Chlorophyll a (chlor_a)

Table of Contents

  1. Product Summary
  2. Algorithm Description
  3. Previous Versions
  4. References
  5. Data Access

1 - Product Summary

This algorithm returns the near-surface concentration of chlorophyll-a (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 Level-2 OC product suite and the Level-3 CHL product suite.

The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multi-mission 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.

Global chlorophyll map from MODIS Aqua chlorophyll colorbar
MODIS Aqua chlor_a seasonal composite for Spring 2014

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 2-4 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:

  1. Chlorophyll concentration is first calculated using the CI algorithm, which is a three-band 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 (a0CI = -0.4287 and a1CI = 230.47) specified by Hu et al (2019), where:
    $$chlor\_a=10^{(a_{0_{CI}}+a_{1_{CI}}*CI)}$$
  2. Chlorophyll concentration is then calculated following the OCx algorithm, which is a fourth-order 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, a0-a4, are sensor-specific (Table 1).

  3. 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:
    $$chlor\_a =\frac{chlor\_a_{CI}(t_2-chlor\_a_{CI})}{t_2-t_1}+\frac{chlor\_a_{OCx}(chlor\_a_{CI}-t_1)}{t_2-t_1}$$
    with t1 = 0.25, and t2=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 Rrs to 555nm. That correction is as follows:

For spectral bands (λ0) in the range of 543 –567 nm, Rrs0) can be converted to Rrs(555) using the following equations:
If λ0 = 555±2nm,
$$ R_{rs}(555)=R_{rs}(\lambda_0)$$
If Rrs0) < sw,
$$R_{rs}(555)=10^{(a_1*log_{10}(R_{rs}(\lambda_0))-b_1)}$$
If Rrs0) ≥ sw,
$$R_{rs}(555)=a_2*R_{rs}(\lambda_0)-b_2$$
For different spectral bands (λ0), sw and a1, b1, a2, and b2 values are shown in table 1.

Table 1. Spectral band specific coefficients of sw, a1 b1, a2 and b2.
Spectral range (λ0,nm) sw a1, b1 a2, b2
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

The coefficients used for the OCx component of the algorithm in standard processing are listed in Table 2 below. In most cases these are taken directly from O’Reilley and Werdell (2019).

Table 2. Coefficients for the OCx algorithm series in standard processing.
sensor Algorithm OCx Rrs 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
VIIRS-SNPP OC3_VIIRS_SNPP, CI Rrs(443>486)/Rrs551 0.23548; -2.63001; 1.65498; 0.16117; -1.37247
VIIRS-NOAA20 OC3_VIIRS_NOAA20, CI Rrs(445>489)/Rrs556 0.28153; -2.65472; 1.30882; 1.31521; -2.08622
VIIRS-NOAA21 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

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 bio-Optical 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 three-band 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), 1524-1543, 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, 24937-24953, 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, 32-47. doi: 10.1016/j.rse.2019.04.021

5 - Data Access