction processors. All ACs that were evaluated showed high levels of uncertainty. ACOLITE and ICOR performed the best statistics with root mean square differences (RMSD) (0.006 sr⁻¹) while Sen2Cor achieved the lowest RMSD (0.023 sr⁻¹) across the different modules. ACOLITE, had a better performance when applied to meso- and hypereutrophic waters, compared with oligotrophic, while C2RCC performs better at the wavelength of 833 nm (0.007 sr⁻¹). Finally, 6S performs better at the wavelength of 665 nm (0.015 sr⁻¹). This study introduces insights and addresses a significant research gap in the field of atmospheric correction for satellite imagery over inland waters. Prior studies have primarily focused on atmospheric correction algorithms for coastal and open ocean environments while few studies focused on the unique characteristics and challenges associated with inland water bodies. The findings of this study are crucial for researchers, remote sensing experts, and environmental scientists working with Sentinel-2A imagery, as it enables them to make more accurate and reliable interpretations of water quality and other environmental parameters derived from satellite data.

Nowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land surface temperature (LST) data were used. The Yangtze River Delta Urban Agglomeration (YRDUA), eastern China, was selected as an example to study SUHI and multiple influencing factors in 16 big cities. Anthropogenic factors are considered the most important ones in determining SUHI, while natural factors remain influential. By using stratified random sampling (SRS), 78,085 random points were selected within the 16 cities. Nine influencing factors were selected in this study: distance from building (BD), distance from the main roads (RD), distance from water (WD), digital elevation model product (DEM), gross domestic product (GDP), normalized difference vegetation index product (NDVI), nighttime lighting intensity (NTI), population (POP) and impervious surface area data (%ISA). The SUHI intensity was extracted at each random point as well as the values of the influencing factors, NDVI, DEM, ISA, POP, NTI and GDP. For BD, WD and RD, random points were selected from the water, building and main roads using the near tool in ArcGIS to measure these distances. Boosted regression tree (BRT) model was applied to capture the contributions of the above factors to SUHI. We also applied a different procedure to evaluate the relative influence of Land Use and Land Cover (LULC). The relative influence refers to the contribution of each factor to determine SUHI. The influencing factors were ranked on the basis of the relative influence on SUHI. The results showed that (1) higher SUHI intensity was recorded in Shanghai, Jiaxing and Nanjing cities respectively, while Hangzhou recorded the lowest SUHI. (2) Anthropogenic drivers have slightly higher relative influence on SUHI than natural drivers, i.e. 51.29% and 48.71% respectively. The influence of all drivers on SUHI from high to low is NTI (27.62%), ISA (24.38%), NDVI (12.11%), GDP (7.95%), DEM (7.29%), POP (6.37%), BD (5.33%), WD (4.93%), RD (4.02%). (3) The variation in the socioeconomic level lead to different spatial patterns of different influence factors, further indicating that the overall mean SUHI intensity is affected by the development of the city.

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.