ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-453 The first consistent global gap-free daily 4 km dataset of particulate organic carbon, particulate organic nitrogen and their ratio (1998–2023) Zhang Yu 1 2 3 4 5 Liu Huizeng https://orcid.org/0000-0002-9018-985X 1 2 3 4 5 Liu Cong 6 Xu Nan 2 3 4 Yan Lin 1 2 3 4 Yang Chao 2 3 4 Wang Yongquan 1 2 3 4 Wu Guofeng 2 3 4 Li Qingquan 1 2 3 4 Institute for Advanced Study & Tiandu-Shenzhen University Deep Space Exploration Joint Laboratory & Space Science Center, Shenzhen University, Shenzhen,·518060, China MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, 518060, China Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen, 518060, China Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China Institute for Carbon Neutrality, Shenzhen University, Shenzhen, 518060, China State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China 29 06 2026 2026 1 28 Copyright: © 2026 Yu Zhang et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-453/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-453/essd-2026-453.pdf

Particulate organic carbon (POC), particulate organic nitrogen (PON) and their ratios (POC:PON) are key to understanding particulate organic matter (POM) and marine biogeochemical cycles. However, global observations remain insufficient, with existing products suffering from spatial and temporal gaps, inconsistent methodologies, and interruptions from clouds and satellite sampling limitations. Here, we present a global gap-free daily 4 km dataset of consistent POC, PON and POC:PON from January 1998 to December 2023 (GGFD-POM), generated using a concise retrieval then reconstruction workflow. POC and PON concentrations were retrieved from ocean color data (OC-CCI v6.0) and Copernicus reanalysis physics data using newly developed Gaussian Process Regression (GPR) models. The models were trained on ~3110 matchups between in-situ observations, OC-CCI bio-optical properties, and Copernicus physical properties, achieving <em>R</em><sup>2</sup> of 0.87 and 0.89, and RMSE of 1.47 mg m<sup>-3</sup> and 1.41 mg m<sup>-3</sup> for POC and PON, respectively. The Discrete Cosine Transform&ndash;Penalized Least Squares (DCT-PLS) approach was subsequently applied to reconstruct missing values in the satellite-retrieved POC and PON fields, resulting in gap-free global datasets. Based on these reconstructed products, a gap-free global POC:PON dataset was further derived. Validation using an independent in-situ dataset confirmed high accuracy of both satellite retrievals and reconstructions for POC, PON and POC:PON. Triple-collocation analysis (TCA) exhibited that the GGFD POC data outperform existing MODIS-Aqua and MULTIOBS POC datasets, reflecting the distinct strengths of the GGFD-POM product. Comparative analysis of spatiotemporal variations in POC, PON, and POC:PON further demonstrated that the gap-free dataset better captures trends and magnitudes, enhancing understanding of their roles in the global carbon and nitrogen cycles. The complete GGFD POM product dataset (1998&ndash;2023) is openly available at <a href="https://doi.org/10.11888/Ocean.tpdc.303488" target="_blank" rel="noopener">https://doi.org/10.11888/Ocean.tpdc.303488</a> (Zhang and Liu, 2026).