Mariculture is an important offshore economic activity, and excessive farming can lead to the deterioration of sea ecology. The concentration of nutrients (mainly DIN (dissolved inorganic nitrogen) and PO4 (orthophosphate-phosphorous)) is the main factor characterizing the health condition of farmed seas. Conventional field monitoring methods are spatiotemporally limited, and remote sensing technology has the advantages of high spatial coverage and long time series monitoring. Thus, the Sentinel-3 reflectance data and the in situ measured data for the offshore waters of Wenzhou were matched simultaneously. Then, the matched dataset between the Sentinel-2 band and the in situ measured data were obtained through spectral correspondence conversion between Sentinel-2 and Sentinel-3, and a machine learning algorithm was used to build the inversion model with an independent validation process. The correlations between the concentration of nutrients, area of rafts and precipitation were assessed, and a strong positive correlation was found between the concentration of nutrients and the area of rafts, and a weak negative correlation was found between the former and precipitation.

The flux of total suspended matter (TSM), F_TSM, output by several large rivers in Asia, has been in decline due to human activities. As the estuary of the Ganges–Brahmaputra River, the Padma River transports a significant amount of suspended matter (SM) to the Bay of Bengal each year. In this study, the TSM concentration (C_TSM) and F_TSM in the Padma River in the period 1991–2019 were calculated based on the data acquired by the Landsat series satellites and an empirical TSM algorithm model for large, high-turbidity rivers. The results showed that the maximum and minimum F_TSM values (318 ± 62 and 73 ± 29 mt, respectively) in the Padma River occurred in 2011 and 2015, respectively. On average, F_TSM in the Padma River decreased at an annual rate of 3.3 mt (p < 0.01). The impact of human activities on C_TSM contributed more significantly to the changes in F_TSM (R = 0.76) than natural factors (R = 0.44). Due to a lack of water conservancy facilities within the river basin, changes in the water and soil retention capacity due to the changes in vegetation coverage were an important human factor (R = −0.79).

Dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) are the two dominant nutrients influencing seawater quality. Due to the non-optically active nature of nutrients and their regional variability, relying solely on optical data is inadequate for achieving high-precision remote sensing retrieval in complex marine environments. We developed a novel remote sensing algorithm for DIN and DIP using MODIS remote sensing reflectance (Rrs) products and the XGBoost machine learning framework. Beyond optical inputs, our model integrates sea surface temperature (SST) and spatiotemporal information, including a shoreline-based pixel location descriptor, which significantly enhances model performance. We generated monthly average distributions of DIN and DIP concentrations across China’s coastal waters from 2012 to 2022. The findings highlight extensive high-nutrient zones in the Bohai Bay and Changjiang River (Yangtze River) Estuary−Hangzhou Bay regions, with a notable declining trend in nutrient concentrations. The Zhujiang River (Pearl River) Estuary also exhibits elevated nutrient levels, albeit with minimal changes. This study pioneers the incorporation of dual-coordinate information in nutrient retrieval for complex marine environments, significantly improving model accuracy and addressing stripping artifacts associated with single-coordinate systems. Moreover, the results provide unprecedented spatiotemporal insights into nutrient distributions in China’s coastal waters, offering valuable support for marine environmental management and policy-making.

Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, based on the existing measured the total suspended matter flux (F_TSM) and discharge data for the Irrawaddy River, an inversion model of the total suspended matter concentration (C_TSM) is constructed for the Irrawaddy River, and the C_TSM and F_TSM from 1990 to 2020 are estimated using the L1 products of Landsat-8 OLI/TIRS and Landsat-5 TM. The results show that over the last 30 years, the F_TSM of the Irrawaddy River decreased at a rate of 3.9 Mt/yr, which is significant at the 99% confidence interval. An increase in the vegetation density of the Irrawaddy Delta has increased the land conservation capacity of the region and reduced the inflow of land-based total suspended matter (TSM). The F_TSM of the Irrawaddy River was estimated by fusing satellite data and data measured at hydrological stations. The research method employed in this paper provides a new supplement to the existing hydrological data for large rivers.