Primary productivity of an aquatic system like 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 of prominent ocean color sensors (OC3M - MODIS, OC-OCM2, and OC3V-VIIRS) with sea truth data, collected from 204 stations for three year period (2015-2017). The in-situ concentrations of Chl-a depicts the geographic region under the mesotrophic and eutrophic spans ((0.1>Ca>1.0 mg m-3). The ratio of CaOCM2/CaIn-situ is 0.97±0.27 mg m-3 (n=199), in unsimilarity with CaVISSR/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 regression analysis proclaims a moderate significant relationship for MODIS (r2 = 0.36; p<0.001), followed OCM2 (r2 = 0.32; p<0.001) and VISSR (r2 = 0.19; p<0.001) with evident overestimation (MODIS and VIRRS) and in tune (OCM2) with the satellite-derived datasets. The global ocean color missions aimed to set RMSE error at 0.35, the OCM2 shown the lowest RMSE as 0.13, which is relatively lower than the reference error limit. 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 concentrations, but the OCM2 algorithm is suitable in the region with a negligible bias of -0.03. The biogeochemical processes and ecosystem characteristics are dynamic from region to region, as yet in its urgent need to validate global and formulate regionally tuned algorithms periodically.