Red Noctiluca scintillans (RNS) is one of the major species causing red tides and bioluminescence, yet extremely difficult to track due to its ephemeral nature. Here, we show that RNS can be fingerprinted from satellite data due to its unique absorption and scattering properties. Retrospective analysis of Moderate Resolution Imaging Spectroradiometer data of 2000–2017 over the East China Sea reveals seasonal patterns and interannual changes as well as an increasing RNS bloom trend, all driven by hydrographical and biological factors and possibly by the construction of the Three Gorges Dam. Unlike earlier reports from field measurements, RNS blooms are found in both coastal and offshore waters of the ECS, to at least 126°E (> 330 km from the Yangtze River mouth). RNS blooms are also found in August and September where water temperature of 28–30 °C is well beyond the earlier reported range of 10–25 °C.

An empirical algorithm for estimating particulate organic carbon (POC) concentration in the surface ocean from satellite observations is formulated and validated using in situ POC data and remote‐sensing reflectance (R_rs) data obtained from match‐up satellite ocean color measurements. The algorithm builds upon the band‐difference algorithm concept, which was originally developed for estimating chlorophyll‐a concentration in clear waters. This algorithm utilizes three spectral bands centered approximately at 490, 550, and 670 nm to determine a color index (CI_POC), from which POC can be estimated from satellite measurements. For comparison, the blue‐green band‐ratio algorithm is also formulated using the same data set of in situ POC and satellite‐derived R_rs. Results show that the statistical parameters characterizing the differences between the satellite‐derived POC and matchup in situ POC are similar when the CI_POC and band ratio algorithms are applied to open ocean waters where the values of CI_POC are relatively low. In coastal waters where the values of CI_POC are generally higher, the statistical parameters of algorithm performance are better for the CI_POC algorithm. In addition, because the CI_POC algorithm is less sensitive to errors and noise in the satellite‐derived R_rs, the image quality obtained with this algorithm can be improved for both open‐ocean and coastal waters.

Particulate organic carbon (POC) plays an important role in coastal carbon cycling and the formation of hypoxia. Yet, coastal POC dynamics are often poorly understood due to a lack of long‐term POC observations and the complexity of coastal hydrodynamic and biogeochemical processes that influence POC sources and sinks. Using field observations and satellite ocean color products, we developed a new multiple regression algorithm to estimate POC on the Louisiana Continental Shelf (LCS) from satellite observations. The algorithm had reliable performance with mean relative error (MRE) of ∼40% and root mean square error (RMSE) of ∼50% for MODIS and SeaWiFS images for POC ranging between ∼80 and ∼1200 mg m⁻³, and showed similar performance for a large estuary (Mobile Bay). Substantial spatiotemporal variability in the satellite‐derived POC was observed on the LCS, with high POC found on the inner shelf (<10 m depth) and lower POC on the middle (10–50 m depth) and outer shelf (50–200 m depth), and with high POC found in winter (January–March) and lower POC in summer to fall (August–October). Correlation analysis between long‐term POC time series and several potential influencing factors indicated that river discharge played a dominant role in POC dynamics on the LCS, while wind and surface currents also affected POC spatial patterns on short time scales. This study adds another example where satellite data with carefully developed algorithms can greatly increase the spatial and temporal observations of important biogeochemical variables on continental shelf and estuaries.

The capacity of CO₂ uptake in the Chukchi Sea is particularly sensitive to rapid physical and biological changes. However, scarce field observations pose a challenge in understanding the long‐term trend of CO₂ uptake capacity on this continental shelf. We adopted a machine‐learning‐based approach to construct a 17‐years (2003–2019) long‐term time series of summer sea surface partial pressure of CO₂ (pCO₂) from remote sensing products. We show that the long‐term increase in CO₂ uptake capacity can be attributed to strong and enhanced biological uptake. In addition, the intraseasonal variability of surface pCO₂ in early summer confirms the crucial role of sea ice melt and the subsequently enhanced photosynthesis as soon as the surface ocean converts into an open system. Our results thus highlight the use of remote sensing data in interpolating/extrapolating the highly dynamic carbonate system in the continental shelf sea and shed light into future studies involving machine learning or algorithms.

Satellite‐based empirical models explaining hypoxic area and volume variation were developed for the seasonally hypoxic (O₂ < 2 mg L⁻¹) northern Gulf of Mexico adjacent to the Mississippi River. Annual variations in midsummer hypoxic area and volume were related to Moderate Resolution Imaging Spectroradiometer‐derived monthly estimates of river plume area (km²) and average, inner shelf chlorophyll a concentration (Chl a, mg m⁻³). River plume area in June was negatively related with midsummer hypoxic area (km²) and volume (km³), while July inner shelf Chl a was positively related to hypoxic area and volume. Multiple regression models using river plume area and Chl a as independent variables accounted for most of the variability in hypoxic area (R² = 0.92) or volume (R² = 0.89). These models explain more variation in hypoxic area than models using Mississippi River nutrient loads as independent variables. The results here also support a hypothesis that confinement of the river plume to the inner shelf is an important mechanism controlling hypoxia area and volume in this region.