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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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doi:10.5194/amt-2016-360
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
01 Dec 2016
Review status
A revision of this discussion paper was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.
Updated MISR Dark-Water Research Aerosol Retrieval Algorithm – Part 1: Empirical Calibration Corrections and Coupled 1.1 km Ocean-Surface Chlorophyll-a Retrievals
James A. Limbacher1,2 and Ralph A. Kahn1 1Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, 20771, USA
2Science Systems and Applications Inc., Lanham, 20706, USA
Abstract. As aerosol amount and type are key factors in the “atmospheric correction” required for remote-sensing chlorophyll-a concentration (Chl) retrievals, the Multi-Angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean-surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo retrievals. We develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, and introduce a self-consistent retrieval of Chl together with aerosol over dark water. The calibration corrections include: a modified stray-light model based on comparison with coincident MODerate-resolution Imaging Spectroradiometer (MODIS) Terra data, and trend analysis using MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. The trend analysis shows that MISR radiometric sensitivity decreased by up to 2 percent for MISR spectral bands between January 2002 and December 2014.

After applying calibration corrections, we run the MISR Research Retrieval Algorithm (RA) to validate the MISR RA-retrieved Chl, and analyze both the MISR and corresponding MODIS-Terra values compared to a set of 49 collocated SeaBASS in situ observations, constrained to Chlin situ < 1.5 mg m−3. Statistically, compared to the validation data, MODIS demonstrates a higher correlation coefficient (r) of 0.91 vs. 0.86 for MISR, a lower root-mean-squared-error (RMSE) of 0.25 vs. 0.22, but a higher median absolute error (MAE) of 0.14 vs. 0.10. Because 49 data points are insufficient to draw strong conclusions, we also compare MODIS-Terra and MISR RA Chl statistically, over broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl < 1.5: r = 0.96, MAE = 0.09, and RMSE = 0.15. MISR-MODIS agreement is substantially better than the 49-data-point MODIS-SeaBASS comparison, indicating that MISR Chl retrievals might complement MODIS, especially after further upgrades are made to the MISR RA ocean color model.

The new dark water aerosol/Chl RA can retrieve Chl in low-Chl (< 1.5), case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR’s unique multi-angular data can better constrain aerosol type, helping reduce uncertainties in the MODIS Terra Ocean color retrieval, and suggesting how a joint MISR-MODIS over-ocean algorithm might exceed the capabilities of either instrument alone.


Citation: Limbacher, J. A. and Kahn, R. A.: Updated MISR Dark-Water Research Aerosol Retrieval Algorithm – Part 1: Empirical Calibration Corrections and Coupled 1.1 km Ocean-Surface Chlorophyll-a Retrievals, Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-360, in review, 2016.
James A. Limbacher and Ralph A. Kahn
James A. Limbacher and Ralph A. Kahn
James A. Limbacher and Ralph A. Kahn

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Short summary
Characterizing the amount and type of airborne particles is key for accurately modeling climate and air quality. Here we refine the time-varying calibration of the MISR space-borne instrument by analyzing multi-year imagery of stable desert sites. We also improve MISR aerosol retrievals over ocean, and simultaneously constrain ocean surface chlorophyll-a concentration, by self-consistently retrieving the ocean surface properties that could otherwise alias the results if not correctly assumed.
Characterizing the amount and type of airborne particles is key for accurately modeling climate...
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