MODIS/Aqua Ocean Color Reprocessing 2018.0


The Ocean Biology Processing Group (OBPG) completed a full-mission ocean color reprocessing of the data from the Moderate Resolution Imaging Spectroreadiometer on Aqua (MODISA). This reprocessing is part of a multi-mission effort to update the instrument calibrations and vicarious calibration. Sensor-independent changes are detailed in the R2018.0 Ocean Color Reprocessing General Description. Here we describe the MODISA sensor-specific details of the reprocessing, and provide an assessment of data quality and impact relative to the previous R2014 MODISA reprocessing.

A minor calibration issue affecting MODIS-Aqua data from 2019 August 1st - Present was detected. This issue has been corrected by a mini-reprocessing to version 2018.1 for the affected ocean color data.

Sensor-specific Processing Details

Source Data

As in previous reprocessings, R2018.0 starts from the MODISA Level-1A files. The Level-1A source files are unchanged for this reprocessing.

Instrument Calibration


For this reprocessing, a major effort was undertaken to perform an independent, end-to-end re-analysis of the instrument calibration as derived from on-board calibration sources (solar/lunar) and vicarious methods. The instrument calibration developed for the R2018 reprocessing follows the same general approach developed and employed by the MODIS Calibration Support Team (MCST) and OBPG for previous ocean color reprocessings, but with a few notable differences. Thes include: 1) use of smoothing rather than fitting to the instrument temporal calibration trending and characterization (i.e., no assumption on functional form); 2) solar diffuser (SD) Bidirectional Reflectance Function (BRF) and SD / solar diffuser stability monitor (SDSM) screen transmission were computed from yaw maneuver data using linear fits, not geometric modeling; 3) SD degradations in red/NIR were determined by wavelength modeling (ref TBD); 4) simple atmospheric correction is performed when computing desert trends used to track response versus scan angle (RVS); 5) modulated RSR impact on ocean data is estimated and used to adjust the 412nm band temporal gains (Lee et. al. 2017). 6) lunar, solar, and desert trends are used to compute the RVS for the 412nm and 443nm bands using a 2nd order polynomial as a function of scan angle. Figure 1 shows the instrument temporal calibration as applied to the to the 412nm band (Band 8) for various scan-angle locations (colored lines), with the R2014 (MCST) calibration shown as dotted lines. The differences between these two calibrations is subtle, but significant for ocean color applications.

Figure 1


For all bands, the gains are derived from SD measurements for each detector and mirror side, and adjusted for effect of changing relative spectral response (RSR) on the earth-viewing observations. This modulated RSR only has meaningful impact to the 412nm band. Lunar calibration and desert measurements are used to derive a temporally varying response versus scan angle (RVS). For all visible and near-infrared (VISNIR) bands longward of 450nm, RVS is determined by lunar calibration measurements alone. The time-dependent RVS is computed based on the prelaunch measured RVS, adjusted for the changing lunar calibration trends (lunar scan angle) relative to the SD trends (SD-view scan angle). For the 412nm and 443nm bands, both lunar and desert measurements are used to determine the time dependent RVS, without using the prelaunch measurements, to enable tracking of changes in the RVS shape over time. The values derived with the above methods are used to overwrite the MCST-delivered instrument calibration lookup tables (LUTs) to create the OBPG LUTs. Note that the R2018 LUTs have a suffix of 'OC_v1.x', i.e. V6.2.1.1_OC_v1.1 is the OBPG LUT version v1.1 modified from MCST delivered LUT version V6.2.1.1. Only the gain (M11) and RVS values in the refected solar band (RSB) LUTs are modified, the rest of the LUTs remain the same as delivered from MCST.

Using these modified LUTs as a starting point, the OBPG derives additional corrections to minimize residual artifacts. First, a detector correction is derived for the NIR bands (748nm and 869nm), using a method described in Meister et al. 2009, that flat-fields the top-of-atmosphere (TOA) radiances for the detectors relative to each other after taking into account the expected variations due to atmospheric scattering. Corrections for the remaining bands are then derived using the crosscalibration (xcal) approach described in (Meister & Franz 2014). The xcal coefficients (M11) correct for residual detector and mirror side striping, as well as for scan angle dependencies. The xcal correction could introduce a small temporal trend due to uncertainties in fitting spatial/temporal/scan angle dependencies. Therefore, the xcal M11 coefficients are normalized at each time stamp to ensure no additional trends is introduced. This additional normalization is new to R2018. Figure 2 shows the xcal temporal trends for 412nm, which has the largest xcal trends among all bands (colors indicate different detectors of the same band).

Figure 2

The time series of xcal coefficients M11 are shown as a function of scan angle in Figure 3. The colored symbols correspond to the mirror side 1, detector mean correction for the selected years. These plots show that the xcal correction is up to ~1% at the extreme angles. Temporally, the correction is fairly consistent except for 412 nm. The R2018 xcal correction is smaller than the R2014 correction in blue bands, indicating an improvement in the temporal RVS characterization as derived from the lunar/solar/desert calibration. For most bands, the R2018 xcal corrections have similar magnitude as in R2014, but different shapes. More detailed xcal corrections plots are available here, where detector specific M11 trends are shown in several scan angles for all bands.

