{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:01:20Z","timestamp":1781280080072,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of\u2013the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.<\/jats:p>","DOI":"10.3390\/rs12152366","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:26:01Z","timestamp":1595503561000},"page":"2366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["PlanetScope Radiometric Normalization and Sentinel-2 Super-Resolution (2.5 m): A Straightforward Spectral-Spatial Fusion of Multi-Satellite Multi-Sensor Images Using Residual Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3822-315X","authenticated-orcid":false,"given":"Nicolas","family":"Latte","sequence":"first","affiliation":[{"name":"Forest is Life, ULi\u00e8ge \u2013 Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-9673","authenticated-orcid":false,"given":"Philippe","family":"Lejeune","sequence":"additional","affiliation":[{"name":"Forest is Life, ULi\u00e8ge \u2013 Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Unninayar, S., and Olsen, L.M. (2015). Monitoring, observations, and remote sensing\u2014Global dimensions. Reference Module in Earth Systems and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.09572-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A critical comparison among pansharpening algorithms","volume":"53","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Garzelli, A. (2016). A review of image fusion algorithms based on the super-resolution paradigm. Remote Sens., 8.","DOI":"10.3390\/rs8100797"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.inffus.2018.05.006","article-title":"Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges","volume":"46","author":"Meng","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","article-title":"Pixel-Level image fusion: A survey of the state of the art","volume":"33","author":"Li","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/JSTARS.2018.2805923","article-title":"Remote sensing image fusion with deep convolutional neural network","volume":"11","author":"Shao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1109\/LGRS.2017.2736020","article-title":"Boosting the accuracy of multispectral image pansharpening by learning a deep residual network","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gargiulo, M., Mazza, A., Gaetano, R., Ruello, G., and Scarpa, G. (2018, January 22\u201327). A CNN-Based fusion method for super-resolution of Sentinel-2 data. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518447"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, J., He, Z., and Wu, J. (2019). Deep self-learning network for adaptive pansharpening. Remote Sens., 11.","DOI":"10.3390\/rs11202395"},{"key":"ref_13","unstructured":"Pohl, C. (2015, January 5\u20139). Multisensor image fusion guidelines in remote sensing. Proceedings of the 9th Symposium of the International Society for Digital Earth (ISDE), Halifax, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-Resolution image reconstruction: A technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111425","DOI":"10.1016\/j.rse.2019.111425","article-title":"Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product","volume":"235","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Feng","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1109\/TGRS.2017.2683444","article-title":"Fusion of Landsat 8 OLI and Sentinel-2 MSI Data","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/JPROC.2012.2190811","article-title":"Very high-resolution remote sensing: Challenges and opportunities [Point of View]","volume":"100","author":"Benediktsson","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/JPROC.2012.2237076","article-title":"Advances in very-high-resolution remote sensing","volume":"101","author":"Benediktsson","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_21","first-page":"883","article-title":"Single-Image super resolution for multispectral remote sensing data using convolutional neural networks","volume":"41B3","author":"Liebel","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lanaras, C., Bioucas-Dias, J., Baltsavias, E., and Schindler, K. (2017, January 21\u201326). Super-Resolution of multispectral multiresolution images from a single sensor. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.194"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-Resolution of Sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Palsson, F., Sveinsson, R.J., and Ulfarsson, O.M. (2018). Sentinel-2 Image fusion using a deep residual network. Remote Sens., 10.","DOI":"10.3390\/rs10081290"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gargiulo, M., Mazza, A., Gaetano, R., Ruello, G., and Scarpa, G. (2019). Fast super-resolution of 20 m Sentinel-2 bands using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11222635"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9589","DOI":"10.1109\/TGRS.2019.2927766","article-title":"Sentinel-2A image fusion using a machine learning approach","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6408","DOI":"10.1109\/TGRS.2019.2906048","article-title":"Sentinel-2 sharpening using a reduced-rank method","volume":"57","author":"Ulfarsson","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, J., He, Z., and Hu, J. (2020). Sentinel-2 Sharpening via parallel residual network. Remote Sens., 12.","DOI":"10.3390\/rs12020279"},{"key":"ref_29","first-page":"95","article-title":"Super-Resolution for sentinel-2 images","volume":"XLII-2\/W16","author":"Galar","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, J., Li, J., Yuan, Q., Li, H., and Shen, H. (2019). Spatial\u2013Spectral fusion in different swath widths by a recurrent expanding residual convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11192203"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104893","DOI":"10.1016\/j.compag.2019.104893","article-title":"Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies","volume":"164","author":"Leach","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Houborg, R., and McCabe, F.M. (2018). Daily retrieval of NDVI and LAI at 3 m resolution via the fusion of cubesat, landsat, and MODIS data. Remote Sens., 10.","DOI":"10.3390\/rs10060890"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2018.02.067","article-title":"A cubesat enabled Spatio-temporal enhancement method (CESTEM) utilizing planet, landsat and MODIS data","volume":"209","author":"Houborg","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"R core team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: http:\/\/www.R-project.org\/."