Evaluation of the operational Chlorophyll-a product from global ocean colour sensors in the coastal waters, south-eastern Arabian Sea

Abstract

The present study aims to address the effect of spatial resolution and retrieval algorithms on the performance of operational chlorophyll-a (chl-a) product from different sensors. To meet the objective, chl-a product at different spatial resolutions derived from MODIS, SNPP-VIIRS, OC-CCI, and Sentinel-3A OLCI were compared with in-situ measurements of the coastal waters of Karnataka, south-eastern Arabian Sea and errors were quantified using statistical criteria. MODIS-derived chl-a concentrations at 4 km tends to overestimate in the nearshore waters as shown by a high mean relative percentage difference (MRPD) of 77.09%, and a low coefficient of determination (R2) of 0.35. A significant improvement was seen for MODIS 1 km chl-a product, (R2 = 0.64, MRPD = 22.30%) and Mean Absolute Percentage Deviation (MAPD) = 47.08%. Validation of SNPP-VIIRS derived chl-a of 4 km with in-situ measurements showed moderate correlation (R2 = 0.54, MAPD = 49.21%), whereas 1 km SNPP-VIIRS showed substantially better result than 4 km with majority of data points aggregated along the 1:1 line (R2 = 0.86, MAPD = 43.66%, MRPD = 20.97%). Chlorophyll-a product at original resolution (750 m) showed less error than 4 km and 1 km resolution (MAPD = 39.18%, R2 = 0.75, MRPD = 10.875%). In contrast, OC-CCI underestimated field measured chl-a concentrations as indicated by MRPD (-36.72%), with moderate R2 = 0.58. All 4 km data showed poor results than higher spatial resolution data. Sentinel-3A OLCI 300 m full-resolution (FR) derived chl-a, showed good agreement with sea-truth data (R2 = 0.80, MAPD = 33.07%, and MRPD = 23.89%), whereas Sentinel-3A reduced-resolution (RR-1.2 km) exhibited slight overestimation (MRPD = 28.43%, MAPD = 39.88%, R2 = 0.78) than Sentinel-3A(FR). Comparison of operational chl-a product from OC-CCI, MODIS, SNPP-VIIRS, and Sentinel-3A OLCI illustrated that Sentinel-3A OLCI performed best in coastal waters of Karnataka indicating the importance of higher spatial resolution and neural network-based algorithms.