{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:42:25Z","timestamp":1764175345633,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Labsphere"},{"name":"RIT"},{"name":"NRC"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The objective of the Ground to Space CALibration Experiment (G-SCALE) is to demonstrate the use of convex mirrors as a radiometric and spatial calibration and validation technology for Earth Observation assets, operating at multiple altitudes and spatial scales. Specifically, point sources with NIST-traceable absolute radiance signal are evaluated for simultaneous vicarious calibration of multi- and hyperspectral sensors in the VNIR\/SWIR range, aboard Unmanned Aerial Vehicles (UAVs), manned aircraft, and satellite platforms. We introduce the experimental process, field site, instrumentation, and preliminary results of the G-SCALE, providing context for forthcoming papers that will detail the results of intercomparison between sensor technologies and remote sensing applications utilizing the mirror-based calibration approach, which is scalable across a wide range of pixel sizes with appropriate facilities. The experiment was carried out at the Rochester Institute of Technology\u2019s Tait Preserve in Penfield, NY, USA on 23 July 2021. The G-SCALE represents a unique, international collaboration between commercial, academic, and government entities for the purpose of evaluating a novel method to improve vicarious calibration and validation for Earth Observation.<\/jats:p>","DOI":"10.3390\/rs15020294","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T04:08:07Z","timestamp":1672805287000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Ground to Space CALibration Experiment (G-SCALE): Simultaneous Validation of UAV, Airborne, and Satellite Imagers for Earth Observation Using Specular Targets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-4841","authenticated-orcid":false,"given":"Brandon J.","family":"Russell","sequence":"first","affiliation":[{"name":"Labsphere, Inc., North Sutton, NH 03260, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-5871","authenticated-orcid":false,"given":"Raymond J.","family":"Soffer","sequence":"additional","affiliation":[{"name":"Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A-0R6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3643-8245","authenticated-orcid":false,"given":"Emmett J.","family":"Ientilucci","sequence":"additional","affiliation":[{"name":"Digital Imaging and Remote Sensing Laboratory, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4872-5118","authenticated-orcid":false,"given":"Michele A.","family":"Kuester","sequence":"additional","affiliation":[{"name":"Maxar Technologies, Longmont, CO 80503, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9225-8575","authenticated-orcid":false,"given":"David N.","family":"Conran","sequence":"additional","affiliation":[{"name":"Digital Imaging and Remote Sensing Laboratory, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0287-8960","authenticated-orcid":false,"given":"Juan Pablo","family":"Arroyo-Mora","sequence":"additional","affiliation":[{"name":"Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A-0R6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tina","family":"Ochoa","sequence":"additional","affiliation":[{"name":"Maxar Technologies, Longmont, CO 80503, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Durell","sequence":"additional","affiliation":[{"name":"Labsphere, Inc., North Sutton, NH 03260, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeff","family":"Holt","sequence":"additional","affiliation":[{"name":"Labsphere, Inc., North Sutton, NH 03260, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.spacepol.2016.05.005","article-title":"A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth","volume":"36","author":"Kansakar","year":"2016","journal-title":"Space Policy"},{"key":"ref_2","unstructured":"Rice, K. (2018). Convolutional Neural Networks for Detection and Classification of Maritime Vessels in Eletro-Optical Satellite Imagery. [Ph.D. Thesis, Naval Postgraduate School]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Agapiou, A. (2020). Evaluation of Landsat 8 OLI\/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies. Remote Sens., 12.","DOI":"10.3390\/rs12030579"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., Bovolo, F., Bruzzone, L., and Gege, P. (2020). Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon. Remote Sens., 12.","DOI":"10.3390\/rs12152381"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13717-020-00255-4","article-title":"Current and near-term advances in Earth observation for ecological applications","volume":"10","author":"Ustin","year":"2021","journal-title":"Ecol. Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Tian, T., and Yin, J. (2021). A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China. Remote Sens., 13.","DOI":"10.3390\/rs13061221"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Akumu, C.E., Amadi, E.O., and Dennis, S. (2021). Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding. Land, 10.","DOI":"10.3390\/land10030321"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"319","DOI":"10.3389\/feart.2020.00319","article-title":"Multiscale Integration of High-Resolution Spaceborne and Drone-Based Imagery for a High-Accuracy Digital Elevation Model Over Tristan da Cunha","volume":"8","author":"Backes","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bracaglia, M., Santoleri, R., Volpe, G., Colella, S., Benincasa, M., and Brando, V.E. (2020). A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability. Remote Sens., 12.","DOI":"10.3390\/rs12101539"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hegarty-Craver, M., Polly, J., O\u2019Neil, M., Ujeneza, N., Rineer, J., Beach, R.H., Lapidus, D., and Temple, D.S. (2020). Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth. Remote Sens., 12.","DOI":"10.3390\/rs12121984"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kopp, S., Becker, P., Doshi, A., Wright, D.J., Zhang, K., and Xu, H. (2019). Achieving the Full Vision of Earth Observation Data Cubes. Data, 4.","DOI":"10.3390\/data4030094"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1016\/j.rse.2010.04.002","article-title":"Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues","volume":"114","author":"Maritorena","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20092530"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nikolakopoulos, K., Kyriou, A., Koukouvelas, I., Zygouri, V., and Apostolopoulos, D. (2019). Combination of Aerial, Satellite, and UAV Photogrammetry for Mapping the Diachronic Coastline Evolution: The Case of Lefkada Island. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110489"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Niro, F., Goryl, P., Dransfeld, S., Boccia, V., Gascon, F., Adams, J., Themann, B., Scifoni, S., and Doxani, G. (2021). European Space Agency (ESA) Calibration\/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability. Remote Sens., 13.","DOI":"10.3390\/rs13153003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sogacheva, L., Popp, T., Sayer, A.M., Dubovik, O., Garay, M.J., Heckel, A., Hsu, N.C., Jethva, H., Kahn, R.A., and Kolmonen, P. (2019). Merging Regional and Global AOD Records from 15 Available Satellite Products, European Geosciences Union. Available online: https:\/\/acp.copernicus.org\/articles\/20\/2031\/2020\/.","DOI":"10.5194\/acp-2019-446"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.envsci.2020.04.005","article-title":"Addressing the need for improved land cover map products for policy support","volume":"112","author":"Szantoi","year":"2020","journal-title":"Environ. Sci. Policy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/JSTARS.2021.3065408","article-title":"An Advanced Framework for Merging Remotely Sensed Soil Moisture Products at the Regional Scale Supported by Error Structure Analysis: A Case Study on the Tibetan Plateau","volume":"14","author":"Kang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2016.02.014","article-title":"Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets","volume":"177","author":"Sclep","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6843","DOI":"10.1080\/01431161.2014.960612","article-title":"Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau","volume":"35","author":"Shen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bouvet, M., Thome, K., Berthelot, B., Bialek, A., Czapla-Myers, J., Fox, N.P., Goryl, P., Henry, P., Ma, L., and Marcq, S. (2019). RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range. Remote Sens., 11.","DOI":"10.3390\/rs11202401"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Helder, D., Anderson, C., Beckett, K., Houborg, R., Zuleta, I., Boccia, V., Clerc, S., Kuester, M., Markham, B., and Pagnutti, M. (2020). Observations and Recommendations for Coordinated Calibration Activities of Government and Commercial Optical Satellite Systems. Remote Sens., 12.","DOI":"10.3390\/rs12152468"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100014","DOI":"10.1016\/j.srs.2021.100014","article-title":"Characterization of Planetscope-0 Planetscope-1 surface reflectance and normalized difference vegetation index continuity","volume":"3","author":"Huang","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lewis, A., Lacey, J., Mecklenburg, S., Ross, J., Siqueira, A., Killough, B., Szantoi, Z., Tadono, T., Rosenavist, A., and Goryl, P. (2018, January 22\u201327). CEOS Analysis Ready Data for Land (CARD4L) Overview. