Estimates of the dissolved organic carbon concertation from remote sensing in the frame of the OCROC project.

Abstract

Carbon monitoring from space is critical for the reporting and verification of carbon stocks and changes in both coastal and open ocean waters. In the frame of the OCROC project, funded by the Copernicus 2 – 1st Service Evolution Call for Tenders (2022-2024), we focus on the particulate (POC) and dissolved (DOC) organic carbon of surface oceanic and coastal waters, which represent the two components of the total organic carbon (TOC) pool in the ocean. The present presentation is mainly dedicated to the estimation of DOC, the main contributor to TOC, over open ocean waters. An enhanced version of the Ocean and Land Color Instrument's (OLCI) DOC algorithm of Bonelli et al. (2022) is presented and adapted to historical and present ocean color sensors. This algorithm employs two different Artificial Neural Network (ANN) algorithms depending on the Optical Water Classes, and four input parameters namely the absorption coefficient of Colored Dissolved Organic Matter (acdom(443)) chlorophyll-a concentration (Chl-α),  Sea Surface Temperature (SST), and Mixed Layer Depth (MLD). In this new version of the algorithm SST and MLD are both delivered by COPERNICUS (Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing).  Each of the four input parameters is provided at a distinct time lag to enhance the accuracy of the model. Furthermore, a revisited “match-up” database, compared to the one used in Bonelli et al. (2022), is utilized to validate the algorithm across multiple ocean color missions.