Traditionally, passive ocean colour images have been used to provide a sustained synoptic view of the distribution of ocean optical properties and colour and biogeochemical parameters continuously for the past 20+ years. These images have revolutionized our view of the distribution of chlorophyll-a concentration, a proxy of phytoplankton. Ocean colour remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties within the ocean surface layer. However, these measurements have some limitations. Specifically, the measured property is a weighted-integrated value over the first tens of meters of the ocean upper-layer. It provides no information during the night, and retrievals are compromised by clouds, absorbing aerosols and low Sun zenithal angles. Major advances in our understanding of global ocean ecosystems require complementary measurements made by new technologies such as Lidar.
Dedicated to the monitoring of wind profiles at global scale with a view to improve weather forecast accuracy, the ADM-Aeolus mission was successfully launched in August 2018. ALADIN aboard ADM-Aeolus is the first space-borne UV Lidar using the HSRL (High Spectral Resolution Lidar) techniques, which means that the backscatter and attenuation coefficients can be directly estimated. Since ALADIN is unique in the space-borne Lidar landscape, there has been no attempt to derive ocean colour parameters from such instruments so far. Nevertheless, the ALADIN UV band can provide new information on ocean colour.
This study is developed in the framework of the ESA Aeolus+Innovation programme and is articulated around two core goals in order to: 1) demonstrate the Aeolus potential to measure the subsurface ocean particulate backscattering and the diffuse attenuation coefficient parameters (bbp and Kd, respectively) in the UV, and 2) assess the Aeolus potential to estimate biogeochemical parameters linked to ocean colour and ocean carbon cycle, in particular the coloured Dissolved Organic Matter (CDOM), the particulate organic carbon (POC) and the phytoplankton carbon (Cphyto).
To meet the first goal, a processing algorithm is developed taking advantage of the HSRL capabilities. Based on the HSRL equations, the “Lidar-derived optical” parameters (i.e. the particulate attenuated backscatter and the attenuation coefficient) can be computed under some assumptions. Then the key ocean optical parameters (i.e. bbp and Kd) are retrieved from the Lidar-derived optical parameters and they are used to derive the biogeochemical parameters (i.e. CDOM, POC and Cphyto). The processing scheme will be described including the impact of the specular reflection on the surface and the whitecaps. A first validation is presented over the Cabo Verde coastal waters. Optical and bio-optical parameters were measured in September 2021 during the CADDIWA campaign.
Finally, in some areas of interest such as sub-tropical gyres and polar regions, the ocean optical and biogeochemical products derived from ADM-Aeolus are evaluated against independent comparable products, both in the UV (TROPMI-S5P) and visible (MODIS-Aqua, OLCI-S3, CALIOP-CALIPSO) spectra.

High spectrally resolved satellite data are a source of the top of the atmosphere radiance signal which can be used for novel algorithms aimed for observations of phytoplankton groups biomass and the spectral composition of the light-lit ocean. Atmospheric sensors such as SCIAMACHY, GOME-2 and OMI have proven in the past to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field. However, the use of these data sets was limited by their temporal and spatial resolution mostly not meeting requirements for time series studies. Within the ESA Sentinel-5p+ Innovation project S5POC, we explore Sentinel-5P instrument TROPOMI's potential for deriving the diffuse attenuation coefficient and the quantification of different phytoplankton groups. As commonly used for the retrieval of atmospheric trace gases, we apply the differential optical absorption spectroscopy combined with radiative transfer modeling (RTM) to infer these oceanic parameters. We present results on a measure describing the diminishing of incoming radiation in the ocean with depth, the diffuse attenuation coefficient Kd. Kd is derived by the retrieval of the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI in the UV and blue spectral range which then is further converted to the associated Kd using RTM. The final TROMPOMI KD data sets resolved for three spectral regions (UV-B+short wave UV-A, UV-A and short blue) agree well with in situ data sampled during an expedition with RV Polarstern in 2018 in the tropical, temperate and polar Atlantic Ocean. Further, Kd-blue compared to wavelength-converted Kd(490 nm) products (OLCI-A and the merged OC-CCI) from common, multispectral, ocean color sensors, show that differences between the three data sets are within uncertainties given for the OC-CCI product. TROPOMI’s potential for retrieving phytoplankton groups is also explored for the Atlantic open ocean and, additionally, for the Portuguese coast and coast and British Columbia, Canada coast. Comparison to independent phytoplankton groups biomass data derived from in-situ pigment data and similar satellite products (CMEMS global PFT product based on Xi et al. 2021 and OCPFT algorithm following Losa et al. 2017 applied to OLCI-Chla and OC-CCI data sets) show reasonable agreement for most groups. Having established these new TROPOMI products, the next steps are to investigate global products over the full operating period of TROPOMI to assess the temporal and spatial stability of the products. Perspectively, these data products delivering information on the spectral underwater light and phytoplankton composition can be used as auxiliary information for modeling marine ecosystem/biogeochemical functioning or photochemical reaction rates of climatically important compounds and inhibition of primary productivity.

