SGLI/GCOM-C1
Algorithm Theoretical Basic Document
Ocean net primary productivity (ONPP)

Version 3.3
31 March 2016

PI: Toru Hirawake1*
Co-I: Amane Fujiwara2, Tomonori Isada3 and Sei-ichi Saitoh1, 4
1Faculty of Fisheries Sciences, Hokkaido University
2
JAMSTEC
3
Field Science Center, Hokkaido University
4
Arctic Research Center, Hokkaido University*e-mail: hirawake@salmon.fish.hokudai.ac.jp

Contributors
Ryosuke Futsuki , Katsuhito Shinmyo2, Shintaro Takao3, Koji Suzuki3, Yuichi Nosaka4,and Yutaka W. Watanabe3
11PASCO Corporation, 2 NTT Data MSE Co., Ltd,
Faculty of Environmental Earth Sciences, Hokkaido University,
4Ishinomaki Senshu University
3This ATBD is based on Hirawake et al. "Light absorption-based primary productivity
model for ocean color remote sensing" preparing to submit to Remote Sens. Environ.

1. Algorithm outline
1.1 Algorithm Code
ONPP_V3.3.f95
1.2 Product Code
ONPP (Ocean net primary productivity, research level product)
1.3 PI
Toru Hirawake (GCOM-C1 2nd RA 133, 4th RA 304)
1.4 Overview of algorithm
This algorithm estimate column integrated daily net primary productivity as acarbon assimilation by photosynthesis of phytoplankton in the ocean.
Most of the algorithms developed in the past used chlorophyll a (chl a)concentration [Behrenfeld and Falkowski, 1997a]. However, estimation of chl aconcentration from satellite data has uncertainty due to the effect of pigment
packaging which leads to underestimation [Hirawake et al., 2000; Mitchell andHolm-Hansen, 1991] and of colored dissolved organic matter (CDOM) whichleads to overestimation [Matsuoka et al., 2007]. Another uncertainty is derivationof photosynthetic rate of phytoplankton. Although the vertically generalized
productivity model (VGPM) [Behrenfeld and Falkowski, 1997b] which is one of
the frequently used algorithms expressed the maximal photosynthetic rate (PBopt) asa function of sea surface temperature (SST), the SST derived PBopt had large error[Kameda and Ishizaka, 2005], particularly in the polar waters [Hirawake et al.,2011]. Furthermore, the photosynthetic rate should be an independent parameter on
the SST to discuss the effect of global warming to primary productivity in theocean.
To reduce these issues, absorption-based primary productivity model [Hirawakeet al., 2011] was developed. The model was based on the result of Marra et al.
[2007] who demonstrated that primary productivity normalized to absorption
coefficient of phytoplankton is relatively invariant in the world ocean, andapplying the relationship between primary productivity and absorption coefficientto the VGPM. Reduction in the effects of SST and chl a estimation in the SouthernOcean [Hirawake et al., 2011], and of CDOM in the Bering and Chukchi Seas
[Hirawake et al., 2012] has been confirmed. In this project, additional dataset inthe temperate waters were obtained and the absorption-based model was expanded.
Dataset to develop the algorithm includes the primary productivity and absorption
coefficient data from the Southern Ocean, Chukchi Sea (Arctic Ocean), Bering Sea,
North Pacific and Bermuda (n = 232) but does not include highly turbid coastal
area currently (Figure 1). The details of sampling and analysis for primary
productivity, absorption coefficients, pigments (HPLC and fluorometry), spectral
radiation were described elsewhere [Hirawake et al., 2012; Hirawake et al., 2011].
Data from the waters around Kuril Islands (cruise of R/V Multanovski in 2014,n=12) and time-series of primary productivity from Station ALOHA of the HOT
project are used for validations with in-situ and satellite (MODIS/aqua SMI,monthly) absorption data, respectively.

Figure 1. Map showing the location of sampling stations (red cicles).

2. Theoretical Description
2.1 Mathematical Description of Algorithm
Basic algorithm for the absorption-based model is the vertical generalized
productivity model (VGPM) which is a function of satellite derived chl a and SST

(Behrenfeld and Falkowski, 1997):

B×
PPeu = Popt

0.66125 × E 0× Z eu × C surf × Dirr ,E 0 + 4.1(1)

where PPeu, E0, Zeu, Csurf and Dirr is column integrated daily ocean net primary
productivity (ONPP, mg C m-2 d-1), daily incident irradiance in quantum unit (sameas photosynthetic available radiation, PAR, mol photons m-2 d-1), euphotic zone
depth determined as the depth at which PAR is 1% of the sea surface (m), sea
surface chl a concentration (mg chl a m-3) and day length (h), respectively. PBopt ischl a normalized optimal photosynthetic rate in the water column (mg C (mg chla)-1 h-1). The seventh-order polynomial function of SST was used to derive PBopt as
in Behrenfeld and Falkowski [1997b]. In this study, PBopt × Csurf (mg C m-3 h-1) wasreplaced by an empirical function of spectrally averaged absorption coefficient ofphytoplankton at the sea surface āph(0-) (m-1) expressed by following equation:
700

a ph (0−) =∫ a ph (λ , 0−) dλ400

700 - 400,(2)where āph(λ, 0-) is absorption coefficient of phytoplankton at each wavelength, λ,and a flat spectrum (constant over the visible region) of surface irradiance inquantum unit is assumed [Hirawake et al., 2011]. Note that homogeneous
distribution of vertical chl a concentration is assumed and its concentration is sameas the Csurf when applying this model.
For the SGLI/GCOM-C1, E0, Zeu and aph(λ, 0-) are given as products 'PAR', 'EZD'
and a part of 'IOP'. Dirr is calculated from latitude and date [Brock, 1981]. If theEZD (m) is deeper than sea bottom depth, the bottom depth is used instead of EZD.
Csurf is not necessary to input here. Although Hirawake et al. (2011, 2012) used
simple relationship between PBopt × Csurf (Popt) and āph(0-) or aph(443, 0-),algorithm in this project applied absorbed radiation by algae (Morel et al. 1991)
per day length (ARA, Futsuki et al. in preparation) instead of absorption
coefficients to derive Popt :𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑎𝑎�ph ×𝐸𝐸0 ×𝐷𝐷𝐷𝐷−1 .

