Improving NASA IOP data products for VIIRS
Lachlan McKinna, GO2Q, lachlan.mckinna@go2q.com.au (Presenter)
Paul Jeremy Werdell, NASA GSFC, jeremy.werdell@nasa.gov
Inherent optical properties (IOP) describe how light is absorbed and scattered by seawater. On their own, IOPs can be used to characterize dissolved and particulate matter present in the water column, such as distributions and concentrations of phytoplankton. IOPs also provide valuable information regarding the underwater light field, which is critical to studying oceanic primary productivity and global carbon fluxes. The NASA OB.DAAC routinely processes and distributes IOP data products derived from satellite ocean color data. Standard IOP data products are currently derived using the Generalized Inherent Optical Properties (GIOP) algorithm framework. Supported sensors include MODIS-Aqua and -Terra, VIIRS-NPP, -J1, and -J2. Here we describe several refinements recently made to the GIOP algorithm. These refinements aim to improve multi-mission consistency across NASA’s 25+ year ocean color data record into the VIIRS era. The first major refinement is a new approach for estimating measurement uncertainties using a Bayesian optimal estimation approach. The second major refinement aims to improve spectral consistency of IOP data products. Via bio-optical band shifting, IOPs are output at a pre-defined set of bands for all supported missions which is designed to alleviate discrepancies caused by sensor-to-sensor spectral differences (e.g., 551 nm VIIRS-NPP vs. 556 nm VIIRS-J1). Moving forward, we plan to implement these changes in the next OB.DAAC reprocessing effort.
Associated Project(s):
Poster Location ID: 2-60
Presentation Type: Poster
Session: Poster Session 2
Session Date: Wed (May 10) 5:15-7:15 PM
CCE Program: OBB