Algorithm to derive inherent optical properties from remote sensing reflectance in turbid and eutrophic lakes

Area of Science:

• Aquatic optics and remote sensing
• Inland water biogeochemistry
• Ocean color science

Background:

• Inherent optical properties (IOPs) are vital for understanding lake biogeochemical processes, serving as proxies for quantities like phytoplankton pigments.
• Accurate remote sensing retrieval of phytoplankton absorption coefficients [a_ph(λ)] in turbid, eutrophic waters remains challenging.
• Existing models often struggle with the complex optical conditions of inland waters.

Purpose of the Study:

• To develop and validate a novel iterative inversion model for retrieving IOPs, including phytoplankton absorption [a_ph(λ)], in turbid inland lakes.
• To improve the accuracy of remote sensing-based estimation of [a_ph(λ)] using regional datasets.
• To apply the developed algorithm to satellite data (Sentinel-3A OLCI) for broader application.

Main Methods:

• Collected extensive in-situ remote sensing reflectance [Rrs(λ)] and absorption coefficient data from lakes in the MLYHR basin, China.
• Implemented a scattering correction method for spectrophotometric measurements, incorporating a baseline correction for particulate absorption [a_p(λ)] at 750 nm.
• Designed and applied a novel iterative inversion model to retrieve total non-water absorption [a_nw(λ)], particulate backscattering [b_bp(λ)], [a_ph(λ)], and absorption of non-algal particles and CDOM [a_dg(λ)] from [Rrs(λ)].

Main Results:

• The developed iterative model demonstrated superior performance in deriving [a_nw(λ)] and [b_bp(λ)] compared to previous models in the study region.
• Accurate estimation of [a_ph(λ)] was achieved for wavelengths > 500 nm, with low Unbiased Absolute Percentage Difference (UAPD) and Root Mean Square Error (RMSE) on both calibration and validation datasets.
• Successful application and validation of the algorithm using Sentinel-3A OLCI satellite data confirmed its effectiveness for turbid inland waters.

Conclusions:

• The novel iterative inversion algorithm effectively retrieves key IOPs, including phytoplankton absorption, from satellite remote sensing data in turbid inland waters.
• This study highlights the importance of using local datasets to develop region-specific algorithms for accurate optical property retrieval.
• The validated algorithm provides a valuable tool for monitoring lake biogeochemistry and water quality using satellite observations.