Bias-corrected remote sensing surface chlorophyll-a concentration datasets

Merged remote sensing chlorophyll-a (chl-a) concentration datasets available online often suffer from temporal inconsistencies, which limit their suitability for robust analyses of long-term changes under climate change. This repository provides eight bias-corrected versions of four widely used chlorophyll-a datasets: OC-CCI v6.0, GlobColour-AVW, GlobColour-CMEMS, and Yu2023. A full description of the datasets and the correction procedures is provided in our paper "North-South asymmetry in subtropical phytoplankton response to recent warming". Two types of correction methods are applied: TGDM: spatio-temporal inconsistencies corrected using the Temporal Gap Detection Method (van Oostende et al., 2022). FILL: inconsistencies corrected through statistical gap filling. Characteristics of the datasets : Regular grid with a 1° resolution Time resolution : monthly Period : 1998-2023 References : van Oostende, M., Hieronymi, M., Krasemann, H., Baschek, B. & Röttgers, R. Correction of inter-mission inconsistencies in merged ocean colour satellite data. Front. Remote Sens. 3, (2022).