Conference Agenda

Satellite-derived particulate backscattering (b_bp) provides key insights into large-scale ocean biology and biogeochemistry, serving as a proxy for phytoplankton biomass and particulate organic carbon. These data underpin estimates of carbon stocks, fluxes, and productivity in coupled physical–biogeochemical models. However, the paucity of in situ multi-band b_bp measurements limits robust uncertainty assessment of satellite products (Brewin et al., 2023). To address these observational gaps, Surface Velocity Programme (SVP) drifting buoys, historically used to validate SST and SSS, have been equipped with bio-optical and oxygen sensors, defining the Biogeochemical-SVP drifters (BGC-SVP). The high sampling frequency combined with a Lagrangian approach enables it to overpass numerous pixels in a single day, thus providing large in situ datasets for validation activities of b_bp. In the context of ESA INSPIRE project, here, we present results for the first comparison of satellite and in-situ b_bp using observations from BGC-SVP Lagrangian drifters which provide b_bp at 470 and 532 nm. To this aim, we tested different algorithms (e.g. QAA, GSM) and satellite sensors (e.g., PACE/OCI, Sentinel-3/OLCI) to quantitatively evaluate the performance of the retrievals. The in-situ data derived entirely from the first BGC-SVP drifter array deployed in the Mediterranean Sea during the ITINERIS’EYES cruise performed in July 2025. In the next future, a coordinated array of BGC-SVP drifters, BGC-Argo floats, and other autonomous platforms working in synergy could provide the required surface and subsurface data at the temporal, spatial, and spectral (e.g., multi- or hyperspectral resolution) scales of interest to future satellite missions.

Particulate organic carbon is central to oceanic carbon export and biogeochemical cycling, yet robust global observations of its mass concentration (POC) remain challenging due to limitations in remote sensing and in situ techniques. The expanding BGC-Argo float array can support integration of surface POC estimates from satellite remote-sensing reflectance R_rs with subsurface observations, but consistent algorithms are essential to avoid platform-induced biases. This study evaluates the performance of a multivariable POC algorithm, referred to as Model-B (Koestner et al., 2024), that uses the particulate backscattering coefficient b_bp and concentration of chlorophyll-a (Chla) as inputs, and is applicable to both BGC-Argo float and satellite observations with a 2-step approach. Three matchup datasets are explored: in situ R_rs and in situ POC (N = 509), satellite R_rs and in situ POC (N = 223), and satellite R_rs and BGC-Argob_bp(700) and Chla (N = 4448). For estimating POC from R_rs, the Model-B input of b_bp(700) is derived using QAA-v5 using either MODIS-Aqua or in situ R_rs, and the Chla input is estimated with the OCI algorithm. Independently, POC is also derived from Model-B using vertically-resolved b_bp(700) and Chla from scattering and fluorescence sensors on BGC-Argo floats. Initial results show that the multivariable Model-B performs comparably across both in situ and satellite matchup datasets to the R_rs-based hybrid POC algorithm developed for global satellite applications (Stramski et al., 2022). Some positive biases for Model-B at low POC occur which are likely driven by uncertainties in b_bp(700) and Chla inputs. For the MODIS–BGC-Argo matchups, Model-B estimates from BGC-Argo floats in the surface layer show promising consistency with satellite hybrid POC algorithm results, particularly when satellite-derived Chla is used instead of float-based fluorescence estimates of Chla. Further evaluation is ongoing to assess regional and temporal agreement, refine BGC-Argo Chla fluorescence corrections, and explore merging satellite and BGC-Argo data into a 3-D POC product.