Web of Science: 2 citations, Scopus: 2 citations, Google Scholar: citations,
Radiometric correction of Landsat-8 and Sentinel-2A scenes using drone imagery in synergy with field spectroradiometry
Padró Garcia, Joan-Cristian (Universitat Autònoma de Barcelona. Departament de Geografia)
Muñoz, Francisco-Javier (HEMAV S.L.)
Ávila, Luis Ávila (Universidad del Azuay. Instituto de Estudios de Régimen Seccional del Ecuador)
Pesquer Mayos, Lluís (Centre de Recerca Ecològica i Aplicacions Forestals)
Pons, Xavier (Universitat Autònoma de Barcelona. Departament de Geografia)

Date: 2018
Abstract: 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 (R > 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 < 2. 484%), but yielded poor results in the near-infrared (NIR) band (RMSE > 6. 688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2. 018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2. 680%), Sen2Cor-SNAP (RMSE < 2. 192%), and PIA-MiraMon (RMSE < 3. 130%), but did not achieve sufficient results in the NIR band (RMSE < 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 < 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.
Note: Número d'acord de subvenció EC/H2020/641762
Note: Número d'acord de subvenció MINECO/CGL2015-69888-P
Note: Número d'acord de subvenció AGAUR/2017/SGR-1690
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Radiometric correction ; Landsat-8 ; OLI ; Sentinel-2 ; MSI ; UAS ; MicaSense RedEdge ; Field spectroradiometry ; Upscaling
Published in: Remote Sensing, Vol. 10, Núm. 11 (November 2018) , art. 1687, ISSN 2072-4292

DOI: 10.3390/rs10111687


26 p, 7.2 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (scientific output) > Experimental sciences > Grupo de Investigación Métodos y Aplicaciones en Teledetección y Sistemas de Información Geográfica (GRUMETS)
Research literature > UAB research groups literature > Research Centres and Groups (scientific output) > Experimental sciences > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Research articles
Articles > Published articles

 Record created 2019-01-16, last modified 2019-09-27



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