I applied C2RCC processor to OLCI data for Yellow Sea which is the coast of eastern China with extremely turbid water, but the result is not so satisfiying. I also applied C2RCC processor to Sentinel-2 MSI data for this coastal region and I selected the C2X set of neuro nets, the result is more realistic with the in situ Chl and TSM data. I have noticed that for OLCI the C2X neuro nets set can’t be selected, will it possible to add this option for OLCI in the next release of SNAP?
The C2X nets are not available for OLCI because the extreme ranges affected the performance of the processor on clearer waters. The ranges used for training neural nets for OLCI C2RCC sufficiently cover many situations in coastal and inland waters.
Hi ana,
I use c2rcc processing Sentinel 3 OLCI data has been some problems, I intend to change a method. I want to retrieve chlorophyll a in clean lake water, in addition to c2rcc, what is the atmospheric correction method for clean water?
Thanks!
You could see how the standard L2W products work in your area. Maybe OC4ME is already good enough for CHL extraction or the reflectances look ok if your waters are clear.
Dear Ana,
Thanks for your answer. Maybe Sen2Cor for L2, but Sen2Cor couldn’t use in water?
Yes! OC4ME is suitable for clean water but I’m so confused that is that an inversion model or an atmospheric correction method? Doesn’t OC4ME need atmospheric correct？C2RCC is a method of atmospheric correction right?
I want to extract the reflectance (such as rrs_1) of a certain point (such as 30E, 90N), and build a model to invert chlorophyll a.
Thanks a lot.