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Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models—the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)—produced over the Red Sea domain for the 1998–2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models’ responses to various climate factors. Here, we use the models’ eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models’ connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts. [ABSTRACT FROM AUTHOR]

An optical algorithm is developed for the retrieval of the downwelling diffuse attenuation coefficient $K_{d}$ (490) in the Red Sea using a comprehensive hydrolight simulated data set ($N = 5000$). We found a robust relationship between the $K_{d}$ (490) and the ratio of remote sensing reflectance $R_{\text {rs}}$ (443)/ $R_{\text {rs}}$ (555), with an excellent determination coefficient ($R^{2} = 0.999$) and a low root-mean-square error (RMSE = 0.00033). The performance of the developed algorithm is evaluated with in situ data collected in the Red Sea by comparing obatined model output with existing empirical (NASA, Morel et al., Zhang and Fell, Tiwari and Shanmugam) and semianalytical (Lee et al.) algorithms. On the used in situ data from the Red Sea, the new algorithm shows good retrievals of $K_{d}$ (490) with a low bias, and a low RMSE compared to that of the existing algorithms. For satellite application, we applied our algorithm to selected MODIS–Aqua images acquired over the Red Sea, which captured spatial features of phytoplankton blooms and physical processes (e.g., cyclonic and anticyclonic circulations) in the Red Sea. The new algorithm has the potential to improve our understanding of water transparency and photosynthetic processes that rely on the availability of solar radiation. [ABSTRACT FROM AUTHOR]

Despite the importance of diffuse attenuation coefficient of downwelling irradiance (K d) to physical, chemical and biological processes in the euphotic zone, accurate determination of this apparent optical property from satellite ocean color data for coastal oceanic waters is still challenging with most of the existing algorithms. This paper presents a simple and robust algorithm developed using the relationship (R² = 0.995) of the ratio of remote sensing reflectances at 490 and 670 nm to the K d at a wavelength of 490 nm derived from a large number of radiometric profiles from various regional waters. When tested with three independent data sets from relatively clear and turbid waters, accuracy of K d (490) prediction by the present algorithm is statistically good compared to that of the existing algorithms. The effectiveness of the present algorithm is also tested on MODIS-Aqua images from complex waters around India. It is found that the present algorithm makes better retrievals of Kd (490) capturing the spatial characteristics of bloom and turbid water features. These results suggest that the present algorithm has the potential to provide more accurate Kd (490) from satellite ocean color data for coastal oceanic waters. [ABSTRACT FROM PUBLISHER]

Several semi-analytical models for the satellite-based estimation of absorption coefficients of phytoplankton aph (\lambda ) have been used to routinely produce aph (\lambda ) product from satellite ocean color data. However, these models are generally applicable for clear ocean waters where they produce aph (\lambda ) values only at a few wavelengths in the blue-green domain; this causes the main difficulty in making these models more usable with any suite of wavelengths. Further, recent studies have shown the performance of these models to be highly questionable in optically complex waters. This emphasized the need for developing a more accurate model for the satellite-based estimation of aph (\lambda ) in a wide range of oceanic waters. In our previous study, we developed an empirical model (hereafter referred as Tiwari-Shanmugam model – “TS model”) based on the relationship of the in situ remote sensing reflectance ratio R rs (670)/R rs (490) and in situ aph(\lambda) which is best fit to a third order polynomial. In the present study, we rigorously test this model along with three global inversion models (e.g., Constrained Linear Matrix (LM) model, Quasi-analytical algorithm (QAA), and GSM semi-analytical model which are often used by the ocean color community) using three independent in situ data sets from clear to turbid coastal waters and satellite match-ups data from global waters. When applied to these data sets, the TS model produces more accurate aph values across the entire visible wavelengths (400–700 nm) in all these waters, whereas LM, QAA and GSM models yield significant errors in addition to being restricted to produce aph values only in the blue-green wavelengths (LM and GSM). Though the TS model is mathematically simple, it overcomes such limitations and yields excellent results in terms of reproducing measured aph spectra that are highly desired by the ocean color community for inputting in various bio-optical models and studying the spatial structure of the different phytoplankton communities from satellite remote sensing observations in global waters. [ABSTRACT FROM PUBLISHER]