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519255"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Manivasagam, V., Kaplan, G., and Rozenstein, O. (2019). Developing Transformation Functions for VENUS and Sentinel-2 Surface Reflectance over Israel. Remote Sens., 11.","DOI":"10.3390\/rs11141710"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shrestha, M., Hasan, N., Leigh, L., and Helder, D. (2019). Derivation of Hyperspectral Profile of Extended Pseudo Invariant Calibration Sites (EPICS) for Use in Sensor Calibration. Remote Sens., 11.","DOI":"10.3390\/rs11192279"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Siqueira, A., Lewis, A., Thankappan, M., Szantoi, Z., Goryl, P., Labahn, S., Ross, J., Hosford, S., Mecklenburg, S., and Tadono, T. (August, January 28). CEOS Analysis Ready Data For Land\u2014An Overview on the Current and Future Work. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899846"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Le Roux, J., Christopher, S., and Maskey, M. (2021). Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research. Remote Sens., 13.","DOI":"10.3390\/rs13152981"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5135","DOI":"10.1109\/TGRS.2019.2897068","article-title":"Vicarious Calibration of Orbiting Carbon Observatory-2","volume":"57","author":"Bruegge","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1109\/TGRS.2010.2089527","article-title":"Vicarious Calibration of the GOSAT Sensors Using the Railroad Valley Desert Playa","volume":"49","author":"Kuze","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sterckx, S., and Wolters, E. (2019). Radiometric Top-of-Atmosphere Reflectance Consistency Assessment for Landsat 8\/OLI, Sentinel-2\/MSI, PROBA-V, and DEIMOS-1 over Libya-4 and RadCalNet Calibration Sites. Remote Sens., 11.","DOI":"10.3390\/rs11192253"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jin, C., Ahn, H., Seo, D., and Choi, C. (2020). Radiometric Calibration and Uncertainty Analysis of KOMPSAT-3A Using the Reflectance-Based Method. Sensors, 20.","DOI":"10.3390\/s20092564"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"296","DOI":"10.3389\/fmars.2017.00296","article-title":"Intercomparison of Approaches to the Empirical Line Method for Vicarious Hyperspectral Reflectance Calibration","volume":"4","author":"Ortiz","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mina\u0159\u00edk, R., Langhammer, J., and Hanu\u0161, J. (2019). Radiometric and Atmospheric Corrections of Multispectral micro-MCA Camera for UAV Spectroscopy. Remote Sens., 11.","DOI":"10.3390\/rs11202428"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1080\/014311699211994","article-title":"The use of the empirical line method to calibrate remotely sensed data to reflectance","volume":"20","author":"Smith","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bruzzone, L. (2015). Sentinel-2 geometric image quality commissioning: First results. Image and Signal Processing for Remote Sensing XXI, SPIE Remote Sensing.","DOI":"10.1117\/12.2194339"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"489","DOI":"10.5194\/isprs-archives-XLI-B1-489-2016","article-title":"Assessment of the geometric quality of sentinel-2 data","volume":"XLI-B1","author":"Mihajlovic","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.3390\/rs70201962","article-title":"Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor","volume":"7","author":"Wenny","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shrestha, M., Hasan, M.N., Leigh, L., and Helder, D. (2019). Extended Pseudo Invariant Calibration Sites (EPICS) for the Cross-Calibration of Optical Satellite Sensors. Remote Sens., 11.","DOI":"10.3390\/rs11141676"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"78130E","DOI":"10.1117\/12.864071","article-title":"The Specular Array Radiometric Calibration (SPARC) method: A new approach for absolute vicarious calibration in the solar reflective spectrum","volume":"Volume 7813","author":"Ardanuy","year":"2010","journal-title":"Remote Sensing System Engineering III"},{"key":"ref_43","unstructured":"Schiller, S.J., Teter, M., and Silny, J. (2017, January 10). Comprehensive vicarious calibration and characterization of a small satellite constellation using the Specular ARray Calibration (SPARC) method. Proceedings of the Session 6: Science Mission Payloads 1, Logan, UT, USA."},{"key":"ref_44","unstructured":"Schiller, S., and Silny, J. (2016, January 25). Using vicarious calibration to evaluate small target radiometry. Proceedings of the Calcon 2016; 25th Annual Meeting on Characterization and Radiometric Calibration for Remote Sensing, Logan, UT, USA."},{"key":"ref_45","unstructured":"Butler, J.J., Xiong, X.J., and Gu, X. (2020). Initial results of the FLARE vicarious calibration network. Earth Observing Systems XXV, SPIE."