Chlorophyll concentration from MODIS
- Coverage, spatial and temporal resolution
- Data quality
- Contact person
- Data citation and License
RESTRICTED: This link to the data set is only available for a restricted user group. The data set is only accessible in CEN/MPI net or accessible from external nets with a customer account. Please contact ICDC if you would like to access this data from outside the network.
- View chlorophyll data at LAS: AQUA | TERRA
- Access chlorophyll data via OPeNDAP
- AQUA Data access via file system: /data/icdc/ocean/modis_aqua_chl
- TERRA Data access via file system: /data/icdc/ocean/modis_terra_chl
Here we offer the level-3 MODIS chlorophyll-a concentration and other parameters (see parameters table) derived from MODIS collection 6.0 radiances applying the v2018.0 reprocessing.
Solar radiance penetrates into the water. Its absorption depends, among other factors, on the amount of chlorophyll-a stored in the cells of the plankton. By measuring the water-leaving radiance at two different wavelengths of which one is at the absorption band of chlorophyll-a one can estimate the chlorophyll-a concentration.
Data offered here are based on the Ocean Color Chlorophyll (OC3) v6 algorithm. It combines calibrated radiances measured by MODIS at two wavelengths of which one is in the blue and the another is in the green part of the electromagnetic spectrum. Chlorophyll-a concentration is derived using a fourth order polynomial based on the ratio of the blue to the green channel radiances in logarithmic scale. The algorithm is suited for so-called Case 1 waters (low concentrations of sediments and yellow substances). We also offer chlorophyll-a concentration based on the OCI algorithm by Hu et al. (2012).
More information is given in the literature as well as on the OceanColour Technical Documents website, see references .
We offer data of both satellites, EOS-TERRA and EOS-AQUA.
Last update of data set at ICDC: April 21, 2022.
|Chlorophyll-a concentration OC3 algorithm||mg/m³||additionally in unit dB|
|Chlorophyll-a concentration OCI algorithm||mg/m³||additionally in unit dB|
|Instantaneous photosynthetically available radiation (iPAR)||Einstein/m²s||in the water|
|Photosynthetically available radiation (PAR)||Einstein/m²day||in the air|
|Particulate Organic Carbon (POC)||mg/m³||additionally in unit dB|
|Particulate In-Organic Carbon (PIC)||mg/m³||additionally in unit dB|
|Diffuse attenuation coefficient at 490 nm||1/m||additionally in unit dB|
Period and temporal resolution:
- Daily: 2002-07-04 to 2022-03-31 (EOS-AQUA)
- Daily: 2000-02-25 to 2022-03-31 (EOS-TERRA)
- Monthly: 2002-07 to 2022-03 (EOS-AQUA)
- Monthly: 2000-02 to 2022-03 (EOS-TERRA)
Coverage and spatial resolution:
- Spatial resolution: 4.63 km x 4.63 km, cartesian climate modeling grid, Cylindrical Equal Angle Projection
- Geographic latitude: -89.9792°N to 89.9792°N
- Geographic longitude: -179.979°E to 179.979°E
- Dimension: 8640 columns x 4320 rows
- Altitude: 0.0 m
The data set itself does not include uncertainty estimates.
Because chlorophyll-a concentration retrieval is only possible over cloud-free areas the daily data offered here may have large data gaps due to cloud cover. The quality of the chlorophyll-a concentration (and additional parameters) estimates depends strongly on the quality of the cloud mask, which has been improved considerably for MODIS collection 6 data. Generally problematic are areas with fog, clouds with a top temperature similar to the sea surface temperature, thin cirrus clouds, and cloud shadows.
The ocean color parameters offered here were computed together with the MODIS sea surface temperature (see MODIS SST at ICDC). The quality flags with regard to cloud coverage / confidence in that data set are also valid for the ocean color data set.
