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                    Section 1: Publication
                                
                Publication Type
                Thesis
                                
                Authorship
                Miller, Bryce.
                                
                Title
                Exploration of Evapotranspiration (ET) Sensitivity to Vegetation Indices (VIs) Using Weighing Lysimeters
                                
                Year
                2021
                                
                Publication Outlet
                University of Guelph
                                
                DOI
                
                                
                ISBN
                
                                
                ISSN
                ISSN 0048-9697
                                
                Citation
                
                    Miller, Bryce. 2021. Exploration of Evapotranspiration (ET) Sensitivity to Vegetation Indices (VIs) Using Weighing Lysimeters. University of Guelph. 
                
                                
                Abstract
                
                    Lake Erie, the shallowest of the five North American Laurentian Great Lakes, exhibits degraded water quality associated with recurrent phytoplankton blooms. Optical remote sensing of these optically complex inland waters is challenging due to the uncertainties stemming from atmospheric correction (AC) procedures. In this study, the accuracy of remote sensing reflectance (Rrs) derived from three different AC algorithms applied to Ocean and Land Colour Instrument (OLCI) observations of western Lake Erie (WLE) is evaluated through comparison to a regional radiometric dataset. The effects of uncertainties in Rrs products on the retrieval of near-surface concentration of pigments, including chlorophyll-a (Chla) and phycocyanin (PC), from Mixture Density Networks (MDNs) are subsequently investigated. Results show that iCOR contained the fewest number of processed (unflagged) days per pixel, compared to ACOLITE and POLYMER, for parts of the lake. Limiting results to the matchup dataset in common between the three AC algorithms shows that iCOR and ACOLITE performed closely at 665 nm, while outperforming POLYMER, with the Median Symmetric Accuracy (MdSA) of ∼30 %, 28 %, and 53 %, respectively. MDN applied to iCOR- and ACOLITE-corrected data (MdSA < 37 %) outperformed MDN applied to POLYMER-corrected data in estimating Chla. Large uncertainties in satellite-derived Rrs propagated to uncertainties ∼100 % in PC estimates, although the model was able to recover concentrations along the 1:1 line. Despite the need for improvements in its cloud-masking scheme, we conclude that iCOR combined with MDNs produces adequate OLCI pigment products for studying and monitoring Chla across WLE.
                
                                
                Plain Language Summary
                
                    Key Points
-Reproducible, transparent modeling increases confidence in model simulations and requires careful tracking of all model configuration steps
-We show an example of model configuration code applied globally that is traced and shared through a version control system
-Standardizing file formats and sharing of code can increase efficiency and reproducibility of modeling studies
                
                 
                
                    Section 2: Additional Information
                                
    
        Program Affiliations
            
                                
    
        Project Affiliations
            
                                
    Submitters
            
                                
                Publication Stage
                N/A
                                
                Theme
                Water Quality and Aquatic Ecosystems
                                
                Presentation Format
                10-minute oral presentation
                                
                Additional Information
                
                    TTSW, Graduate and Undergraduate Theses