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%; RMSE_SST ≤ 0.011 and RMSE_Chla ≤ 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.

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.

The Changjiang River Estuary and the adjacent East China Sea is one of the world’s largest coastal carbon sinks, with a steadily increasing sink capacity over recent decades. However, the potential changes in its carbon sink and control mechanisms at decadal scales under climate change remain unclear. This study, based on 27 years (1998–2024) of continuous satellite remote sensing data, investigates the spatiotemporal distribution and long-term evolution of this coastal carbon sink. The results reveal a typical carbon sink with a capacity of −5.23 ± 3.73 mmol m⁻² d⁻¹ and significant seasonal variation. High-frequency remote sensing data reduces uncertainty compared to traditional shipborne observations. Over the past 27 years, the air–sea CO₂ flux increased at a rate of 0.24 mmol m⁻² d⁻¹ yr⁻¹, with a five-fold enhancement in carbon sink capacity. However, after atmospheric pCO₂ exceeded 400 μatm in 2014, the rate of increase slowed, indicating stabilization. Control mechanism analysis shows that biogeochemical processes have been persistently active, while over the past decade the influence of Changjiang discharge on seawater pCO₂ increased by 50%, shifting the system from primarily physical dilution to enhanced biogeochemical regulation. The findings provide insights into the evolution and management of coastal carbon cycles under climate change.

Phytoplankton fluorescence quantum yield (Φ _F ) reveals energy allocation in photosynthesis, and serves as a direct metric quantifying the proportion of absorbed light energy emitted as fluorescence. Traditional in situ Φ _F measurements are limited by sparse spatial and temporal coverage and cannot capture high‐frequency spatiotemporal variations. Based on long‐term, large‐area, hourly sea surface Φ _F retrieved from the Geostationary Ocean Color Imager (GOCI) over the East China Sea (2011–2020), this study systematically analyzed the hourly daytime variation patterns of phytoplankton Φ _F for the first time and explored their potential environmental controls. We constructed more than 4,000 quality‐controlled daytime series of Φ _F , chlorophyll‐ a , and instantaneous photosynthetically active radiation (iPAR) from GOCI's eight observations per day. Using a multi‐dimensional time series clustering method (K‐MDTSC), two dominant Φ _F daytime variation patterns were identified: Type I (U‐shape) and Type II (continuous decreasing). Analysis of the matched iPAR and modeled temperature and nutrient data suggested that light intensity and temperature are the primary environmental factors associated with these patterns: Type I dominates under strong summer irradiance, while Type II prevails under weak winter light and low seawater temperature, with both patterns coexisting in spring and autumn. Nutrient levels affect the Φ _F magnitude directly: under nutrient stress, reduced photosynthetic efficiency diverted more absorbed energy to fluorescence, thereby elevating Φ _F . Leveraging GOCI's high‐frequency and large‐area observation capabilities, this study establishes the first comprehensive perspective on phytoplankton Φ _F daytime variation patterns, providing mechanistic insights into phytoplankton physiological status and its response to environmental changes.