Semianalytical Derivation of Phytoplankton, CDOM, and Detritus Absorption Coefficients From the Landsat 8/OLI Reflectance in Coastal Waters

Abstract

The Operational Land Imager (OLI) onboard Landsat 8 has potential for mapping the water bio‐optical properties with high spatial resolution. Landsat 8/OLI generates the remote sensing reflectance (Rrs) at four visible bands (λ1‐4 = 443, 482, 561, and 655 nm) and is lack of a 412‐nm band commonly included for ocean color sensors. This spectral configuration has limited the use of Landsat 8/OLI reflectance product for analytical derivation of light absorption coefficients of phytoplankton, colored dissolved organic matter (CDOM), and detritus. In this study, we proposed a hybrid approach to fill this gap. First, we developed an algorithm to estimate the reflectance in a virtual band centered at λ0 = 412 nm from the OLI reflectance spectra Rrs(λ1‐4). Both the estimated Rrs(λ0) and measured Rrs(λ1‐4) were then used together to retrieve the water component absorption coefficients with existing algorithms including the quasi‐analytical algorithm. We assessed the model performance using in situ measurements from the global waters. It was found that the proposed approach could estimate Rrs(412) with a median absolute percentage difference of ~9%. The subsequent retrievals of the component absorption coefficients were satisfactorily accurate, with median absolute percentage difference roughly equal to 35%, 40%, and 60% for phytoplankton, CDOM, and detritus, respectively. The results suggest the feasibility to generate analytically the component absorption coefficients from the Landsat 8/OLI reflectance.

Global Estimation of Suspended Particulate Matter From Satellite Ocean Color Imagery

“…It is possible to implement the NIR‐RGB SPM algorithm with R rs ( λ ) data from OLI. In particular, the OLI imagery allows for mapping in greater detail with 30‐m spatial resolution in coastal and inland waters (Luis et al., 2019; Pan et al., 2018; Wei et al., 2019). We tested two OLI images captured over the Mississippi River Delta and the Nantucket Sound off of Massachusetts.…”

“…It is possible to implement the NIR-RGB SPM algorithm with R rs (λ) data from OLI. In particular, the OLI imagery allows for mapping in greater detail with 30-m spatial resolution in coastal and inland waters (Luis et al, 2019;Pan et al, 2018;Wei et al, 2019 The above function results from the least squares fit to a large set of hyperspectral measurements (Wei et al, 2016), with R 2 = 0.99. Similarly, R rs (561) of OLI was transformed to R rs (551) with the function given below (R 2 = 0.…”

Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine

“…However, they have a limitation in receiving signals in the 400-450 nm in order to capture some clear water, which generally included for ocean colour space sensors, and restricted their usage for the quasi-analytical derivation of Z SD and other optically active water quality parameters [32]. By including such feasible, virtually estimated reflectance bands at these missing wavelengths we can further improve the accuracy of the analytical derivation approach [69].…”

Determining spatio-temporal distribution dynamics of chlorophyll-a in Banyuasin sea waters using remote sensing data

“…Chl-a is a pigment capable of photosynthesis and a marker for phytoplankton biomass, which is the most significant driver of the health and productivity of ocean ecosystems. Phytoplankton are an important component of coastal aquatic ecosystems [6]. According to [7], this is due to the fact that phytoplankton are the main primary producers in marine ecosystems and support important food webs in waters.…”