Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide.

This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. TSM concentration was notably high in 2013, peaking at 180 mg/L during the flood season and 80 mg/L in the dry season. In contrast, 2020 saw lower concentrations. Similar spatial distribution patterns were observed during dry and flood seasons, with high nearshore and low offshore TSM concentrations. Statistical analyses revealed natural factors (precipitation and runoff) as major influencers of the TSM distribution, with human activities presenting localized, limited impacts, except under long-term and large-scale conditions. Over time, the influence of large-scale water-based construction, such as the Hong Kong–Zhuhai–Macao Bridge, on TSM distribution became significant.

The fish catch in natural upwelling areas, which accounts for only 0.1% of the ocean surface, accounts for more than 40% of the world’s catch. The Zhoushan fishery, which is the largest fishery in China, is mainly formed by a coastal upwelling that features a low temperature. The upwelling in the study area (29.5–31.5° N, 121.5–123.5° E) is a vital factor affecting the formation of the Zhoushan fishery, and the primary productivity and low temperature that are brought by the rising water are important features of the upwelling. This study used global real-time high-resolution multivariate fused satellite (OSTIA) daily sea surface temperature (SST) data developed by the United Kingdom Meteorological Office that were collected from 1981 to 2020 to explore the spatial and temporal variation of the characteristics of the upwelling phenomenon in the study area. The data were processed by a temperature gradient-based upwelling edge detection algorithm to extract information on the central location of the upwelling, the location clusters in the core area, and the intensity index. The quantities of center and core area clusters were counted for each pixel point, and their corresponding probability values were calculated. The results of the spatial and temporal variation of the characteristics of the upwelling show that the upwelling in the study area was generated in April of each year, increased in intensity, and peaked in August, furthermore, the southern part of the upwelling dissipated in September. The region’s upwelling is spatially oblique and elliptical, with its long axis following the northeast and extending as far as the mouth of the Yangtze River. Its central location and core area were relatively stably existing in Ma’an Archipelago and Zhongjieshan Islands, which was consistent with the location of the two marine pastures in Zhoushan. According to our findings, locations with higher probability values in the upwelling center and core area, where upwelling occurs frequently, are usually accompanied by higher productivity and offer the potential to develop fishing grounds. The insights that were drawn from the study observations can, therefore, provide some reference for future artificial upwelling site selection.

A blocking high in the Ural Mountains, which is recognized as the third major blocking high area in the northern hemisphere, describes a deep warm high-pressure system superimposed on the westerly belt. Based on the ERA-5 daily reanalysis data (the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis global climate dataset) and using the Tibaldi and Molteni (TM) method, we selected 43 blocking high events in the Ural Mountains during the extended winters of 1979–2020 and analyzed their atmospheric circulation characteristics and influencing factors. Our findings revealed a downward trend in the frequency of occurrence of blocking highs in the Ural Mountains in winter, most of them were short-lived; furthermore, the frequency and duration of these occurrences generally followed a 3–4 years oscillating cycle. The synthetic results of the geopotential height (HGT) anomaly field and the surface air temperature (SAT) anomaly field of these 43 extended wintertime blocking high events in the Ural Mountains region showed that during the development of a blocking high, the central intensity of the positive anomalies in the Ural Mountains region first increased and then weakened, while the central intensity and meridional span of the negative anomalies in the Eurasian mid-latitudes of the SAT anomaly field increased continuously. In addition, abnormally high sea surface temperature (SST) in the North Atlantic sea area and abnormal reduction of sea ice (SI) in the Barents-Kara Sea and the Chukchi Sea in autumn had a significant impact on the wintertime formation of Ural Mountains blocking highs. In contrast, in autumn, the abnormal reduction of SI in the Barents-Kara and Chukchi Seas might also have led to the westward positioning of Ural Mountains blocking highs.

Vegetation plays an important role in the exchange of heat and moisture in the earth-atmosphere system, and the vegetation index can not only better measure the growth and changes of vegetation, but also provides required data for meteorological, hydrological, ecological and other studies. Therefore, in-depth study of the spatial and temporal changes in regional vegetation index is of great significance for guiding eco-logical environment protection and governance. This paper takes the northern foot-hills of the Qinling Mountains and its north area in Shaanxi as the research object, an-alyzes the sensitivity of normalized vegetation index (NDVI) acquired from Sentinel-2, Landsat and MODIS data to the phenological characteristics of vegetation, and uses the linear regression slope method and partial correlation coefficient method to ana-lyze and discuss the spatiotemporal distribution characteristics and influencing factors of NDVI at different time scales. The results show that: 1) Sentinel-2 images and MODIS images have better spatiotemporal consistency in obtaining monthly NDVI data and refining vegetation phenological characteristics in the study area than Landsat images; 2) NDVI at different time scales in the study area showed a signifi-cant upward trend during 2001-2023, with a large NDVI increase mainly concentrated in the eastern part of the study area, especially in the Lishan Mountain area, while the areas with a decrease or no significant change in NDVI are mainly urban areas where human life and production are relatively frequent; 3) The NDVI in most areas of the study area showed an increasing trend from 2019 to 2023, especially in the Lishan Mountain area within the ecological protection and restoration project area, but there was a large area of NDVI decrease in the western part of the Qinling mountain front flood plain; 4) The lagging effects of precipitation and air temperature on monthly NDVI are both positive in a short period of time. Meanwhile, in spring and monthly scales, precipitation and air temperature have a positive correlation with vegetation growth in the study area, and the influence of air temperature is more significant.