},{"key":"ref_35","unstructured":"Hijmans, R.J. (2020, May 01). Raster: Geographic Data Analysis and Modeling. Available online: https:\/\/cran.r-project.org\/web\/packages\/raster\/raster.pdf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"439","DOI":"10.32614\/RJ-2018-009","article-title":"Simple features for R: Standardized support for spatial vector data","volume":"10","author":"Pebesma","year":"2018","journal-title":"R J."},{"key":"ref_37","unstructured":"Allaire, J.J., and Chollet, F. (2020, May 01). Keras: R Interface to \u201cKeras\u201d. Available online: https:\/\/keras.rstudio.com\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"GDAL\/OGR Contributors (2020). GDAL\/OGR Geospatial Data Abstraction Software Library, Open Source Geospatial Foundation.","DOI":"10.22224\/gistbok\/2020.4.1"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Inglada, J., and Christophe, E. (2009, January 12\u201317). The Orfeo Toolbox remote sensing image processing software. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417481"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospatial Data Softw. Stand."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Laviron, X. (2020, May 01). theiaR: Download and Manage Data from Theia. Available online: https:\/\/cran.r-project.org\/web\/packages\/theiaR\/theiaR.pdf.","DOI":"10.32614\/CRAN.package.theiaR"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lonjou, V., Desjardins, C., Hagolle, O., Petrucci, B., Tremas, T., Dejus, M., Makarau, A., and Auer, S. (2016). MACCS-ATCOR Joint Algorithm (MAJA). Remote Sensing of Clouds and the Atmosphere XXI, International Society for Optics and Photonics.","DOI":"10.1117\/12.2240935"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of copernicus sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask Processors Using reference cloud masks generated with a supervised active learning procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sanchez, H.A., Picoli, C.A.M., Camara, G., Andrade, R.P., Chaves, E.D.M., Lechler, S., Soares, R.A., Marujo, F.B.R., Sim\u00f5es, E.O.R., and Ferreira, R.K. (2020). Comparison of Cloud cover detection algorithms on sentinel\u20132 images of the amazon tropical forest. Remote Sens., 12.","DOI":"10.3390\/rs12081284"},{"key":"ref_45","unstructured":"Leutner, B., Horning, N., and Schwalb-Willmann, J. (2020, May 01). RStoolbox: Tools for Remote Sensing Data Analysis. Available online: https:\/\/cran.r-project.org\/web\/packages\/RStoolbox\/RStoolbox.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., and Hostert, P. (2017). AROSICS: An automated and robust open-source image co-registration software for multi-sensor satellite data. Remote Sens., 9.","DOI":"10.3390\/rs9070676"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K. (2017, January 21\u201327). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_50","first-page":"691","article-title":"Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images","volume":"63","author":"Wald","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-Time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201323). Residual dense network for image super-resolution. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_53","unstructured":"Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., and Shi, W. (2017). Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Odena, A., Dumoulin, V., and Olah, C. (2016). Deconvolution and checkerboard artifacts. Distill.","DOI":"10.23915\/distill.00003"},{"key":"ref_55","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. Int. Conf. Learn. Represent."},{"key":"ref_56","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Data-Dependence of plateau phenomenon in learning with neural network\u2014statistical mechanical analysis. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss functions for image restoration with neural networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Barron, J.T. (2019, January 16\u201320). A general and adaptive robust loss function. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition, 300 E Ocean Blvd, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00446"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_60","first-page":"4","article-title":"Comparison of image quality metrics","volume":"1","author":"Silpa","year":"2012","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5194\/isprs-annals-IV-2-W7-47-2019","article-title":"Data augmentation approaches for satellite image super-resolution","volume":"42W7","author":"Ghaffar","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yoo, J., Ahn, N., and Sohn, K.-A. (2020, January 16\u201318). Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00840"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Qiu, S., Xu, X., and Cai, B. (2018, January 20\u201324). FReLU: Flexible rectified linear units for improving convolutional neural networks. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546022"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:51:11Z","timestamp":1760176271000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12152366"],"URL":"https:\/\/doi.org\/10.3390\/rs12152366","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]}}}