Hi,
I’m working with Sentinel-2 L1C data and applying C2RCC to retrieve chlorophyll-a. The output only gives me products for this three— C2RCC_nets, C2X_nets, and C2X_complex_nets algorithms.
I’d like to know if there’s any way to get the OC4ME chlorophyll after running C2RCC, or if I need to use a different processor. I’m asking because I see a large gap between in-situ chl-a and the C2RCC-derived chl-a, and I want to compare with OC4ME for validation.
Currently, I’m using the default C2RCC settings in SNAP.
Any guidance or workflow suggestions would be really appreciated.
Thanks!
@Marco_EOM @abruescas
As far as I know OC4ME is only defined for Sentinel-3 OLCI but not for Sentinel-2.
C2RCC doesn’t provide OC4ME and it can’t be derived from the results.
Thanks so much for your kind reply. Hi, I’m a bit unsure about which atmospheric correction algorithm would be more suitable for my study region along the Western Australia coast — ACOLITE (top) or C2RCC (down). I’ve been using C2RCC with the default settings, but I recently came across someone’s results processed with ACOLITE ( Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach - ScienceDirect ), which look like the following (although the colour scales are not identical). This was on 2025/09/04 (the same date for both images).
Could you please help me understand the main differences between these two algorithms and advise which might be more appropriate for my region? @Marco_EOM @abruescas
