Chlorophyll a (chlor_a)
Table of Contents
1  Product Summary
This algorithm returns the nearsurface concentration of chlorophylla (chlor_a) in mg m^{3}, calculated using empirical relationships derived from in situ measurements of chlor_a and remote sensing reflectances ($R_{rs}$). The implementation is contingent on the availability of three or more sensor bands spanning the 440  670 nm spectral regime. The algorithm is applicable to all current ocean color sensors. The chlor_a product is included as part of the standard Level2 OC product suite and the Level3 CHL product suite.
The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multimission ocean color processing, is a blend between the updated OC3/OC4 (OCx) band ratio algorithm (O'Reilly and Werdell 2019) and the color index (CI) of Hu et al. (2019). The combined algorithm and theoretical basis is described in Hu et al. (2019), and the current implementation is detailed below.
Algorithm Point of Contact: P. Jeremy Werdell, NASA Goddard Space Flight Center; Chuanmin Hu, University of South Florida
2  Algorithm Description
Inputs:
$R_{rs}$ at 24 wavelengths between 440 and 670nm
Outputs:
chlor_a, concentration of chlorophyll a in mg m^{3}
Approach:
The current standard chlor_a product is based on the algorithm of Hu et al.(2019), which combines an empirical band difference approach at low chorophyll concentrations with a band ratio approach at higher chlorophyll concentrations. The band difference approach is the color index or CI (Hu et al. 2019), and the band ratio approach is based on the OCx series of algorithms introduced in O'Reilley et al 1998, with updated coefficients from O'Reilley and Werdell (2019).
The algorithm proceeds as follows:
 Chlorophyll concentration is first calculated using the CI algorithm, which is a threeband reflectance difference algorithm employing the difference between sensor specific Rrs in the green band and a reference formed linearly between Rrs in the blue and red bands (bands are instrument specific  see Table 1):
$$CI = R_{rs}(\lambda_{green})  [R_{rs}(\lambda_{blue}) + (\lambda_{green}\lambda_{blue)}/(\lambda_{red}\lambda_{blue}) * (R_{rs}(\lambda_{red})R_{rs}(\lambda_{blue}))]$$
Final calculation of CI chlorophyll is done using two coefficients (a_{0CI} = 0.4287 and a_{1CI} = 230.47) specified by Hu et al (2019), where:
$$chlor\_a=10^{(a_{0_{CI}}+a_{1_{CI}}*CI)}$$

Chlorophyll concentration is then calculated following the OCx algorithm, which is a fourthorder polynomial relationship between a ratio of $R_{rs}$ and chlor_a, where:
$$log_{10}(chlor\_a) = a_0 + \sum\limits_{i=1}^4 a_i \left(log_{10}\left(\frac{R_{rs}(\lambda_{blue})}{R_{rs}(\lambda_{green})}\right)\right)^i$$
where the numerator, $R_{rs}(\lambda_{blue})$, is the greatest of several input $R_{rs}$ values and the coefficients, a_{0}a_{4}, are sensorspecific (Table 1). In most cases these are taken directly from O’Reilley and Werdell (2019).
Table 1. Coefficients for the OCx algorithm series in standard processing. sensor Algorithm OCx R_{rs} used
(blue/green)a(0,1,2,3,4) SeaWiFS OC4, CI Rrs(443>489>510)/Rrs555 0.32814; 3.20725; 3.22969; 1.36769; 0.81739 MODIS OC3M, CI Rrs(443>488)/Rrs547 0.26294; 2.64669; 1.28364; 1.08209; 1.76828 VIIRSSNPP OC3_VIIRS_SNPP, CI Rrs(443>486)/Rrs551 0.23548; 2.63001; 1.65498; 0.16117; 1.37247 VIIRSNOAA20 OC3_VIIRS_NOAA20, CI Rrs(445>489)/Rrs556 0.28153; 2.65472; 1.30882; 1.31521; 2.08622 VIIRSNOAA21 OC3_VIIRS_NOAA21, CI Rrs(445>488)/Rrs555 0.24765; 2.54926; 1.55323; 0.39485; 1.54632 MERIS OC4E, CI Rrs(443>489>510)/Rrs560 0.42487; 3.20974; 2.89721; 0.75258; 0.98259 OCTS OC4O, CI Rrs(443>489>516)/Rrs565 0.54655; 3.51799; 3.39128; 0.91567; 0.97112 GOCI OC4,CI Rrs(412>443>489)/Rrs555 0.28043; 2.49033; 1.53980; 0.09926; 0.68403 HAWKEYE OC4, CI Rrs(447>488>510)/Rrs556 0.32814, 3.20725,3.22969, 1.36769, 0.81739 OLCI OC4, CI Rrs(443>490>510)/Rrs560 0.42540; 3.21679; 2.86907; 0.62628; 1.09333 CZCS OC3, CI Rrs(443>520)/Rrs555 0.31841; 4.56386; 8.63979; 8.41411; 1.91532 
For chlorophyll retrievals below 0.25 mg m^{3}, the CI algorithm is used.
