{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:56:19Z","timestamp":1775850979218,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Atlantic Salmon Conservation Foundation"},{"name":"Government of New Brunswick"},{"name":"McCain Postdoctoral Fellowship in Innovation"},{"name":"New Brunswick Innovation Foundation"},{"name":"Department of Fisheries and Oceans Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Thermal mapping of surface waters and the land surface via UAVs offers exciting opportunities in many scientific disciplines; however, unresolved issues persist related to accuracy and drift of uncooled microbolometric thermal infrared (TIR) sensors. Curiously, most commercially available UAV-based TIR sensors are black, which will theoretically facilitate heating of the uncooled TIR sensor via absorbed solar radiation. Accordingly, we tested the hypothesis that modifying the surface absorptivity of uncooled TIR sensors can reduce thermal drift by limiting absorptance and associated microbolometer heating. We used two identical uncooled TIR sensors (DJI Zenmuse XT2) but retrofitted one with polished aluminum foil to alter the surface absorptivity and compared the temperature measurements from each sensor to the accurate measurements from instream temperature loggers. In addition, because TIR sensors are passive and measure longwave infrared radiation emitted from the environment, we tested the hypotheses that overcast conditions would reduce solar irradiance, and therefore induce thermal drift, and that increases in air temperature would induce thermal drift. The former is in contrast with the conceptual model of others who have proposed that flying in overcast conditions would increase sensor accuracy. We found the foil-shielded sensor yielded temperatures that were on average 2.2 \u00b0C more accurate than those of the matte black sensor (p &lt; 0.0001). Further, we found positive correlations between light intensity (a proxy for incoming irradiance) and increased sensor accuracy for both sensors. Interestingly, light intensity explained 73% of the accuracy variability for the black sensor, but only 40% of the variability in accuracy deviations for the foil-shielded sensor. Unsurprisingly, an increase in air temperature led to a decrease in accuracy for both sensors, where air temperature explained 14% of the variability in accuracy for the black sensor and 31% of the accuracy variability for the foil-shielded sensor. We propose that the discrepancy between the amount of variability explained by light intensity and air temperature is due to changes in the heat energy budget arising from changes in the surface absorptivity. Additionally, we suggest fine-scale changes in river-bed reflectance led to errors in UAV thermal measurements. We conclude with a suite of guidelines for increasing the accuracy of uncooled UAV-based thermal mapping.<\/jats:p>","DOI":"10.3390\/rs14246356","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"6356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Limiting External Absorptivity of UAV-Based Uncooled Thermal Infrared Sensors Increases Water Temperature Measurement Accuracy"],"prefix":"10.3390","volume":"14","author":[{"given":"Ant\u00f3in M.","family":"O\u2019Sullivan","sequence":"first","affiliation":[{"name":"Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"},{"name":"Canadian Rivers Institute, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"},{"name":"O\u2019Sullivan Ecohydraulics Inc., Fredericton, NB E3B 7G6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8244-3838","authenticated-orcid":false,"given":"Barret L.","family":"Kurylyk","sequence":"additional","affiliation":[{"name":"Department of Civil and Resource Engineering, Dalhousie University, Halifax, NS B3H 4R2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2021WR031168","DOI":"10.1029\/2021WR031168","article-title":"Looking to the Skies: Realising the Combined Potential of Drones and Thermal Infrared Imagery to Advance Hydrological Process Understanding in Headwaters","volume":"58","author":"Dugdale","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1002\/lom3.10132","article-title":"Unmanned aerial vehicles (UAVs)-based thermal infrared (TIR) mapping, a novel approach to assess groundwater discharge into the coastal zone","volume":"14","author":"Lee","year":"2016","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107262","DOI":"10.1016\/j.compag.2022.107262","article-title":"UAV-based multispectral and thermal cameras to predict soil water content\u2014A machine learning