Hi everyone!

I'm trying to figure out how to implement NASA's chlor_a algorithm in R for MODIS-Aqua, SeaWiFS, and VIIRS-SNPP. The OCx part of it has been no problem, but I'm having trouble finding which wavebands and coefficients to use for the Hu color index algorithm. I've been looking through the 3 sources below and gathering bits and pieces of information, but it would be nice to have a full table of sensor-specific wavebands and optimized coefficients.

NASA chlor_a algorithms
https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/

Hu et al. (2012)
Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three‐band reflectance difference
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JC007395

Hu et al. (2019)
Improving Satellite Global Chlorophyll-a Data Products Through Algorithm Refinement and Data Recovery
https://www.researchgate.net/publication/331098463_Improving_Satellite_Global_Chlorophyll_a_Data_Products_Through_Algorithm_Refinement_and_Data_Recovery

Can anyone point me in the right direction?

Thanks!

-Stephanie

The definitive reference for details of algorithms used by l2gen is the OCSSW source ($OCSSWROOT/ocssw_src/src/l2gen/get_chl.c).   Many of the coefficients used by l2gen come from sensor-specific parameter files so it is often easier if you can build l2gen from source locally and add some print statements.  If you have access to macOS or linux you can install the current source from the SeaDAS 8 GUI.   I think the instructions for installing the OCSSW software manually are outdated, but that may not matter for the Hu algorithm.

Building l2gen should be straightforward if you can arrange to use one of the OS versions used by NASA, e.g., in a VM.  Newer linux versions have removed some functions from glibc so the 3rd party sources won't build.

Stephanie,

The Hu CI does not have sensor specific coefficients.  The first reference you list identifies the  current coefficients (and bands) to use for the OCx alogrithms.

Coincidentally, I was just fiddling around with an analysis and wanted to apply the OCx/Hu algorithms to some insitu data...I used Python, but should be easy enough to translate to R.  Since you have OCx under control, here's what you can do for Hu and "OCI" (the blending we do operationally for our chlor_a product).  (The functions below are based on the code George referenced.)

Hu from the 2019 paper (the coefficients differ from the 2012 paper, but otherwise it's the same):
def hu(rrs443,rrs555,rrs670):
    w = np.array([443., 555., 670.])
    a = np.array([-0.4287, 230.47])
   #For the 2012 version use:
   # a = np.array([-0.4909, 191.6590])
    chl = np.NaN
    ci = np.min([rrs555 - (rrs443 + (w[1] - w[0]) / (w[2] - w[0])*(rrs670 - rrs443)), 0.0]);
   
    if ci <= 0.0:
        chl = np.power(10.0,(a[0] + a[1] * ci))
   
    return chl

To blend the OCx and Hu.  This uses the 2019 paper as well, but with a lower max threshold value because the Hu algorithm with the 2019 coefficients doesn't produce a value above about 0.37 mg/m3.  To mimic what we do operationally (until we reprocess), set t1=0.15; t2=0.20 :

def oci(hu,ocx):
    t1 = 0.25
    t2 = 0.35
    chl = np.NaN
    if ~np.isnan(hu):
        if (hu <= t1):
            chl = hu
        else:
            if (hu >= t2):
                chl = ocx
            else:
                chl = hu * (t2 - hu) / (t2 - t1) + ocx * (hu - t1) / (t2 - t1);
    else:
        chl = ocx

    return chl

The OCx coefficients for the reprocessing will also change to those from the O'Reilly and Werdell (2019) paper:
https://doi.org/10.1016/j.rse.2019.04.021

Hope this helps.

Sean