NASA’s PACE and GLIMR missions will provide the first hyperspectral ocean colour-quality observations from sun-synchronous and geostationary orbits, respectively. An overview of their instrument designs, capabilities, ocean science and applications objectives, and data products will be presented. With the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launching early in 2024, NASA will embark upon a new era of unprecedented global ocean colour and atmospheric satellite data products. PACE will be capable of performing radiometric and polarimetric ocean and atmosphere observations, returning a range of biogeophysical data from which properties of the ocean and atmosphere can be produced to add to other critical climate and Earth system variables. With advanced global remote sensing capabilities of continuous and high spectral resolution radiances from the ultraviolet to the near-infrared (320-890 nm) and multiple short-wave infrared bands, PACE’s high-quality observations will enable advanced understanding of ocean ecosystems, vulnerable coastal regions, quantification of ocean carbon cycling and phytoplankton community composition, cross-disciplinary studies of aerosol and ocean interactions, and unparalleled characterization of ecosystem change, function, and water quality.

NASA recently initiated the scientific investigation and instrument build for the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR), a hyperspectral ocean colour sensor launching after 2026 that will target the Gulf of Mexico and other coastal and ocean waters of North and South America. At the heart of the GLIMR instrument is a spectrometer with a ground sample distance of 300 m, continuous spectral range of 340 to 1040nm, and capability to scan the Gulf of Mexico in about 70 minutes. With its vantage point from geostationary orbit, GLIMR will be the first hyperspectral ocean color sensor in the Western Hemisphere to study ocean processes at the diurnal timescales required to observe the dynamic ecological, biogeochemical, and physical processes typical of coastal and ocean waters. The two main science goals of GLIMR are (1) to understand the processes contributing to rapid changes in phytoplankton growth rate and community composition, and (2) quantify how high frequency fluxes of sediments, organic matter, and other materials between and within coastal ecosystems regulate the productivity and health of coastal ecosystems. The science applications objectives aim to study the formation, magnitude, and trajectories of harmful algal blooms, oil spills, and Sargassum accumulation events.