approach","volume":"200","author":"Bertalan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vollmer, M., and M\u00f6llmann, K.P. (2010). Infrared Thermal Imaging: Fundamentals, Research and Applications. Infrared Thermal Imaging: Fundamentals, Research and Applications, Wiley-VCH Verlag GmbH & Co. KGaA.","DOI":"10.1002\/9783527630868"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6124","DOI":"10.1029\/2018GL078133","article-title":"Ultralow Surface Temperatures in East Antarctica from Satellite Thermal Infrared Mapping: The Coldest Places on Earth","volume":"45","author":"Scambos","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2005.07.007","article-title":"Accuracy and uncertainty of thermal-infrared remote sensing of stream temperatures at multiple spatial scales","volume":"100","author":"Handcock","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Handcock, R.N., Torgersen, C.E., Cherkauer, K.A., Gillespie, A.R., Tockner, K., Faux, R.N., and Tan, J. (2012). Thermal Infrared Remote Sensing of Water Temperature in Riverine Landscapes. Fluvial Remote Sensing for Science and Management, Wiley.","DOI":"10.1002\/9781119940791.ch5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1890\/1051-0761(1999)009[0301:MTRASH]2.0.CO;2","article-title":"Multiscale thermal refugia and stream habitat associations of chinook salmon in northeastern Oregon","volume":"9","author":"Torgersen","year":"1999","journal-title":"Ecol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.rse.2013.05.018","article-title":"Temporal variability of thermal refuges and water temperature patterns in an Atlantic salmon river","volume":"136","author":"Dugdale","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1002\/hyp.10506","article-title":"Rethinking the longitudinal stream temperature paradigm: Region-wide comparison of thermal infrared imagery reveals unexpected complexity of river temperatures","volume":"29","author":"Fullerton","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2020WR028122","DOI":"10.1029\/2020WR028122","article-title":"Effects of topographic resolution and geologic setting on spatial statistical river temperature models","volume":"56","author":"Devito","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"64","DOI":"10.3389\/fenvs.2020.00064","article-title":"Unmanned Aerial Vehicle (UAV)-Based Thermal Infra-Red (TIR) and Optical Imagery Reveals Multi-Spatial Scale Controls of Cold-Water Areas Over a Groundwater-Dominated Riverscape","volume":"8","author":"Pander","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e14258","DOI":"10.1002\/hyp.14258","article-title":"Drone-based characterization of intertidal spring cold-water plume dynamics","volume":"35","author":"KarisAllen","year":"2021","journal-title":"Hydrol. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1002\/hyp.13178","article-title":"Explicit consideration of preferential groundwater discharges as surface water ecosystem control points","volume":"32","author":"Briggs","year":"2018","journal-title":"Hydrol. Process."},{"key":"ref_15","first-page":"107","article-title":"The salmon-peloton: Hydraulic habitat shifts of adult Atlantic salmon (Salmo salar) due to behavioural thermoregulation","volume":"38","author":"Linnansaari","year":"2021","journal-title":"River Res. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e02768","DOI":"10.1002\/ecs2.2768","article-title":"Thermal imaging in plant and ecosystem ecology: Applications and challenges","volume":"10","author":"Still","year":"2019","journal-title":"Ecosphere"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Goddijn-murphy, L., Williamson, B.J., McIlvenny, J., and Corradi, P. (2022). Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sens., 14.","DOI":"10.3390\/rs14133179"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mokhtari, A., Ahmadi, A., Daccache, A., Drechsler, K., Fritz, S., Hu, Q., Jin, Z., Wu, W., and You, L. (2021). Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach. Remote Sens., 13.","DOI":"10.3390\/rs13122315"},{"key":"ref_19","unstructured":"Torgersen, C.E., Faux, R.N., and McIntosh, B.A. (1999). Aerial Survey of the upper McKenzie River, Oregon State University."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/S0034-4257(01)00186-9","article-title":"Airborne thermal remote sensing for water temperature assessment in rivers and streams","volume":"76","author":"Torgersen","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jenvman.2012.12.024","article-title":"Linking landscape variables to cold water refugia in rivers","volume":"118","author":"Monk","year":"2013","journal-title":"J. Environ. Manage."