Characterization of Inherent Optical Properties (IOPs) over Global Coastal and Inland Waters from VIIRS Ocean Color Observations

Over the global turbid coastal and inland waters, the traditional satellite inherent optical property (IOP) algorithms, e.g., the quasi-analytical algorithm (QAA), the Garver-Siegel-Maritorena (GSM), and the generalized IOP (GIOP) algorithms, have some issues to accurately retrieve IOPs from the satellite ocean color measurements. In this presentation, we show that particle backscattering coefficient b_bp(λ), phytoplankton absorption coefficient a_ph(λ), and dissolved and detrital absorption coefficient a_dg(λ) can be derived accurately with a near-infrared (NIR)-based IOP algorithm using the normalized water-leaving radiance spectra nL_w(λ) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The combination of the NIR-based and the QAA-based IOP algorithms can produce high-quality IOP data over both the open ocean and turbid coastal/inland waters. Specifically, VIIRS-derived nL_w(λ) spectra from seven years observations (2012–2018) are used to produce the b_bp(λ), a_dg(λ), a_ph(λ), and particle size distribution (PSD) for various global turbid coastal and inland waters, e.g., in the Amazon River Estuary, China’s coastal region, Lake Taihu, US east coast and Chesapeake Bay region, etc. We further use the La Plata River Estuary as an example to provide a detailed evaluation of the seasonal and inter-annual variations in b_bp(λ), a_dg(λ), a_ph(λ), and PSD, and assess the physical, optical, and biological processes and their interactions in the La Plata River Estuary. It is demonstrated that satellite-measured IOP products using the NIR-based algorithm can provide an effective tool to study dynamics and processes of the ecosystem, assess the long-term environmental change, monitor and detect the hazardous events in the coastal and inland waters.