The summer upwelling around the Zhoushan Islands is well-known. The previous concise review of (mostly) observational studies reveals that the present knowledge of the Zhoushan upwelling is unsatisfactory and has focused on seasonal variations. In this study, a sea surface temperature (SST) gradient-based upwelling detection algorithm was used. The Level 3 daily and hourly SST data from the geostationary satellite Himawari-8 were used to explore statistical features, seasonal variations, and short-term variations of the Zhoushan upwelling. Despite the duration period being like in previous studies, there is a new finding that the location of the upwelling center has a significant monthly migration. The statistical results show that the potential upwelling spots are clustered in the location with large topographic gradients and can be divided into four aggregation areas: between Gouqi Island and Lvhua Island, off Shengsi Island, around the Zhongjieshan Islands, and off the Taohua-Liuheng Islands. The core area of the Zhoushan upwelling is located at 122°E–123°E, 29.5°N–31.15°N with an irregular ellipse extending from southwest to northeast. The continuous cloud-free satellite images display that the lifecycle of the short-term variations was about 24 h and included two stages: intensification and decay. Meanwhile, the surface upwelling center has onshore–offshore movement under the advective transport of local tidal currents. A preliminary discussion suggests that the quasi-24 h periodic variations may be caused by the competing effect between tidal mixing and the stratification in the water column.

Dryland lakes are indispensable to regional water resource systems. Ebinur Lake, the largest saline lake in Xinjiang Uygur Autonomous Region, is vital for regional biodiversity and environmental stability but has been facing the predicament of gradual shrinkage in recent decades. In this study, we proposed a new dual-index method for Landsat (-5, -7, -8, and -9) data to extract water with the combinations of the normalized difference water index (NDWI) and the modified NDWI for turbid waters (NDWI_turbid). The dual-index method showed a high overall accuracy of 96.36% for Ebinur Lake. Landsat series images from 1992 to 2023 were employed to acquire the water areas of Ebinur Lake. The results showed that, over the past three decades, the area of Ebinur Lake exhibited a fluctuating decreasing trend, with an average lake area of 568.74 ± 152.43 km². The northwest intermittent water areas showed significant changes, and there was a close connection between the northwest and core water areas. Seasonally, the lake area decreased from spring to autumn. River inflow, driven by rainfall and human activities, was the primary factor affecting the inter/inner annual changes in Ebinur Lake. Furthermore, due to the valley effects, wind was found to be a critical factor in the diurnal changes in the water areas. This study should deepen the understanding of the variations of Ebinur Lake and benefit local water resource management.

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr⁻¹, R² = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr⁻¹, R² = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr⁻¹, R² ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio R_rs(490)/R_rs(560) (R² = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

The relationship between the urbanization process and the ecological environment is key to regional development. As a typical Chinese city undergoing rapid urban development, Zhengzhou is an important representative of the urbanization process and the changes in the ecological environment. In this study, we explored the response relationship between urban development and the ecological environment in Zhengzhou, using night light data, Landsat satellite imagery, and population data from this city. The analysis of the NTL data showed that there were three stages of development in Zhengzhou from 2000 to 2021: the slow expansion stage from 2000 to 2003, the steady expansion stage from 2004 to 2011, and the rapid expansion stage from 2012 to 2021. The multi-year average RSEI value of Zhengzhou was less than 0.4, and it showed a trend of first increasing and then decreasing, indicating that the quality of the city’s ecological environment was poor and indirectly indicating that the urbanization degree of the region was significant. The changes in the NTL and RSEI indicate that urban development has significantly reduced the quality of the city’s ecological environment, particularly after Zhengzhou entered the stage of rapid expansion. The coupling degree (C) and coupling coordination degree (D) between urbanization and the ecological environment showed a decreasing trend, and the average value was lower than 0.3. This indicates that the ecological environment in Zhengzhou has been seriously affected by the process of urbanization, and the natural ecology has been strongly impacted by human activity. C and D also showed a decreasing trend from 2000 to 2015 but increased from 2016 to 2021, indicating that the ecological environment in Zhengzhou has gradually improved. The degree of coordination D between urbanization and the ecological environment in Zhengzhou had a strong negative correlation with the population size and growth rate but a positive correlation with the Moran value, indicating that an increase in the population increases the burden on the ecological environment. However, a reasonable spatial population distribution is conducive to improving regional urban–ecological coordination.

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The “black pixel” assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the “black pixel”. Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr⁻¹) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters.