preprints.org > environmental and earth sciences > remote sensing > doi: 10.20944/preprints202401.2041.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 29 January 2024 / Approved: 29 January 2024 / Online: 29 January 2024 (14:41:02 CET)

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the high-quality reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of comprehensive in situ data together with high-resolution hyperspectral imagery. We showcase the capabilities of this team using data collected in a north Texas pond during three collections in the fall of 2020. Machine learning models for 13 variables are trained using the data set of paired in situ measurements and coincident reflectance spectra. These models can successfully estimate physical variables, including temperature, conductivity, pH, and turbidity, as well as the concentrations of blue-green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. The maps generated by applying each trained model to the collected images reveal the small-scale spatial variability within the pond. Additionally, the permutation importance computed for each feature of the trained models highlights the spectral features relevant to each water quality variable.

Water Quality; Robotic Teams; Hyperspectral Imaging; Machine Learning; Conformal Prediction

Environmental and Earth Sciences, Remote Sensing

* All users must log in before leaving a comment