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The aim of Copernicus Climate Change Service (C3S) is to supply reliable climate data in support of strategies to adaptation and mitigation to climate change. The C3S provides access to high-quality climate data through its Climate Data Records (CDRs) of atmospheric, marine and land Essential Climate Variables (ECVs). Global Earth Surface Albedo (SA) satellite-based products are included in the land (biosphere) portfolio. SA is a magnitude which quantifies the fraction of solar energy reflected by the surface of the Earth. This paper details the retrieval methodology and preliminary validation results for global estimates of surface albedo based on Sentinel-3 observations for the C3S ECVs data (C3S SA v3.0). The retrieval algorithm exploits the synergistic use of the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) on-board Sentinel-3 A and B satellites. Firstly, the atmospherically corrected reflectances are generated in the Copernicus Global Land Service framework. After that, the Bidirectional Reflectance Distribution Function (BRDF) inversion module concludes the BRDF model parameters, which are transferred to the angular integration module in order to generate spectral albedo quantities for the selected OLCI (Oa03, Oa04, Oa07, Oa17 and Oa21) and SLSTR (S1, S2, S5 and S6) bands. At the end, the spectral integration module generates broadband albedo quantities in three different standard broadband spectral regions (visible [0.4μ m − 0.7μ m ], near infrared [0.7μ m − 4μ m ] and total shortwave [0.3μ m − 4μ m ]). Preliminary validation results over 10-months demonstration period (July 2018–April 2019) show, in terms of spatial and temporal consistency, that C3S Sentinel-3 SA global estimates reached in general good agreement as compared to other satellite operational references derived from MODIS (MCD43A3 C6) and PROBA-V (C3S PROBA-V SA v1.0) acquisitions. The comparison with ground data shows similar results to the MCD43A3 C6 comparisons but opposite sign in differences (marginally positive in case of Sentinel-3), with accuracy of 0.005 (3.7%), precision of 0.016 (11.3%) and uncertainty of 0.032 (22.7%). Our results have demonstrated the feasibility to estimate global fields of SA from Sentinel-3 observations, with similar quality of existing operational products. These Sentinel-3 based SA datasets will give the continuity to the existing C3S SA CDR, introducing improvements in terms of spatial resolution (300 m) and spectral information (9 spectral albedos) in contrast to previous datasets based on Advanced Very High Resolution Radiometer (AVHRR; 4 km, 4 channels) and Vegetation instruments (VGT; 1 km, 4 channels). • Retrieval method for global surface albedo estimates from Sentinel-3 is proposed. • Algorithm is based on BRDF inversion, angular and spectral integration. • A cross-comparison with MODIS and PROBA-V satellite products is performed. • The direct validation includes comparison with spatially representative ground data. [ABSTRACT FROM AUTHOR]

Phytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surface in-situ dataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers. [Display omitted] • Compile the largest global in-situ dataset of total and size-fractionated Chl-a. • Advance SST-dependent model of phytoplankton size structure for global application. • Model parameters have changed over the last 40 years globally. • Concerns raised about satellite-derived Chl-a trends using standard algorithms. [ABSTRACT FROM AUTHOR]