Furthermore the retrieval itself can cause uncertainties and biases. It is based on an empirical approach. Its coefficients have been adopted several time during recent years to include results from validation and inter-comparison studies. Still generally problematic are gradients in the amount of suspended matter in the water (case 1 / case 2 waters) and insufficient calibration of the radiances measured. Observations angle and the angle of the incident solar radiation impact the products' quality (see e.g. Barnes, B. B., and C. Hu, 2016 in references).
More information is given in the references.
Jeremy Werdell (for Chlorophyll-a and Kd490)
NASA / GSFC
Greenbelt, MA, U.S.A.
email: jeremy.werdell (at) nasa.gov
Brian Franz (for POC and iPAR)
NASA / GSFC
Greenbelt MA, U.S.A.
email: brian.a.franz (at) nasa.gov
Dariusz Stramski (for POC)
Scripps Institution of Oceanography
La Jolla, CA, U.S.A.
email: dstramski (at) ucsd.edu
Robert Frouin (for PIC)
Scripps Institution of Oceanography
La Jolla, CA, U.S.A.
email: rfrouin (at) ucsd.edu
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de
- Algorithm Theoretical Basis Document - Bio-optical algorithms (pdf, not barrier free)
- Algorithm Theoretical Basis Document - PAR and iPAR (pdf, not barrier free)
- Algorithm Theoretical Basis Document - Coccolith Concentration (pdf, not barrier free)
- Ahmad, Z., et al. (2010) New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans. Applied Optics, 49: (29) 5545-5560, https://doi.org/10.1364/AO.49.005545
- Bailey, S. W., and P. J. Werdell (2006) A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102, 12-23, https://doi.org/10.1016/j.rse.2006.01.015
- Balch, W. M., et al. (2005) Calcium carbonate measurements in the surface global ocean based on Moderate-Resolution Imaging Spectroradiometer data. Journal of Geophysical Research 110, C07001. https://doi.org/10.1029/2004jc002560
- Behrenfeld, M. J., et al. (2009) Satellite-detected fluorescence reveals global physiology of ocean phytoplankton. Biogeosciences 6, 779-795. https://doi.org/10.5194/bg-6-779-2009
- Brian B. Barnes, Chuanmin Hu, Dependence of satellite ocean color data products on viewing angles: A comparison between SeaWiFS, MODIS, and VIIRS, Remote Sensing of Environment, Vol. 175, 2016, p 120-129, https://doi.org/10.1016/j.rse.2015.12.048
- Brewin, R. J. W., et al. (2016) Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals. Remote Sensing of Environment, 183, 82-97, https://doi.org/10.1016/j.rse.2016.05.005
- Clay, S., et al. (2019) Evaluation of satellite-based algorithms to retrieve chlorophyll-a concentration in the Canadian Atlantic and Pacific Oceans. Rem. Sens., 11(22), 2609, doi: 10.3390/rs11222609. https://doi.org/10.3390/rs11222609 [They investigate how accurate standard OCx algorithms are in comparison to regionally tuned versions and to a semi-analytical solution.]
- L. Feng and C. Hu, "Comparison of Valid Ocean Observations Between MODIS Terra and Aqua Over the Global Oceans," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 3, pp. 1575-1585, March 2016, doi: 10.1109/TGRS.2015.2483500. https://doi.org/10.1109/TGRS.2015.2483500
- Frouin, R., et al. (2012) A time series of photosynthetically available radiation at the ocean surface from SeaWiFS and MODIS data. Remote Sensing of the Marine Environment II, https://doi.org/10.1117/12.981264
- Frouin, R. and R. T. Pinker (1995) Estimating Photosynthetically Active Radiation (PAR) at the earth's surface from satellite observations. Remote Sensing of Environment, Volume 51, Issue 1, January 1995, Pages 98-107, ISSN 0034-4257. https://doi.org/110.1016/0034-4257(94)00068-X
- Gordon, H. R., et al. (2001) Retrieval of coccolithophore calcite concentration from SeaWiFS imagery. Geophysical Research Letters 28(8), 1587-1590. https://doi.org/110.1029/2000gl012025
- Haentjens, N., et al. (2017) Revisiting ocean color algorithms for chlorophyll a and particular organic carbon in the Southern Ocean using biogeochemical floats. J. Geophys. Res. - Oceans, 122, 6583-6593, doi:10.1002/2017JC012844. https://doi.org/10.1002/2017JC012844
- Hu, C., et al. (2019) Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. J. Geophys. Res.-Oceans, 124(3), 1524-1543, https://doi.org/10.1029/2019JC014941 .
