Abstract
Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with R rs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from R rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer R rs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.
Highlights
O CEANIC color remote sensing initially focused on estimating the chlorophyll-a concentration in the upper ocean at basin and global scales [1], [2]
After comparing the IDAS version 2 (IDASv2) algorithm results to the quasi-analytical algorithm (QAA) algorithm results, we found that IDASv2 more effectively retrieved inherent optical properties (IOPs)
Our results confirmed that the IDASv2 and QAA algorithms effectively process ocean color data for retrieving IOPs, yet IDASv2 performed significantly better than the QAA algorithm for our satellite data
Summary
O CEANIC color remote sensing initially focused on estimating the chlorophyll-a concentration in the upper ocean at basin and global scales [1], [2]. CHEN et al.: IMPROVED IOPS DATA PROCESSING SYSTEM FOR RESIDUAL ERROR CORRECTION IN TURBID NATURAL WATERS satellite data [20], which can cause inconsistent outputs among the different algorithms even for the same water-leaving signals [21] This is a disadvantage for generating smooth and long-term series of IOPs data on global or local scales. Doxaran et al [24] and Han et al [25] showed that the Rrs values at visible bands tend to saturate because of the suspended particulate in highly turbid water It is questionable whether the four “common bands” can offer sufficient optical information for estimating the IOPs of highly turbid water. 4) to analyze how well the IDASv2 algorithm corrects the intermission consistency in data of turbid water
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