Figure 3

Vicarious Calibration

The OBPG applies an additional vicarious calibration to VIIRS during Level-2 processing (Franz et al. 2007). The 869nm bands is assumed to be correctly calibrated based on prelaunch measurements and absolute calibration to the solar diffuser. The 748nm bands is then adjusted using match-ups from the South Pacific Gyre, to force the aerosol type retrievals to match, on average, the aerosol type observed at the Tahiti AERONET site. The calibration of visible bands (412nm to 678nm) is then adjusted to produce retrievals that match, on average, measurements from the Marine Optical Buoy (MOBY) near Lanai Hawaii.

A major change in this reprocessing is to make use of the updated MOBY time-series, which was reprocessed by the MOBY Operations Team (MOT) to incorporate advancements in MOBY instrument calibration and characterization as discussed in the R2018 General Description.

The updated vicarious gains for this reprocessing are reported in the table below.

Wavelength (nm) 412 443 469 488 531 547 555 645 667 678 748 859 869 1240 1640 2130
Gain 0.9791 0.9908 1.0079 0.9945 1.0017 1.0014 0.9954 1.0197 1.0109 1.0095 1.0053 1.0184 1.0 1.0 1.0 1.0

Level-2 Processing

The product algorithms, product suites, and data formats are identical to those employed in the R2014.0 MODISA reprocessing.

Impact and Quality Assessment

Impact of Reprocessing on Timeseries

To assess the impact of this R2018.0 reprocessing relative to the previous R2014 reprocessing, a comparative timeseries analysis was performed (see Franz 2009 for details on the evaluation approach). The impact of all calibration, algorithm, and ancillary data changes on remote sensing reflectance is shown in this comparison of the mean Rrs for various globally-distributed geographic subsets. In these plots, AT155 is the R2018 reprocessing configuration, and AT135 is the R2014 configuration. On average, the combination of revised MOBY time-series and revised absolute instrument calibration contributes to a significant bias shift in all spectral bands, of order 2%-10% on the global deep-water mean (largest in blue and red), with resulting decrease in chlorophyll of order 10%. See summary tables here. The updated instrument calibration also significantly reduces a suspect negative trend in the blue bands (412, 443), relative to previous reprocessing, with associated impact to blue-water chlorophyll trends.

Comparison with In Situ Measurements

Validation of the remote sensing reflectance (Rrs) and chlorophyll retrievals was performed relative to all available in situ match-ups from SeaBASS and the Aerosol Robotic Network - Ocean Color (AERONET-OC), where only AERONET-OC Rrs data of quality level 2 were considered. Statistical analysis and scatter plots of the satellite to in situ match-ups are provided below, with results from R2014 also shown for reference. The R2018 Rrs retrievals show significant reduction in mean absolute error (MAE) and mean bias (MB) relative to in situ measurements, especially in the 412 channel. This is primarily an impact of the revised MOBY measurements. For all VIS bands, the MAE is now equal or better than 0.001/sr. Corresponding improvement in agreement was also found beween MODISA-retrieved and in situ chlorophyll, with a notable reduction in mean bias from 32% to 18%.

Rrs Validation Statistics for R2014 Reprocessing

Rrs Validation Statistics for R2018 Reprocessing

R2014 R2018


Eplee, R., K. R. Turpie, G. Meister, F. S. Patt, G.F. Fireman, B. A. Franz, and C. R. McClain (2013). A synthesis of VIIRS solar and lunar calibrations, Proc. SPIE 8866, Earth Observing Systems XVIII, 88661L (October 15, 2013); doi:10.1117/12.2024069.

Franz, B. A., S. W. Bailey, P. J. Werdell and C. R. McClain (2007). Sensor-independent Approach to the Vicarious Calibration of Satellite Ocean Color Radiometry. Applied Optics, 46: (22) 5068-5082.

Franz, B. A. (2009). Methods for Assessing the Quality and Consistency of Ocean Color Products.

Lee, S. and G. Meister (2017). MODIS Aqua Optical Throughput Degradation Impact on Relative Spectral Response and Calibration of Ocean Color Products, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 9, pp. 5214-5219. doi: 10.1109/TGRS.2017.2703672.

Meister, G., Franz, B. A., Kwiatkowska, E. J., Eplee, R. E., McClain, C. R. 2009. Detector dependency of MODIS polarization sensitivity derived from on-orbit characterization. In SPIE Optical Engineering+ Applications (pp. 74520N-74520N). International Society for Optics and Photonics.

Meister, G., & Franz, B. A. (2014). Corrections to the MODIS Aqua Calibration Derived From MODIS Aqua Ocean Color Products. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6534-6541.