An empirical algorithm to estimate silicate in the Southwest Bay of Bengal

Silicate is a significant prerequisite for the growth and development of primary producers, mainly in diatoms, it remains a prevalent contributor. Satellite ocean colour sensors data are broadly utilized for the identification, mapping and monitoring the phytoplankton characteristics, spatial and temporal. In this study, an empirical algorithm was developed for mapping the silicate concentration an important nutrient for planktonic diatoms depending on the relationship between chlorophyll-a, Sea Surface Temperature (SST) and silicate at a high spatio-temporal resolution. Three dimensional polynomial functions, such as plane, paraboloid, Gaussian and Lorentzian functions were used to correlate SST, chlorophyll-a and silicate. Among these the paraboloid function provided significant relationship between the variables with an R2 value of 0.828. Validation of Visible Infrared Imaging Radiometer Suite (VIIRS) derived SST (R2 = 0.634, Mean Normalized Bias (MNB) = 0.006, Root Mean Square Error (RMSE) = 0.280 and Standard Error of Estimation (SEE) = ±0.227) and chlorophyll-a (R2 = 0.523, MNB = 0.369, RMSE = 0.846, and SEE = ±0.632) observed better synchronization with in situ measurements of SST and chlorophyll-a, respectively. The VIIRS-derived silicate algorithm provided better agreements with in situ silicate concentration (R2 = 0.784, MNB = -0.001, RMSE = 1.394 and SEE = ±0.839) along the Southwest Bay of Bengal.