},{"key":"ref_46","unstructured":"Schiller, S.J. (2019, January 24). Demonstration of the Mirror-based Empirical Line Method (MELM). Proceedings of the 18th Annual Joint Agency Commercial Imagery Evaluation (JACIE) Workshop, Reston, VA, USA."},{"key":"ref_47","unstructured":"Joseph, G. (2005). Fundamentals of Remote Sensing, 2nd ed, Universities Press (India) Private Limited."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1080\/07038992.2019.1650334","article-title":"Validation of Airborne Hyperspectral Imagery from Panel Characterization to Image Quality Assessment Implications for an Arctic Peatland Surrogate Simulation Site Laboratory","volume":"45","author":"Soffer","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_49","first-page":"717","article-title":"SpecTIR hyperspectral airborne Rochester experiment data collection campaign","volume":"Volume 8390","author":"Shen","year":"2012","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII"},{"key":"ref_50","first-page":"94","article-title":"The SHARE 2012 data campaign","volume":"Volume 8743","author":"Shen","year":"2013","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX"},{"key":"ref_51","first-page":"125","article-title":"SHARE 2012: Analysis of illumination differences on targets in hyperspectral imagery","volume":"Volume 8743","author":"Shen","year":"2013","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX"},{"key":"ref_52","first-page":"57","article-title":"Using a new GUI tool to leverage LiDAR data to aid in hyperspectral image material detection in the radiance domain on RIT SHARE LiDAR\/HSI data","volume":"Volume 8870","author":"Mouroulis","year":"2013","journal-title":"Imaging Spectrometry XVIII"},{"key":"ref_53","first-page":"171","article-title":"Target detection assessment of the SHARE 2010\/2012 hyperspectral data collection campaign","volume":"Volume 9472","author":"Kruse","year":"2015","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI"},{"key":"ref_54","first-page":"121","article-title":"New SHARE 2010 HSI-LiDAR dataset: Re-calibration, detection assessment and delivery","volume":"Volume 9976","author":"Silny","year":"2016","journal-title":"Imaging Spectrometry XXI"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/07038992.2016.1160771","article-title":"Quality Control Assessment of the Mission Airborne Carbon 13 (MAC-13) Hyperspectral Imagery from Costa Rica","volume":"42","author":"Kalacska","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_56","unstructured":"ITRES Research Limited (2008). CASI Instrument Manual, ITRES Research Limited."},{"key":"ref_57","unstructured":"Soffer, R. (2008). Hyperspectral Reflectance in Support of Biomass Monitoring of Agricultural Fields\u2014Part 1: Image Georectification Support, AAFC Contract No. 3000362053."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6666","DOI":"10.1109\/TGRS.2017.2731399","article-title":"Detection and Correction of Spectral Shift Effects for the Airborne Prism Experiment","volume":"55","author":"Jia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chapman, J., Thompson, D., Helmlinger, M., Bue, B., Green, R., Eastwood, M., Geier, S., Olson-Duvall, W., and Lundeen, S. (2019). Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). Remote Sens., 11.","DOI":"10.3390\/rs11182129"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Soffer, R., Ifimov, G., Pan, Y., and Belanger, S. (2021, January 19\u201323). Acquisition and Spectroradiometric Assessment of the Novel WaterSat Imaging Spectrometer Experiment (WISE) Sensor for the Mapping of Optically Shallow Coastal Waters. Proceedings of the Hyperspectral Imaging and Sounding of the Environment 2021, Washington, DC, USA.","DOI":"10.1364\/HISE.2021.HF4E.4"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"600","DOI":"10.3390\/rs70100600","article-title":"The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI","volume":"7","author":"McCorkel","year":"2015","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Pacifici, F. (2016, January 10\u201315). Validation of the DigitalGlobe surface reflectance product. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729508"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Conran, D., and Ientilucci, E.J. (2022, January 17\u201322). Interrogating UAV Image and Data Quality Using Convex Mirrors. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883984"},{"key":"ref_64","unstructured":"Pinto, C.T. (2016). Uncertainty Evaluation for In-Flight Radiometric Calibration of Earth Observation Sensors. [Ph.D. Thesis, Instituto Nacional de Pesquisas Espaciais (INPE)]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:58:27Z","timestamp":1760119107000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,4]]},"references-count":64,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020294"],"URL":"https:\/\/doi.org\/10.3390\/rs15020294","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,1,4]]}}}