- Lewis, K. M., et al. (2016) Regional chlorophyll-a algorithms in the Arctic Ocean and their effect on satellite-derived primary production estimates. Deep Sea Research II, 130, 14-27, https://doi.org/10.1016/j.dsr2.2016.04.020
- Morel, A., et al. (2007) Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sensing of Environment, 111, 69-88, https://doi.org/10.1016/j.rse.2007.03.012
- Morel, A., and S. Maritorena (2001) Bio-optical properties of oceanic waters: A reappraisal. Journal of Geophysical Research: Oceans, 106(C4), 7163–7180. doi: 10.1029/2000jc000319. https://doi.org/10.1029/2000jc000319
- Moutier, W., et al. (2019) Evaluation of chlorophyll-a and POC MODIS Aqua products in the Southern Ocean. Rem. Sens., 11(15), 1793, doi: 10.3390/rs11151793. https://doi.org/10.3390/rs11151793 [They present evaluation results for the level-2 1km-resolution product, highlighting among other issues that also in-situ observations - in their case from cruises during 2008-2017 - need to be processed and interpreted carefully to avoid misinterpretation of the evaluation results.]
- Stramski, D., et al. (2008) Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans. Biogeosciences, 5(1), 171–201. doi:10.5194/bg-5-171-2008. https://doi.org/10.5194/bg-5-171-2008
- Werdell, J. (2009), Global Bio‐optical Algorithms for Ocean Color Satellite Applications: Inherent Optical Properties Algorithm Workshop at Ocean Optics XIX; Barga, Italy, 3–4 October 2008, Eos Trans. AGU, 90( 1), 4– 4, doi:10.1029/2009EO010005. https://doi.org/10.1029/2009EO010005
- Werdell, P. J., et al. (2007) Approach for the long-term spatial and temporal evaluation of ocean color satellite data products in a coastal environment. Proceedings of SPIE, 6680, pp 12, https://doi.org/10.1117/12.732489
- Werdell, P. J. and S. W. Bailey (2005) An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment 98, 122-140, https://doi.org/10.1016/j.rse.2005.07.001.
- OceanColour Technical Documents, https://oceancolor.gsfc.nasa.gov/docs/technical/#AT
- OceanColour MODIS AQUA, https://oceancolor.gsfc.nasa.gov/data/aqua/
- OceanColour MODIS Terra, https://oceancolor.gsfc.nasa.gov/data/terra/
Please cite the data as follows:
NASA Goddard Space Flight Center, Ocean Biology Processing Group; (2018): MODIS Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA. https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Daily/4km/chlor_a/. Last access: April 10, 2022. Maintained by NASA Ocean Biology Distributed Active Archive Center (OB.DAAC), Goddard Space Flight Center, Greenbelt MD. DOI: 10.5067/AQUA/MODIS/L3M/CHL/2018
Please replace "MODIS-Aqua" by "MODIS-Terra" in case you cite MODIS-Terra products; please replace "Daily" by "Monthly" in case you use the monthly data; in the doi please replace "CHL" by "PIC", "POC", "FLH", "PAR", KD" depending on the other parameters you may have used.
and with the following acknowledgments:
MODIS Chlorophyll-a concentrations and additional ocean color standard mapped image data, originally obtained from https://oceandata.sci.gsfc.nasa.gov, were provided with time axis and merged into one netCDF file per day / month by the Integrated Climate Data Center (ICDC), CEN, University of Hamburg, Hamburg, Germany.
Usage and distribution of the data need to follow: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/ and https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/data-rights-related-issues