Synergistic application of oceanographic variables from multi-satellite sensors for forecasting potential fishing zones: methodology and validation results

Abstract

This study uses synergistic application of satellite-derived chlorophyll concentration (CC), sea surface temperature (SST) and sea surface wind (SSW) for forecasting potential fishing zones (PFZs). PFZs are validated in near-real time through fishing operations and detailed statistical analysis of fishing operation data. CC and SST images were derived from Indian Remote Sensing Satellite-Ocean Colour Monitor (IRS-OCM) and NOAA-AVHRR, respectively, to delineate the oceanographic features exhibiting different oceanic processes. QuikSCAT/SeaWinds derived sea surface wind vectors were used to understand, quantify and demonstrate the variability of wind-induced water mass flow as well as their impacts on features/oceanographic process. Oceanographic features such as eddies, rings and fronts were found to be shifted according to the speed and direction of the wind. An algorithm was developed to compute water mass transport and feature shift. An improved methodology was developed and demonstrated using these prime variables, which are responsible for fishery resources distribution. PFZ forecasts were generated and validated through near-real-time fishing operations. The fishing operations data were taken from the logbooks of fishing vessels for detailed statistical analysis. On average, 80% of observations were recorded with more yield than monthly mean catch in the respective areas. A paired t-test showed statistically significant results.