{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T02:53:49Z","timestamp":1764471229541,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Texas National Security Network Excellence Fund award for Environmental Sensing Security Sentinels","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}]},{"name":"SOFWERX award for Machine Learning for Robotic Teams","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}]},{"DOI":"10.13039\/100000001","name":"NSF Award","doi-asserted-by":"publisher","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Texas at Dallas Office of Sponsored Programs, Dean of Natural Sciences and Mathematics, and Chair of the Physics Department","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}]},{"name":"TRECIS CC* Cyberteam","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}]},{"DOI":"10.13039\/100000139","name":"EPA P3","doi-asserted-by":"publisher","award":["OAC-2115094","NSF #2019135","84057001-0"],"award-info":[{"award-number":["OAC-2115094","NSF #2019135","84057001-0"]}],"id":[{"id":"10.13039\/100000139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 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 hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue\u2013green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl\u2212, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition.<\/jats:p>","DOI":"10.3390\/rs16060996","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:17:16Z","timestamp":1710245836000},"page":"996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-0183","authenticated-orcid":false,"given":"John","family":"Waczak","sequence":"first","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0126-218X","authenticated-orcid":false,"given":"Adam","family":"Aker","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2688-648X","authenticated-orcid":false,"given":"Lakitha O. H.","family":"Wijeratne","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9841-6703","authenticated-orcid":false,"given":"Shawhin","family":"Talebi","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-2345","authenticated-orcid":false,"given":"Ashen","family":"Fernando","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2657-3416","authenticated-orcid":false,"given":"Prabuddha M. H.","family":"Dewage","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Mazhar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Matthew","family":"Lary","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"David","family":"Schaefer","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4265-9543","authenticated-orcid":false,"given":"David J.","family":"Lary","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/s7123209","article-title":"Remote sensing sensors and applications in environmental resources mapping and modelling","volume":"7","author":"Melesse","year":"2007","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0309133309339563","article-title":"A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters","volume":"33","author":"Joyce","year":"2009","journal-title":"Prog. 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