Spatial variability assessment of local chlorophyll-A estimation using satellite data

Abstract

The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in Chl-a retrieval using local ocean colour algorithm. Studies on Chl-a variation, assessment of MODIS data and development of local ocean colour algorithm are less for Malacca Straits water. The aim of this study is to locally calibrate and validate the Chl-a derived from MODIS standard Chl-a algorithm (OC3M) on the latest R2013 data within the acceptable error tolerance at the Absolute Percentage Difference (APD) below 35% and to test the algorithm’s applicability. Iterative regression method with weighted function (WFd) namely Iterative Conditional Regression Model (ICRM) is introduced to reduce the spatial bias in the Chl-a estimate. Locally calibrated OC3M algorithm with in-situ data taken at two static stations and kernel 7×7 size named as OCms1 (calibrated with in-situ Case-1 water) and OCms2 (calibrated with in-situ Case-2 water) remarkably reduced the Chl-a bias with APD of 37% and 30% from 54% and 116% respectively. Then, using the ICRM, the APD of OCms1 WFd and OCms2 WFd is 26% and 29% respectively. Results of OCms WFd and OCms (with and without weighted function respectively) are combined for mapping the Chl-a in Case-1 and Case-2 waters. Result of applicability test and statistical analysis shows that OCms WFd ocean colour algorithm provides statistically highest accuracy for Chl-a estimation. The development of local Chl-a algorithm is essential for accurate Chl-a retrieval and it is significant to other marine studies such as in primary production and algal bloom in Malacca Strait water.