{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T12:35:50Z","timestamp":1774960550084,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,11,5]],"date-time":"2017-11-05T00:00:00Z","timestamp":1509840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested \u201cabsolutely\u201d (in respect of a selected reference frame) and \u201crelatively\u201d (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow.<\/jats:p>","DOI":"10.3390\/jimaging3040049","type":"journal-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T11:39:38Z","timestamp":1509968378000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring"],"prefix":"10.3390","volume":"3","author":[{"given":"Andrea","family":"Lessio","sequence":"first","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, University of Turin, Largo Braccini 2, 10095 Grugliasco (TO), Italy"}]},{"given":"Vanina","family":"Fissore","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, University of Turin, Largo Braccini 2, 10095 Grugliasco (TO), Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-8013","authenticated-orcid":false,"given":"Enrico","family":"Borgogno-Mondino","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, University of Turin, Largo Braccini 2, 10095 Grugliasco (TO), Italy"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,5]]},"reference":[{"key":"ref_1","unstructured":"(2017, July 03). Copernicus Programme. Available online: http:\/\/ec.europa.eu\/growth\/sectors\/space\/copernicus\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2013.06.004","article-title":"Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect","volume":"137","author":"Hill","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.rse.2016.01.017","article-title":"Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data","volume":"176","author":"Laurin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1,-2, and-3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"(2017, November 04). Harmonized Landsat Sentinel-2, Available online: https:\/\/hls.gsfc.nasa.gov\/."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mandanici, E., and Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens., 8.","DOI":"10.3390\/rs8121014"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0168-1699(02)00106-0","article-title":"Mapping vineyard leaf area with multispectral satellite imagery","volume":"38","author":"Johnson","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1080\/01431160710155974","article-title":"Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images","volume":"24","author":"Thenkabail","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eja.2012.12.001","article-title":"A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems","volume":"46","author":"Delegido","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multi sensor multi-resolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"285","DOI":"10.5721\/EuJRS20144718","article-title":"Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates","volume":"47","author":"Testa","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MGRS.2015.2434351","article-title":"Fusing Landsat and MODIS data for vegetation monitoring","volume":"3","author":"Gao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5589\/m02-096","article-title":"Disturbance recognition in the boreal forest using radar and Landsat-7","volume":"29","author":"Ranson","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS\u2013Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","unstructured":"Hall, D.L., and McMullen, S.A. (2004). Mathematical Techniques in Multisensor Data Fusion, Artech House."},{"key":"ref_23","unstructured":"Wu, M., and Wang, C. (2011, January 24\u201326). Spatial and Temporal Fusion of Remote Sensing Data using wavelet transform. Proceedings of the 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE), Nanjing, China."},{"key":"ref_24","first-page":"203","article-title":"A Preliminary Comparison between Landsat-8 OLI and Sentinel-2 MSI for Geological Applications","volume":"740","author":"Nikolakopoulos","year":"2016","journal-title":"Living Planet Symp."},{"key":"ref_25","first-page":"112","article-title":"A Data Fusion Approach for the Production of Impervious Surface Area Estimates Using Sentinel-1 A and Landsat-8 Data","volume":"740","author":"Mantas","year":"2016","journal-title":"Living Planet Symp."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"21117","DOI":"10.3390\/s141121117","article-title":"Remote sensing of ecosystem health: Opportunities, challenges, and future perspectives","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Sannier, C., and Corpetti, T. (2016). Monitoring urban areas with Sentinel-2A data: Application to the update of the Copernicus high resolution layer imperviousness degree. Remote Sens., 8.","DOI":"10.3390\/rs8070606"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4952","DOI":"10.1080\/01431161.2014.933280","article-title":"Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas","volume":"35","author":"Vanonckelen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","first-page":"436","article-title":"Evaluation and parameterization of ATCOR3 topographic correction method for forest cover mapping in mountain areas","volume":"18","author":"Balthazar","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoin. Form."},{"key":"ref_30","first-page":"1414","article-title":"FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation","volume":"3","author":"Cooley","year":"2002","journal-title":"Geosci. Remote Sens. Symp."},{"key":"ref_31","unstructured":"Rouse, J.W. (2017, November 04). Monitoring the Vernal advancement and Retrogradation (Greenwave Effect) of Natural Vegetation.NASA\/GSFCT Type III Final Report, Available online: https:\/\/ntrs.nasa.gov\/search.jsp?R=19740022555."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Borgogno Mondino, E., and Lessio, A. (2015, January 26\u201331). Estimation and Mapping of NDVI Uncertainty from Landsat 8 OLI datasets: An Operational Approach. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325842"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"137","DOI":"10.5721\/EuJRS20164908","article-title":"A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis","volume":"49","author":"Lessio","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_35","unstructured":"(2017, July 03). Earth Explorer, Available online: http:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_36","unstructured":"European Space Agency (2013). Sentinel-2 User Handbook, European Space Agency."},{"key":"ref_37","unstructured":"Department of the Interior U.S. GeologicSurvey (2017, November 04). Landsat 8 (L8) Data User Handbook, 2015, Available online: https:\/\/landsat.usgs.gov\/landsat-8-l8-data-users-handbook."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_39","unstructured":"(2017, July 03). SRTM DEM. Available online: http:\/\/www.cgiar-csi.org\/data\/srtm-90m-digital-elevation-database-v4-1."},{"key":"ref_40","unstructured":"(2017, July 03). Corine Land Cover Maps. Available online: http:\/\/land.copernicus.eu\/pan-european\/corine-land-cover."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Borgogno Mondino, E., Lessio, A., Tarricone, L., Novello, V., and de Palma, L. (2017). A comparison between multispectral aerial and satellite imagery in precision viticulture. Precis. Agric., 1\u201323.","DOI":"10.1007\/s11119-017-9510-0"},{"key":"ref_42","unstructured":"De Smith, M.J., Goodchild, M.F., and Longley, P.A. (2007). Goodchild, and Paul Longley. Geospatial Analysis\u2014A Comprehensive Guide to Principles, Techniques and Software Tools, Troubador Publishing Ltd."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0034-4257(92)90076-V","article-title":"Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output","volume":"41","author":"Moran","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"1025","article-title":"Image-based atmospheric corrections. Revisited and Improved Photogrammetric Engineering and Remote Sensing, [Falls Church, Va.]","volume":"62","author":"Chavez","year":"1996","journal-title":"Am. Soc. Photogramm."},{"key":"ref_45","unstructured":"Wahid, D.A., and Akiyama, T. (2007, January 7\u201311). Phenological change detection in flat and terrace paddy using aster satellite images in Takayama river basin area. Proceedings of the ASPRS 2007 Annual Conference, Tampa, FL, USA."},{"key":"ref_46","unstructured":"Fenn, R.W., Clough, S.A., Gallery, W.O., Good, R.E., Kneizys, F.X., Mill, J.D., Rothman, L.S., Shettle, E.P., and Volz, F.E. (1985). Handbook of Geophysics and Space Environment\u2014Cap.18: Optical and Infrared Properties of the Atmosphere, Air Force Cambridge Research Laboratories U.S."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/3\/4\/49\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:12Z","timestamp":1760208492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/3\/4\/49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,5]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["jimaging3040049"],"URL":"https:\/\/doi.org\/10.3390\/jimaging3040049","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,5]]}}}