Ocean color in remotely sensed imagery is indicative of water depth and can be combined with in situ data to estimate benthic depth and model bathymetric surfaces. However, this has yet to be fully exploited at the regional scale due to the limitations faced in collecting sufficient in situ data for model calibration and validation. Here, we provide a method for combining Sentinel-2 optical imagery and ICESat-2 spaceborne lidar for overcoming this challenge. Discrete benthic depths are extracted from ICESat-2 data and combined with Sentinel-2 composite imagery to create spatially continuous bathymetric models for three study locations, comprised of the Bay of Biscayne (Florida), Gulf of Chania (Crete) and island nation of Bermuda. Image composites were created using the Google Earth Engine, using the 20th percentile reflectance per-pixel value from an image stack, in order to reduce image artifacts such as sun glint and turbidity. Both Top-Of-Atmosphere and Surface Reflectance imagery were compared in order to determine the costs and benefits of advanced optical image correction on model performance. We tested the two primaryreflectance-depth algorithms of Stumpf and Lyzenga, as well as Support Vector Machine (SVM) regression to determine the strongest performing algorithm. Across all of the study sites we successfully created 10 m spatial resolution spatially continuous nearshore ocean bathymetric surface models from Sentinel-2 imagery, trained with ICESat-2 derived depths. We achieved depths of up to 26 m with a low RMSE of 10%. We determined that Surface Reflectance data did not outperform TOA data, making advanced time and computationally expensive correction unnecessary to achieve accurate models. Across all study sites, the Lyzenga method outperformed other algorithms with the SVM algorithm consistently performing the most poorly. Independent NOAA Digital Elevation Models (DEMs) and in situ single sonar beam data were used to validate the models in Biscayne Bay/Bermuda and Gulf of Chania, respectively. The independent model at Bermuda was determined to be of inferior quality to the Sentinel-2 derived model, thus a purely spaceborne approach was developed which used ICESat-2 derived depths as both calibration and validation data. This was an important step to achieving a purely spaceborne estimate of nearshore coastal bathymetry and ecosystem structure. Coastal seascapes, composed of mangroves, seagrasses, coral reefs and tidal flats support a range of critical ecosystems. They support billions of livelihoods and generate billions of dollars in revenue. They provide 25% of the oceanic carbon pool and support as much as 25% of global biodiversity. However, our current lack of knowledge on coastal ecosystem structure prevents them from being adequately accounted and monitored. We provide a purely spaceborne method for the wall-to-wall mapping of sub-aquatic structure at 10 m spatial resolution, in order to overcome this knowledge gap. Moving forward, we also provide results from a preliminary automated workflow that can automatically detect surface depths from ICESat-2 data and create bathymetric maps on a per-scene basis, thus creating spatially continuous maps of uncertainty. Additional advances include the use of advanced bootstrapped machine learning models that performed with high accuracy. Our goal is to use spaceborne derived maps of nearshore coastal bathymetry to improve current estimates of sub-aquatic topography and facilitate important accurate submerged ecosystem accounting.

In the last two decades, the East China Sea and its adjacent waters have witnessed radical shifts in the composition of the pelagic plant community which previously comprised of phytoplankton year-round to macroalgae (Ulva prolifera and Sargassum honeri) in a significant part of the year. A large scale U. prolifera outbreaks in the northwestern part of the Yellow Sea in every June-July since 2008. Its floating canopies occupy more than 30,000 km2. S. honeri outbreaks in the wide area of the continental shelf of the East China Sea in April-May since 2012. The landing of these macroalgae at the shore disrupts intensive and extensive enclosure culture of finfish and shellfish, and hanging net macroalgae culture industries in coastal inlets, ship traffic in small harbors, and the shoreline tourism industry. These large-scale abrupt marine biological events will further aggravate already stressed coastal waters with y with recurring harmful dinoflagellate blooms such as Cochlodinium polykrikoides, Gyrodinium moestrupii and marine heatwaves.

A more frequent local and basin-wide observation is increasingly required to address these basin-scale extreme marine biological events for the protection of coastal aquaculture, seashore tourism industry, and small harbor operations. GOCI has an advantage over other ocean color sensors in that it collects images every hour during the day. It enables tracking the temporal evolution of those plant blooms throughout their lifetime. GOCI II has offered 12 narrow bands (380-865 nm) to observe a subtle color change occurring at the sea surface. In situ measured Rrs spectra indicate that dinoflagellate blooms exhibit depressed reflectance in the 412-660nm and enhanced reflectance at 680-709nm. Geostationary ocean color imager (GOCI) sensor was found to distinguish abrupt dinoflagellate bloom from diatom bloom or non-bloom state. We will embark GOCI-IMBeR study group to observe and develop a database over the local area and the full-disk area for the coming decades along with other marine observation platforms (e.g., ESA satellites). The group membership is open-ended to all who have scientific interests.