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Against the background of climate warming, marine heatwaves (MHWs) and terrestrial drought events have become increasingly frequent in recent decades. However, the combined effects of MHWs and terrestrial drought on CO2 uptake in marginal seas are still unclear. The East China Sea (ECS) experienced an intense and long-lasting MHW accompanied by an extreme terrestrial drought in the Changjiang basin in the summer of 2022. In this study, we employed multi-source satellite remote sensing products to reveal the patterns, magnitude, and potential drivers of CO2 flux changes in the ECS resulting from the compounding MHW and terrestrial drought extremes. The CO2 uptake of the ECS reduced by 17.0% (1.06 Tg C) in the latter half of 2022 and the Changjiang River plume region shifted from a CO2 sink to a source (releasing 0.11 Tg C) in July-September. In the majority of the ECS, the positive sea surface temperature (SST) anomaly during the MHW diminished the solubility of CO2 in seawater, thereby reducing CO2 uptake. Moreover, the reduction in nutrient input associated with terrestrial drought, which is unfavorable to phytoplankton growth, further reduced the capacity of CO2 uptake. Meanwhile, the CO2 sink doubled for the offshore waters of the ECS continental shelf in July-September 2022, indicating the complexity and heterogeneity of the impacts of extreme climatic events in marginal seas. This study is of great significance in improving the estimation results of CO2 fluxes in marginal seas and understanding sea–air CO2 exchanges against the background of global climate change. [ABSTRACT FROM AUTHOR]

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport. [ABSTRACT FROM AUTHOR]

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO2) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO2 and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST ≤ ±0.5 °C and Chla ≤ ±20%; RMSESST ≤ 0.011 and RMSEChla ≤ 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25° × 0.25° were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO2 products to understand the carbonate system change and the ocean ecological environments responding to the global change. [ABSTRACT FROM AUTHOR]