Morgan, P. FlintWeller, Wyatt W.Maxwell, Dylan J.Hamp, Shannon M.Venkatesulu, EricaShaw, Joseph A.Whitaker, Bradley M.Roddewig, Michael R.2024-08-132024-08-132024-05P. Flint Morgan, Wyatt W. Weller, Dylan J. Maxwell, Shannon M. Hamp, Erica Venkatesulu, Joseph A. Shaw, Bradley M. Whitaker, and Michael R. Roddewig "Toward polarization-enhanced water quality remote sensing measurements from UAVs", Proc. SPIE 13083, SPIE Future Sensing Technologies 2024, 1308308 (28 May 2024); https://doi.org/10.1117/12.3023048 PROCEEDINGS 9 PAGES + PRESENTATION GET CITATION Advertisement Advertisement RIGHTS & PERMISSIONS Get copyright permission Get copyright permission on Copyright Marketplace KEYWORDS Polarization Cameras Remote sensing Unmanned aerial vehicles Chlorophyll Water quality Data modeling RELATED CONTENT Monitoring and inversion of wheat scab based on UAV multi... Proceedings of SPIE (December 02 2022) Application of remote sensing methods for monitoring wild life populations... Proceedings of SPIE (September 21 2023) A mechanism for the management and optimization of imaging systems... Proceedings of SPIE (May 12 2005) Subscribe to Digital Library Receive Erratum Email Alerthttps://scholarworks.montana.edu/handle/1/18725Montana and similar regions contain numerous rivers and lakes that are too small to be spatially resolved by satellites that provide water quality estimates. Unoccupied Aerial Vehicles (UAVs) can be used to obtain such data with much higher spatial and temporal resolution. Water properties are traditionally retrieved from passively measured spectral radiance, but polarization has been shown to improve retrievals of the attenuation-to-absorption ratio to enable calculation of the scattering coefficient for in-water particulate matter. This feeds into improved retrievals of other parameters such as the bulk refractive index and particle size distribution. This presentation will describe experiments conducted to develop a data set for water remote sensing using combined UAV-based hyperspectral and polarization cameras supplemented with in-situ sampling at Flathead Lake in northwestern Montana and the results of preliminary data analysis. A symbolic regression model was used to derive two equations: one relating DoLP, AoP, and the linear Stokes parameters at wavelengths of 440 nm, 550 nm and 660 nm, to chlorophyll-a content, and one relating the same data to the attenuation-to-absorption ratio for 440 nm, 550 nm and 660 nm. Symbolic regression is a machine learning algorithm where the inputs are vectors and the output is an analytic expression, typically chosen by a genetic algorithm. An advantage of this approach is that the explainability of a simple equation can be combined with the accuracy of less explainable models, such as the genetic algorithm.en-USCopyright SPIE 2024https://www.spiedigitallibrary.org/article-sharing-policies#_=_water remote sensingpolarizationUAVchorophyllsymbolic regressionArticle10.1117/12.3023048