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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 &#x201C;common bands&#x201D; at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDAS_v2. We applied the IDAS_v2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDAS_v2 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&#x2019; 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 IDAS_v2 algorithm provided IOPs data with &lt;28.36&#x0025; uncertainty for oceanic waters and &lt;37.83&#x0025; uncertainty for inland waters, which was much more effective than what a quasi&#x002D;analytical algorithm provided. Moreover, the near-infrared band was better at removing the residual error and partial intermission bias in satellite remote sensing reflectance (<italic>R</italic>_rs) data because of the strong absorption of pure water. We tested the IDAS_v2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDAS_v2, the IOPs data were accurately determined from <italic>R</italic>_rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer <italic>R</italic>_rs data at 443 and 551 nm decreased from 8&#x0025;&#x2013;25&#x0025; to 1&#x0025;&#x2013;9&#x0025;. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.