Selection of atmospheric correction method and estimation of Chlorophyll-a (Chl-a) in coastal waters of Hong Kong

Abstract

Precise atmospheric correction is important for applications where small differences in Surface Reflectance (SR) are significant, such as biomass estimation, crop phenology and retrieval of water quality parameters. As a precursor to monitor water quality parameter Chlorophyll-a (Chl-a), around the coastal waters of Hong Kong using medium resolution sensor, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), this study evaluated the performance of five atmospheric correction methods. The estimated SR, using the five methods including, 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), ATCOR (ATmospheric CORection), DOS (Dark Object Subtraction) and ELM (Empirical Line Method), was validated with in situ Multispectral Radiometer (MSR) SR measurements over sand, artificial turf, grass and water surfaces for the first four reflective bands of Landsat 7 ETM+ and HJ-1 A/B satellites. Among the five methods 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in situ measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites and equaled the good performance of 6S over the high reflective sand surfaces. Therefore, the Landsat TM/ETM+ atmospherically corrected images along with the in situ Chl-a data from 2000 to 2012 were used to develop and validate the regression models for Chl-a concentration of 0.3 to 13.0 ug/l. The validation results showed that the most accurate Chl-a was estimated using the ratio of Band 3 (0.63-0.69 μm) and (Band 1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.45-0.52 μm) with correlation coefficient (R) 0.86, Root Mean Square Error (RMSE) of 2.70 ug/l and Mean Absolute Error (MAE) of 1.13 ug/l for coastal waters of Hong Kong.