Corrections to the Calibration of MODIS Aqua Ocean Color Bands Derived From SeaWiFS Data

Meister, Gerhard; Franz, Bryan A.; Kwiatkowska, Ewa; McClain, Charles R.

doi:10.1109/tgrs.2011.2160552

Cited by 62 publications

(28 citation statements)

References 27 publications

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“…Finally, the radiometric sensitivity of a satellite instrument often degrades over time as the satellite ages, leading to decreased SNRs and increased instrumental uncertainties [54]. In practice, uncertainties for R rs λ vary spatially and temporally, and this variability includes changes in both their magnitude and spectral shape.…”

Section: Fig 2 Comparison Of Giop-dc and Ground Truth (In Situ)mentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

Self Cite

356

319

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Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

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Section: Fig 2 Comparison Of Giop-dc and Ground Truth (In Situ)mentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

Self Cite

356

319

View full text Add to dashboard Cite

show abstract

“…To limit the data set for the 500 m channels (20 detectors), the M coefficients are generated for the neighboring detector pairs, in other words, at 1 km resolution. For the near-nadir geometries, where azimuthal angle changes rapidly, one should also consider rotation angle α, which is the angle between the sensor reference (scan) plane, with respect to which the Mueller matrix is defined, and plane of observation, defined by the normal to the surface and view direction, with respect to which the Q and U components are calculated (for detail, see Meister et al, 2012;Kwiatkowska et al, 2008;Gordon et al, 1997). Once PC coefficients are produced, Eq.…”

Section: Polarization Correction Of Modis Terra Datamentioning

confidence: 99%

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

et al. 2014

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Abstract. The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and TerraAqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1 % of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1-B4, and provides a good consistency between the two MODIS sensors. MA-IAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra NDVI ∼ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloudfree observations compared to the afternoon Aqua measurements.

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“…Such an increase of more than 50 %, observed over the 11-year period, is much higher than the uncertainty in the SPM concentration estimated from MODIS satellite data using our algorithm (±25 %, according to Doxaran et al, 2009Doxaran et al, , 2012; this is an uncertainty which is significantly minimized when producing monthly averages of SPM concentrations (assuming there is no correlation between SPM and clouds). Moreover, due to regular vicarious calibrations of the MODIS-Aqua sensor (Franz et al, 2007;Meister et al, 2012), a temporal degradation in the satellite data quality cannot alone explain the observed variations. This confirms the consistency of the result presented here.…”

Section: Multiyear Spm Concentrations and Fluxes At The Mackenzie Rivmentioning

confidence: 99%

A 50 % increase in the mass of terrestrial particles delivered by the Mackenzie River into the Beaufort Sea (Canadian Arctic Ocean) over the last 10 years

Doxaran

Devred²,

Babin³

2015

Biogeosciences

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Abstract. Global warming has a significant impact on the regional scale on the Arctic Ocean and surrounding coastal zones (i.e., Alaska, Canada, Greenland, Norway and Russia). The recent increase in air temperature has resulted in increased precipitation along the drainage basins of Arctic rivers. It has also directly impacted land and seawater temperatures with the consequence of melting permafrost and sea ice. An increase in freshwater discharge by main Arctic rivers has been clearly identified in time series of field observations. The freshwater discharge of the Mackenzie River has increased by 25 % since 2003. This may have increased the mobilization and transport of various dissolved and particulate substances, including organic carbon, as well as their export to the ocean. The release from land to the ocean of such organic material, which has been sequestered in a frozen state since the Last Glacial Maximum, may significantly impact the Arctic Ocean carbon cycle as well as marine ecosystems.In this study we use 11 years of ocean color satellite data and field observations collected in 2009 to estimate the mass of terrestrial suspended solids and particulate organic carbon delivered by the Mackenzie River into the Beaufort Sea (Arctic Ocean). Our results show that during the summer period, the concentration of suspended solids at the river mouth, in the delta zone and in the river plume has increased by 46, 71 and 33 %, respectively, since 2003. Combined with the variations observed in the freshwater discharge, this corresponds to a more than 50 % increase in the particulate (terrestrial suspended particles and organic carbon) export from the Mackenzie River into the Beaufort Sea.

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Corrections to the Calibration of MODIS Aqua Ocean Color Bands Derived From SeaWiFS Data

Cited by 62 publications

References 27 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

A 50 % increase in the mass of terrestrial particles delivered by the Mackenzie River into the Beaufort Sea (Canadian Arctic Ocean) over the last 10 years

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