NASA Logo, National Aeronautics and Space Administration

National Aeronautics and Space Administration


CCE baner

CCE

 
Funded Research

Advancing the retrieval of marine inherent optical properties from satellite ocean color radiometry

Werdell, Paul (Jeremy): NASA GSFC (Project Lead)

Project Funding: 2014 - 2016

NRA: 2013 NASA: Terra and Aqua: Algorithms--Existing Data Products   

Funded by NASA

Abstract:
Marine inherent optical properties (IOPs), namely the spectral absorption and scattering properties of seawater and its dissolved and particulate constituents, describe the contents of the water column. Remotely-sensed estimates of IOPs from satellite ocean color provide exceptional data streams for studying biogeochemical processes in the upper ocean on spatial and temporal scales that cannot be achieved using conventional in situ and aircraft sampling techniques. As such, the oceanographic community has long invested in the development and application of IOP algorithms for satellites such as the NASA Moderate Resolution Imaging Spectrometer (MODIS) onboard Terra and Aqua. Semi-analytical algorithms (SAAs), arguably the most notable class of IOP algorithm for MODIS, permit the simultaneous estimation of spectral backscattering by particles, absorption by phytoplankton, and absorption by non-algal particles and dissolved material. Time-series of these marine optical properties provide unparalleled resources for studying air-sea carbon exchanges, phytoplankton diversity shifts, and ecosystem responses to climatic disturbances on regional to global scales. Many SAAs emerged over the past decade, all with accompanying advantages and limitations. Most differ only in their assumptions on the spectral behaviors of expected water column constituents, and few capture how these spectral parameterizations evolve in response to spatially and temporally varying environmental conditions. The 'first generation' of SAAs for MODIS (e.g., Maritorena et al. 2002) use constant, globally- tuned parameterizations for every viable satellite pixel. The second generation (e.g., Lee et al. 2002) allow dynamic parameterization on a pixel-by-pixel basis, based on ratios of spectral ocean color. The current (third) generation of SAAs adopt ensemble approaches, employing either ranges of parameterizations (e.g., Wang et al. 2005) or on-the-fly identification of optical water types (OWTs; Moore et al. 2009). Unfortunately, such ensemble approaches have yet to be applied operationally to MODIS. Furthermore, none of these SAAs for MODIS permit application in optically shallow waters (those where light reflecting off of the seafloor ultimately exits the water column). The Generalized IOP framework (GIOP, Werdell et al. 2013) provides a novel resource for applying SAAs to MODIS ocean color data records. Briefly, GIOP consolidates the features of most common SAAs into a single satellite data processing environment and allows SAA configuration at run-time. An international panel of experts recommended its default, global configuration, which produces MODIS IOP time-series of comparable (if not superior) quality to the SAAs cited above. The NASA Ocean Biology Processing Group includes GIOP in its operational ocean color satellite processing software (l2gen) and distributes GIOP to the oceanographic community via the SeaWiFS Data Analysis System (SeaDAS; oceancolor.gsfc.nasa.gov/seadas). We propose to expand the GIOP framework to include 'third generation' SAA features. Specifically, we propose to: (1) develop ensemble capabilities within GIOP for both iterative and OWT schemes; (2) enable the inclusion of ancillary data that modulate marine optical properties, such as sea surface temperature and salinity fields; (3) extend the radiative transfer framework to permit application in optically shallow environments; and, (4) continue to collaborate with the research community to identify and adopt emerging insights into marine bio- optics and calculation of IOP uncertainty budgets. By enhancing the GIOP framework, we also propose to develop, validate, and interpret modern MODIS-derived IOP time- series at multiple regional and global scales (with uncertainties) and to publicly distribute its enhanced capabilities via SeaDAS, thus facilitating community progress towards further SAA refinement.


More details may be found in the following project profile(s):