Abstract
The primary productivity of an aquatic system like the Arabian Sea is broadly determined by the concentration of Chlorophyll-a (Chl-a/Ca) pigment. The present study is essaying to validate the Chl-a data set retrieved from the prominent ocean color sensors (OC3M - MODIS, OC-OCM2, and OC3V-VIIRS) with sea truth data, collected from 204 stations for a three year period (2015–2017). The in-situ concentrations of Chl-a depict the geographic region under the mesotrophic and eutrophic spans with a mean of 1.36 mg m-3 ((0.1 > Ca>1.0 mg m-3). The ratio of CaOCM2/CaIn-situ was 0.97 ± 0.27 mg m-3 (n = 199), but the ratios were higher with CaVIIRS/CaIn-situ is 1.75 ± 0.79 mg m-3 (n = 170) and CaMODIS/CaIn-situ is 2.53 ± 1.42 mg m-3 (n = 158). The coefficient of determination proclaims a moderate significant relationship for MODIS (R2 = 0.36; p < 0.001), followed OCM2 (R2 = 0.32; p < 0.001) and VIIRS (R2 = 0.19; p < 0.001). The OCM2 showed the lowest RMSE as 0.13, which is relatively lower than the reference error limit by global ocean color missions at 0.35. In overall performance among three algorithms, the OCM2 will provide a better estimation of Chl-a with a prediction of 32% accuracy and 34.37 % of bias. The log bias values for MODIS (0.35) and VIIRS (0.20) algorithms indicating the overestimation of Chl-a with in-situ Chl-a, but the OCM2 algorithm is suitable in the region with a negligible bias (-0.03). The biogeochemical processes and ecosystem characteristics are dynamic from region to region, as yet in its urgent need to validate global sensors to fine-tune the regional algorithms periodically.
Estimation of Phytoplankton Responses to Hurricane Gonu over the Arabian Sea Based on Ocean Color Data