First Experiences with the Landsat-8 Aquatic Reflectance Product: Evaluation of the Regional and Ocean Color Algorithms in a Coastal Environment

Abstract

Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for estimating the coastal water Chl-a concentrations with higher detail and higher accuracy than previously. The RTA20 algorithm developed in this study outperformed the previous algorithms (RTA16, RTA17, RTA19, OC2 and OC3), e.g., with lower values for Root Mean Square Error (RMSE; 0.92 mg/m3), bias (−0.26 mg/m3) and mean ratio (1.29). Although inferior to the RTA20, the OC2 algorithm also performed well in terms of Pearson’s correlation coefficient (r; 0.84), slope (6.87) and intercept (−8.44) while for RTA20 the values for r, slope and intercept were 0.96, 0.77 and 0.27, respectively. This preliminary evaluation reveals that the OC2 algorithm can be used as an operational algorithm for L8 Chl-a product generation for global coastal areas while RTA20 can be used as a regional algorithm for the routine monitoring of Chl-a concentrations around the coastal areas of Hong Kong or for coastal areas with similar water quality elsewhere in the world.

[Retracted] AHP‐Based Analysis Factors Influencing the Construction of a Smart City with Three‐Dimensional Regional Color

“…Among them, W is the standardized weight of the i-th variable and n is the total number of variables [21].…”

Estimation of Water Quality Parameters Along the Indian Coast Using Satellite Observations

“…Chlorophyll-a was derived using the OC2 algorithm (Eq. 22.2) with the coefficients suggested by (Franz et al 2015;Nazeer et al 2020).…”

Trend of harmful algal bloom dynamics from GOCI observed diurnal variation of chlorophyll a off Southeast coast of China

“…In the process of GOCI image data processing, it is necessary to screen the satellite remote sensing data with stringent criteria (Siswanto et al, 2011;Nazeer et al, 2020). In terms of time, the satellite data acquisition time needed to be consistent with the HAB growth time; in space, each target pixel of the remote sensing product was taken as the center, and the observed values of the pixels in the 3×3 window were selected for matching calculation.…”

“…Then, if the number of effective pixels was greater than 1/3 of the total pixels in the window (Nazeer et al, 2020), the mean of the window was calculated as the matching value of the target pixel.…”