Ocean chlorophyll concentration is a key parameter for characterizing marine primary productivity, ecological conditions, and biogeochemical processes. However, existing ocean chlorophyll products generally suffer from sparse vertical observations, insufficient spatiotemporal continuity, limited capability in resolving coastal structures, and inadequate high-spatial-resolution representation.

To address these issues, this study focuses on the reconstruction of a three-dimensional (3D) chlorophyll field and develops a technical framework that spans from global 3D background field reconstruction to localized high-precision 3D optimization. By integrating satellite remote sensing, environmental field data, and BGC-Argo profile observations, a global 3D chlorophyll reconstruction method is established, resulting in a dataset with improved spatiotemporal continuity and enhanced representation of vertical structures.

The results indicate that multi-source data fusion combined with physically constrained modeling can effectively improve the realism and resolution of the 3D chlorophyll field, providing robust data support for studies of ocean carbon cycling and regional biogeochemical processes.