Gregg, Watson: NASA GSFC (Project Lead)
Project Funding:
2014 - 2017
NRA: 2013 NASA: Suomi National Polar-orbiting Partnership (NPP) Science Team and Science Investigator-led Processing Systems for Earth System Data Records From Suomi NPP
Funded by NASA
Abstract:
Observing long term trends in ocean color data requires consistency among successive missions, since the lifetime of each is finite. S-NPP/VIIRS is the newest global ocean color mission, following the highly successful SeaWiFS and MODIS-Aqua sensors. Like all preceding ocean color sensors (except perhaps MODIS-Terra, which should be discounted because of inadequate capability), VIIRS has a different design and orbit than

any other global sensor. Rectifying the differences among different sensors and producing consistent ocean color observations is crucial to our understanding of global ocean biology variability and trends.
We propose to investigate the ability of an established approach to improve the consistency of VIIRS ocean color data. Our approach has been applied to the data sets of SeaWiFS and MODIS-Aqua and has increased the consistency of these sensor data sets globally and in all major oceanographic basins. In addition to improving consistency, the approach also forces satellite data to conform more closely to in situ data, producing a unified description of ocean biology from satellites and in situ platforms.
We now have 2 full years of VIIRS chlorophyll data and have evaluated the data quality extensively. Although there are issues with VIIRS, it appears to have potential to continue the ocean color time series. Our data assimilation approach can enhance the sensor data quality and consistency with MODIS-Aqua, but our in situ data bias correction has not been productive due to lack of in situ data so far.
In this proposal we hope to obtain sufficient in situ data to apply the bias correction and formalize the use of data assimilation of data product enhancement and consistency with MODIS-Aqua. We intend to proceed toward making our methodology available for routine processing, cognizant that this Level-4 algorithm will likely involve substantial obstacles. However, we believe that in situ bias correction and data assimilation for sampling bias reduction are approaches whose time has come and confronting the challenges of implementing them in a data processing scheme are worthwhile.
Publications:
Gregg, W. W., Rousseaux, C. S. 2014. Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models. Journal of Geophysical Research: Oceans. 119(9), 5921-5933. DOI: 10.1002/2014JC010158
More details may be found in the following project profile(s):