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2014.12.021","article-title":"Spatial distribution of thermal refuges analysed in relation to riverscape hydromorphology using airborne thermal infrared imagery","volume":"160","author":"Dugdale","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2015.12.050","article-title":"Effects of geomorphology and groundwater level on the spatio-temporal variability of riverine cold water patches assessed using thermal infrared (TIR) remote sensing","volume":"175","author":"Wawrzyniak","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.catena.2019.02.006","article-title":"The influence of landscape characteristics on the spatial variability of river temperatures","volume":"177","author":"Devito","year":"2019","journal-title":"CATENA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4721","DOI":"10.5194\/hess-26-4721-2022","article-title":"Present and future thermal regimes of intertidal groundwater springs in a threatened coastal ecosystem","volume":"26","author":"KarisAllen","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1139\/cjfas-2017-0422","article-title":"Understanding summertime thermal refuge use by adult Atlantic salmon using remote sensing, river temperature monitoring, and acoustic telemetry","volume":"75","author":"Frechette","year":"2018","journal-title":"Can. J. Fish. Aquat. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1002\/rra.3570","article-title":"Characterizing physical habitat preferences and thermal refuge occupancy of brook trout (Salvelinus fontinalis) and Atlantic salmon (Salmo salar) at high river temperatures","volume":"36","author":"Wilbur","year":"2020","journal-title":"River Res. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mejia, F.H., Torgersen, C.E., Berntsen, E.K., Maroney, J.R., Connor, J.M., Fullerton, A.H., Ebersole, J.L., and Lorang, M.S. (2020). Longitudinal, Lateral, Vertical, and Temporal Thermal Heterogeneity in a Large Impounded River: Implications for Cold-Water Refuges. Remote Sens., 12.","DOI":"10.3390\/rs12091386"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103028","DOI":"10.1016\/j.jtherbio.2021.103028","article-title":"Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","volume":"100","author":"Fuller","year":"2021","journal-title":"J. Therm. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Morgan, A.M., and O\u2019Sullivan, A.M. (2022). Cooler, bigger; warmer, smaller: Fine-scale thermal heterogeneity maps age class and species distribution in behaviourally thermoregulating salmonids. River Res. Appl.","DOI":"10.1002\/rra.4073"},{"key":"ref_31","first-page":"hyp.13557","article-title":"Ice Cover Exists (ICE): A quick method to delineate groundwater inputs in running waters for cold and temperate regions","volume":"33","author":"Linnansaari","year":"2019","journal-title":"Hydrol. Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4812","DOI":"10.1080\/01431161.2013.782113","article-title":"Prediction of water temperature heterogeneity of braided rivers using very high resolution thermal infrared (TIR) images","volume":"34","author":"Wawrzyniak","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1002\/hyp.13395","article-title":"Assessing the potential of drone-based thermal infrared imagery for quantifying river temperature heterogeneity","volume":"33","author":"Dugdale","year":"2019","journal-title":"Hydrol. Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"64","DOI":"10.3389\/feart.2018.00064","article-title":"Mapping surface temperatures on a debris-covered glacier with an unmanned aerial vehicle","volume":"6","author":"Kraaijenbrink","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mesas-Carrascosa, F.J., P\u00e9rez-Porras, F., de Larriva, J.E.M., Frau, C.M., Ag\u00fcera-Vega, F., Carvajal-Ram\u00edrez, F., Mart\u00ednez-Carricondo, P., and Garc\u00eda-Ferrer, A. (2018). Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles. Remote Sens., 10.","DOI":"10.3390\/rs10040615"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"053113","DOI":"10.1117\/1.OE.57.5.053113","article-title":"Unmanned aerial vehicle-based monitoring of groundwater inputs to surface waters using an economical thermal infrared camera","volume":"57","author":"Abolt","year":"2018","journal-title":"Opt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kelly, J., Kljun, N., Olsson, P.O., Mihai, L., Liljeblad, B., Weslien, P., Klemedtsson, L., and Eklundh, L. (2019). Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera. Remote Sens., 11.","DOI":"10.3390\/rs11050567"},{"key":"ref_38","first-page":"24","article-title":"Optical Thermal Imaging\u2014Replacing microbolometer technology and achieving universal deployment","volume":"19","author":"Ostrower","year":"2006","journal-title":"III-Vs Rev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ravindra, N.M. (2021). Microbolometers: Fundamentals, Materials, and Recent Developments, Elsevier.","DOI":"10.1016\/B978-0-08-102812-4.00010-3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1364\/AO.51.001788","article-title":"Thermal drift compensation method for microbolometer thermal cameras","volume":"51","author":"Olbrycht","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/hyp.13332","article-title":"Efficient hydrogeological characterization of remote stream corridors using drones","volume":"33","author":"Briggs","year":"2019","journal-title":"Hydrol. Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.scitotenv.2018.12.457","article-title":"Relative information from thermal infrared imagery via unoccupied aerial vehicle informs simulations and spatially-distributed assessments of stream temperature","volume":"661","author":"Caldwell","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/j.1745-6584.2005.00052.x","article-title":"Heat as a Ground Water Tracer","volume":"43","author":"Anderson","year":"2005","journal-title":"Ground Water"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ribeiro-Gomes, K., Hern\u00e1ndez-L\u00f3pez, D., Ortega, J.F., Ballesteros, R., Poblete, T., and Moreno, M.A. (2017). Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors, 17.","DOI":"10.3390\/s17102173"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Furze, S., O\u2019Sullivan, A.M., Allard, S., Pronk, T., and Curry, R.A. (2021). A High-Resolution, Random Forest Approach to Mapping Depth-to-Bedrock across Shallow Overburden and Post-Glacial Terrain. Remote Sens., 13.","DOI":"10.3390\/rs13214210"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rampton, V.N., Gauthier, R.C., Thibault, J., and Seaman, A.A. (1984). Quaternary Geology of New Brunswick, Geological Survey of Canada.","DOI":"10.4095\/119730"},{"key":"ref_47","unstructured":"Touloukian, Y.S., and Buyco, E.H. (1971). Thermophysical Properties of Matter\u2014The TPRC Data Series. Volume 4. Specific Heat\u2014Metallic Elements and Alloys, Thermophysical and Electronic Properties Information Analysis Center."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wan, Q., Brede, B., Smigaj, M., and Kooistra, L. (2021). Factors Influencing Temperature Measurements from Miniaturized Thermal Infrared (TIR) Cameras: A Laboratory-Based Approach. Sensors, 21.","DOI":"10.3390\/s21248466"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Maguire, M.S., Neale, C.M.U., and Woldt, W.E. (2021). Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications. Remote Sens., 13.","DOI":"10.3390\/rs13091635"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e2295","DOI":"10.1002\/eco.2295","article-title":"An ecohydrological typology for thermal refuges in streams and rivers","volume":"14","author":"Sullivan","year":"2021","journal-title":"Ecohydrology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4486","DOI":"10.1002\/2016WR018808","article-title":"Combined use of thermal methods and seepage meters to efficiently locate, quantify, and monitor focused groundwater discharge to a sand-bed stream","volume":"52","author":"Rosenberry","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jhydrol.2015.09.059","article-title":"A comparison of thermal infrared to fiber-optic distributed temperature sensing for evaluation of groundwater discharge to surface water","volume":"530","author":"Hare","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Aragon, B., Johansen, K., Parkes, S., Malbeteau, Y., Al-mashharawi, S., Al-amoudi, T., Andrade, C.F., Turner, D., Lucieer, A., and McCabe, M.F. (2020). A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments. Sensors, 20.","DOI":"10.3390\/s20113316"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Virtue, J., Turner, D., Williams, G., Zeliadt, S., McCabe, M., and Lucieer, A. (2021). Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter. Drones, 5.","DOI":"10.3390\/drones5040119"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6356\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:11Z","timestamp":1760146931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":54,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246356"],"URL":"https:\/\/doi.org\/10.3390\/rs14246356","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}