(3)

Relationship between Popt and ARA has dependency on PAR due to difference inquantum yield under different light intensities and saturation of photosynthesis
under high radiation. Therefore, we separated the relationship to three ranges of E0:
Popt = 10 (2.89497 + 1.39751 × log10 ARA)
when 0 < E0 < 20 mol photons m-2 d-1,(4)Popt = 10 (1.74729 + 0.96122 × log10 ARA)
20 ≤ E0 < 40 mol photons m-2 d-1(5)Popt = 10 (1.3221 + 0.79609 × log10 ARA)
E0 ≥ 40 mol photons m-2 d-1.

(6)

Coefficient of determination (r2) of the three functions had 0.88 (n=74), 0.70(n=105) and 0.68 (n = 53), respectively (Figure 2a-c).

a)b)c)

Figure 2. Relationship between Popt and ARA. (a) 0 < E0 < 20, (b) 20 ≤ E0 < 40,(c) E0 ≥ 40 mol photons m-2 d-1.

As shown in Eq. (2), absorption coefficients over whole visible region arerequired to retrieve āph(0-). However, number of wavelength of the SGLI is quite
limited and estimation error in absorption coefficients at longer wavelengths morethan 500 nm is large. Therefore, āph(0-) was estimated empirically from aph(443,0-)to reduce missing pixels (Figure 3):āph(0-) = 0.59472 × aph(443, 0-)1.09856,(r2 = 0.987, n = 246).

(7)

Figure 3. Retrieval of āph(0-) from aph(443, 0-).

2.2 Validation
Reproducibility of the ONPP algorithm was confirmed using same dataset asthat for the algorithm development (Figure 4). Although the dataset includes datafrom various optical conditions such as the polar seas and coastal region, thealgorithm accounted for 78% and 64% of the observed variability in Popt and PPeu,respectively.

a)b)

Figure 4. Relationship between estimated and observed values of (a) Popt and (b)PPeu. Black line shows 1:1 relationship.
The ONPP algorithm was validated using the in situ data from the waters around
Kuril Islands (n = 12). As shown in Figure 5, estimated values from bothalgorithms satisfied a factor of 2 of measured values (RMSE = 0.230 and 0.287).

Figure 5. Comparison between in situ and estimated values of (a) Popt and (b) PPeu.
PPeu estimated using MODIS satellite data was also validated (Figure 6). Retrieved
PPeu using MODIS data coincided with in situ one until 2008. However,underestimation of PPeu was remarkable after 2008. We used previous release of
MODIS data (R2013) and degradation of satellite sensor had affected on the trends ofthe MODIS product. Underestimation is probably attributed to this phenomenon. NASA
has already released new version of the MODIS data (R2014.0) recently. Therefore, weshould validate using the new one, particularly for the data since 2008.

Figure 6. Comparison between observed and estimated primary production at Station
ALOHA.

3. References
Behrenfeld, M. J., and P. G. Falkowski (1997a), A consumer's guide to phytoplankton
primary productivity models, Limnol. Oceanogr., 42, 1479-1491.
Behrenfeld, M. J., and P. G. Falkowski (1997b), Photosynthetic rates derived fromsatellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1-20.
Brock, T. D. (1981), Calculating solar radiation for ecological studies, EcologicalModelling, 14(1-2), 1-19.
Hirawake, T., K. Shinmyo, A. Fujiwara, and S. i. Saitoh (2012), Satellite remote sensingof primary productivity in the Bering and Chukchi Seas using an absorption-based
approach, ICES Journal of Marine Science, 69(7), 1194-1204.
Hirawake, T., H. Satoh, T. Ishimaru, Y. Yamaguchi, and M. Kishino (2000), Bio-opticalrelationship of Case I waters: The difference between the low- and mid-latitude
waters and the Southern Ocean, Journal of Oceanography, 56, 245-260.
Hirawake, T., S. Takao, N. Horimoto, T. Ishimaru, Y. Yamaguchi, and M. Fukuchi(2011), A phytoplankton absorption-based primary productivity model for remotesensing in the Southern Ocean, Polar Biology, 34(2), 291-302.
Kameda, T., and J. Ishizaka (2005), Size-fractionated primary production estimation bya two-phytoplankton community model applicable to ocean color remote sensing,
Journal of Oceanography, 61, 663-672.
Marra, J., C. C. Trees, and J. E. O'Reilly (2007), Phytoplankton pigment absorption: A
strong predictor of primary productivity in the surface ocean, Deep-Sea Research I,
54, 155-163.
Matsuoka, A., Y. Huot, K. Shimada, S. I. Saitoh, and M. Babin (2007), Bio-opticalcharacteristics of the western Arctic Ocean: implications for ocean color algorithms,
Can J Remote Sens, 33(6), 503-518.
Mitchell, B. G., and O. Holm-Hansen (1991), Bio-optical properties of Antarctic
Peninsula waters: differentiation from temperate ocean models, Deep-Sea Research,
38, 1009-1028.