For chlorophyll retrievals above 0.35 mg m^{3}, the OCx algorithm is used.
In between these values, the CI and OCx algorithm are blended using a weighted approach where:
$$chlor\_a =\frac{chlor\_a_{CI}(t_2chlor\_a_{CI})}{t_2t_1}+\frac{chlor\_a_{OCx}(chlor\_a_{CI}t_1)}{t_2t_1}$$
with t_{1} = 0.25, and t_{2}=0.35 (edges of the current blending region).
For the CI algorithm, the nearest band to 443, 555, and 670nm is used for the blue, green, and red band, respectively, for all sensors. For sensors that do not have a band very close to 555nm, a correction is performed to shift the nearest green band R_{rs} to 555nm. That correction is as follows:
For spectral bands (λ_{0}) in the range of 543 –567 nm, R_{rs}(λ_{0}) can be converted to R_{rs}(555) using the following equations:
If λ_{0} = 555±2nm,
$$ R_{rs}(555)=R_{rs}(\lambda_0)$$
If R_{rs}(λ_{0}) < sw,
$$R_{rs}(555)=10^{(a_1*log_{10}(R_{rs}(\lambda_0))b_1)}$$
If R_{rs}(λ_{0}) ≥ sw,
$$R_{rs}(555)=a_2*R_{rs}(\lambda_0)b_2$$
For different spectral bands (λ_{0}), sw and a_{1}, b_{1}, a_{2}, and b_{2} values are shown in table 2.
Spectral range (λ_{0},nm)  sw  a_{1}, b_{1}  a_{2}, b_{2} 

543  547  0.001723  0.986; 0.081495  1.031; 0.000216 
548  552  0.001597  0.988; 0.062195  1.014; 0.000128 
558  562  0.001148  1.023; 0.103624  0.979; 0.000121 
563  567  0.000891  1.039; 0.183044  0.971; 0.000170 
3  Previous Versions
Briefly, both chlorophyll products are computed and then blended with transition between the CI and OCx occurs at 0.25 < CI < 0.35 mg m^{3}. In some cases, where no update to OCx band ratio algorithm was offered by O'Reilly and Werdell 2019, coefficients stayed the same as in previous implementations. Older implementations were using O'Reilly et al. (1998) approach on deriving the coefficients from version 2 of the NASA bioOptical Marine Algorithm Data set (NOMAD), merged with CI, using coefficients by Hu et al (2012), with the same transition zone (0.15 < CI < 0.2).
4  References
Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on threeband reflectance difference . Journal of Geophysical Research, 117(C1). doi: 10.1029/2011jc007395
Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., & Proctor, C. W. (2019). Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. Journal of Geophysical Research: Oceans, 124(3), 15241543, doi: 10.1029/2019JC014941
O'Reilly, J.E., Maritorena, S.,Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., & McClain, C. R. (1998). Ocean color chlorophyll algorithms for SeaWiFS, Journal of Geophysical Research 103, 2493724953, doi: 10.1029/98JC02160.
O'Reilly, J.E., & Werdell, P. J. (2019). Chlorophyll algorithms for ocean color sensors  OC4, OC5 & OC6. Remote Sensing of Environment, 229, 3247. doi: 10.1016/j.rse.2019.04.021
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