{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:41:45Z","timestamp":1775738505992,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,26]],"date-time":"2018-10-26T00:00:00Z","timestamp":1540512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020 641762-2 EC"],"award-info":[{"award-number":["H2020 641762-2 EC"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["CGL2015-69888-P"],"award-info":[{"award-number":["CGL2015-69888-P"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["SGR2017-1690"],"award-info":[{"award-number":["SGR2017-1690"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["2016B_00410"],"award-info":[{"award-number":["2016B_00410"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 &gt; 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS &lt; 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR &gt; 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS &lt; 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE &lt; 2.680%), Sen2Cor-SNAP (RMSE &lt; 2.192%), and PIA-MiraMon (RMSE &lt; 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR &lt; 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS &lt; 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.<\/jats:p>","DOI":"10.3390\/rs10111687","type":"journal-article","created":{"date-parts":[[2018,10,26]],"date-time":"2018-10-26T10:51:01Z","timestamp":1540551061000},"page":"1687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2034-7856","authenticated-orcid":false,"given":"Joan-Cristian","family":"Padr\u00f3","sequence":"first","affiliation":[{"name":"Grumets Research Group, Dep. Geografia, Edifici B, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Catalonia, Spain"}]},{"given":"Francisco-Javier","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"HEMAV S.L. Edifici RDIT, 08860 Castelldefels, Catalonia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6027-4340","authenticated-orcid":false,"given":"Luis","family":"\u00c1vila","sequence":"additional","affiliation":[{"name":"Instituto de Estudios de R\u00e9gimen Seccional del Ecuador (IERSE), Universidad del Azuay, 010107 Cuenca, Azuay, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-2468","authenticated-orcid":false,"given":"Llu\u00eds","family":"Pesquer","sequence":"additional","affiliation":[{"name":"Grumets Research Group, CREAF, Edifici C, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Catalonia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6924-1641","authenticated-orcid":false,"given":"Xavier","family":"Pons","sequence":"additional","affiliation":[{"name":"Grumets Research Group, Dep. Geografia, Edifici B, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Catalonia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,26]]},"reference":[{"key":"ref_1","unstructured":"United Nations (UN) (2018, August 07). Transforming Our World: The 2030 Agenda for Sustainable Development. Available online: http:\/\/www.refworld.org\/docid\/57b6e3e44.html."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Emery, W., and Camps, A. (2017). Introduction to Satellite Remote Sensing, Elsevier. [1st ed.].","DOI":"10.1016\/B978-0-12-809254-5.00001-4"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Diaz-Delgado, R., Lucas, R., and Hurford, C. (2017). The Roles of Remote Sensing in Nature Conservation: A Practical Guide and Case Studies, Springer. [1st ed.].","DOI":"10.1007\/978-3-319-64332-8"},{"key":"ref_4","unstructured":"Group on Earth Observation (GEO) (2018, August 07). 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