High quality in situ radiometric observations is needed for calibration, validation, and bio-optical algorithm development of ocean color remote sensing, moreover in studying and understanding of ocean optical, biological, and biogeochemical properties. Notably, calibration and validation of ocean color satellite data depend on high quality in situ data. Therefore, to improve our understanding of optical properties of the Red Sea, radiometric field measurements performed during in 2016 oceanographic cruises. The data set measured using the SatlanticTM HyperPro instrument equipped with radiometers, includes downwelling irradiance (Ed), upwelling radiance (Lu), and surface reference irradiance (Es). Profiles of downwelling irradiances were used to calculate diffuse attenuation coefficient, the first optical depth, PAR, and depth of the euphotic zone. Remote sensing reflectance computed from the ratio of upwelling radiance to downwelling irradiance. Derivative analysis performed on the spectral remote sensing reflectance to identify the different phytoplankton pigments based on the various peaks. The results obtain from the observational data analysis will be presented in this paper and discussed for ocean color implications.
Despite the importance of the optical properties such as the downwelling diffuse attenuation coefficient for characterizing the upper water column, until recently no in situ optical measurements were published for the Red Sea. Kirby et al. used observations from the Coastal Zone Color Scanner to characterize the spatial and temporal variability of the diffuse attenuation coefficient (Kd(490)) in the Red Sea. To better understand optical variability and its utility in the Red Sea, it is imperative to comprehend the diffuse attenuation coefficient and its relationship with in situ properties. Two apparent optical properties, spectral remote sensing reflectance (Rrs) and the downwelling diffuse attenuation coefficient (Kd), are calculated from vertical profile measurements of downwelling irradiance (Ed) and upwelling radiance (Lu). Kd characterizes light penetration into water column that is important for understanding both the physical and biogeochemical environment, including water quality and the health of ocean environment. Our study tests the performance of the existing Kd(490) algorithms in the Red Sea and compares them against direct in situ measurements within various subdivisions of the Red Sea. Most standard algorithms either overestimated or underestimated with the measured in situ values of Kd. Consequently, these algorithms provided poor retrieval of Kd(490) for the Red Sea. Random errors were high for all algorithms and the correlation coefficients (r2) with in situ measurements were quite low. Hence, these algorithms may not be suitable for the Red Sea. Overall, statistical analyses of the various algorithms indicated that the existing algorithms are inadequate for the Red Sea. The present study suggests that reparameterizing existing algorithms or developing new regional algorithms is required to improve retrieval of Kd(490) for the Red Sea.
Standard blue-green ratio algorithms do not usually work well in turbid productive waters because of the contamination of the blue and green bands by CDOM absorption and scattering by non-algal particles. One of the alternative approaches is based on the two- or three band ratio algorithms in the red/NIR part of the spectrum, which require 665, 708, 753 nm bands (or similar) and which work well in various waters all over the world. The critical 708 nm band for these algorithms is not available on MODIS and VIIRS sensors, which limits applications of this approach. We report on another approach where a combination of the 745nm band with blue-green-red bands was the basis for the new algorithms. A multi-band algorithm which includes ratios Rrs(488)/Rrs(551)and Rrs(671)/Rrs(745) and two band algorithm based on Rrs671/Rrs745 ratio were developed with the main focus on the Chesapeake Bay (USA) waters. These algorithms were tested on the specially developed synthetic datasets, well representing the main relationships between water parameters in the Bay taken from the NASA NOMAD database and available literature, on the field data collected by our group during a 2013 campaign in the Bay, as well as NASA SeaBASS data from the other group and on matchups between satellite imagery and water parameters measured by the Chesapeake Bay program. Our results demonstrate that the coefficient of determination can be as high as R2 > 0.90 for the new algorithms in comparison with R2 = 0.6 for the standard OC3V algorithm on the same field dataset. Substantial improvement was also achieved by applying a similar approach (inclusion of Rrs(667)/Rrs(753) ratio) for MODIS matchups. Results for VIIRS are not yet conclusive.