{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:13:25Z","timestamp":1783455205048,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T00:00:00Z","timestamp":1600300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Northwest Agriculture and Forest University","award":["No. Z1090219094"],"award-info":[{"award-number":["No. Z1090219094"]}]},{"name":"the Science and Technology Major Project","award":["No. 2018A03004"],"award-info":[{"award-number":["No. 2018A03004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41972308"],"award-info":[{"award-number":["No. 41972308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With several bands covering iron-bearing mineral spectral features, Sentinel-2 has advantages for iron mapping. However, due to the inconsistent spatial resolution, the sensitivity of Sentinel-2 data to detect iron-bearing minerals may be decreased by excluding the 60 m bands and neglecting the 20 m vegetation red-edge bands. Hence, the capability of Sentinel-2 for iron-bearing minerals mapping were assessed by applying a multivariate (MV) method to pansharpen Sentinel-2 data. Firstly, the Sentinel-2 bands with spatial resolution 20 m and 60 m (except band 10) were pansharpened to 10 m. Then, extraction of iron-bearing minerals from the MV-fused image was explored in the Cuprite area, Nevada, USA. With the complete set of 12 bands with a fine spatial resolution, three band ratios (6\/1, 6\/8A and (6 + 7)\/8A) of the fused image were proposed for the extraction of hematite + goethite, hematite + jarosite and the mixture of iron-bearing minerals, respectively. Additionally, band ratios of Sentinel-2 data for iron-bearing minerals in previous studies were modified with substitution of narrow near infrared band 8A for band 8. Results demonstrated that the capability for detection of iron-bearing minerals using Sentinel-2 data was improved by consideration of two extra bands and the unified fine spatial resolution.<\/jats:p>","DOI":"10.3390\/rs12183028","type":"journal-article","created":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T08:29:43Z","timestamp":1600331383000},"page":"3028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Assessment of the Capability of Sentinel-2 Imagery for Iron-Bearing Minerals Mapping: A Case Study in the Cuprite Area, Nevada"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3462-9045","authenticated-orcid":false,"given":"Wenyan","family":"Ge","sequence":"first","affiliation":[{"name":"Institute of Soil and Water Conservation, Northwest A&amp;F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuming","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5213-4399","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest Agriculture &amp; Forestry University, 26 Xinong Road, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Molei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"ref_1","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"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":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2018.03.014","article-title":"Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island","volume":"215","author":"Vrieling","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"170","article-title":"Sentinel-2A red-edge spectral indices suitability for discriminating burn severity","volume":"50","author":"Quintano","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"873","DOI":"10.3390\/rs8100873","article-title":"Separability analysis of Sentinel-2A Multi-Spectral Instrument (MSI) data for burned area discrimination","volume":"8","author":"Huang","year":"2016","journal-title":"Remote Sens."},{"key":"ref_8","first-page":"221","article-title":"Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity","volume":"64","author":"Quintano","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ge, W., Cheng, Q., Tang, Y., Jing, L., and Gao, C. (2018). Lithological classification using sentinel-2A data in the Shibanjing ophiolite complex in inner Mongolia, China. Remote Sens., 10.","DOI":"10.3390\/rs10040638"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.rse.2014.03.022","article-title":"Potential of ESA\u2019s Sentinel-2 for geological applications","volume":"148","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12635","DOI":"10.3390\/rs71012635","article-title":"Sentinel-2 for mapping iron absorption feature parameters","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","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_15","first-page":"97","article-title":"Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery","volume":"58","author":"Navarro","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.2138\/am.2005.1700","article-title":"The visible and infrared spectral properties of jarosite and alunite","volume":"90","author":"Bishop","year":"2005","journal-title":"Am. Mineral."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.2113\/gsecongeo.74.7.1613","article-title":"Spectra of altered rocks in the visible and near infrared","volume":"74","author":"Hunt","year":"1979","journal-title":"Econ. Geol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6790","DOI":"10.3390\/rs6086790","article-title":"Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: Hyperion\/OLI and EnMAP\/Sentinel-2","volume":"6","author":"Mielke","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.oregeorev.2018.07.017","article-title":"Hydrothermally altered mineral mapping using synthetic application of Sentinel-2A MSI, ASTER and Hyperion data in the Duolong area, Tibetan Plateau, China","volume":"101","author":"Hu","year":"2018","journal-title":"Ore Geol. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, H., Jing, L., and Tang, Y. (2017). Assessment of pansharpening methods applied to WorldView-2 imagery fusion. Sensors, 17.","DOI":"10.3390\/s17010089"},{"key":"ref_21","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2016.10.030","article-title":"Fusion of Sentinel-2 images","volume":"187","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"723","DOI":"10.5194\/isprs-archives-XLI-B7-723-2016","article-title":"Pansharpening on the narrow VNIR and SWIR spectral bands of Sentinel-2","volume":"41","author":"Vaiopoulos","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Park, H., Choi, J., Park, N., and Choi, S. (2017). Sharpening the VNIR and SWIR bands of Sentinel-2A imagery through modified selected and synthesized band schemes. Remote Sens., 9.","DOI":"10.3390\/rs9101080"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1080\/01431161.2017.1392640","article-title":"The effect of fusing Sentinel-2 bands on land-cover classification","volume":"39","author":"Jogun","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zheng, H., Du, P., Chen, J., Xia, J., Li, E., Xu, Z., Li, X., and Yokoya, N. (2017). Performance evaluation of downscaling Sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sens., 9.","DOI":"10.3390\/rs9121274"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6459","DOI":"10.1080\/01431160903439841","article-title":"A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data","volume":"31","author":"Jing","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","unstructured":"ESA (2020, September 11). Sentinel-2 User Handbook, Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_30","unstructured":"Clerc, S., and MPC Team (2020, September 11). Sentinel-2 Data Quality Report; Report Issue 55; ESA-CS, France. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_L1C_Data_Quality_Report."},{"key":"ref_31","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aiazzi, B., Baronti, S., Selva, M., and Alparone, L. (August, January 31). Enhanced Gram-Schmidt spectral sharpening based on multivariate regression of MS and Pan data. Proceedings of the 2006 IEEE International Symposium on Geoscience and Remote Sensing Symposium, Denver, CO, USA.","DOI":"10.1109\/IGARSS.2006.975"},{"key":"ref_33","unstructured":"Klonus, S., and Ehlers, M. (2009, January 6\u20139). Performance of evaluation methods in image fusion. Proceedings of the 12th International Conference on Information Fusion, Seattle, WA, USA."},{"key":"ref_34","unstructured":"Li, C., Liu, L., Wang, J., Zhao, C., and Wang, R. (2004, January 20\u201324). Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/LGRS.2004.836784","article-title":"A global quality measurement of pan-sharpened multispectral imagery","volume":"1","author":"Alparone","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","first-page":"295","article-title":"Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic","volume":"57","author":"Chavez","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.14358\/PERS.69.5.513","article-title":"Multi-band wavelet for fusing SPOT panchromatic and multispectral images","volume":"69","author":"Shi","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992). Discrimination Among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm. Summaries of the 4th JPL Airborne Earth Science Workshop, NASA. JPL Publication, Summaries of the Third Annual JPL Airborne Geoscience Workshop."},{"key":"ref_39","unstructured":"Wald, L. (2002). Data Fusion: Definitions and Architectures\u2013Fusion of Images of Different Spatial Resolutions, Presses des Mines."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and multispectral image fusion based on a sparse representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1976","DOI":"10.1109\/TGRS.2010.2103944","article-title":"Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities","volume":"49","author":"Mahyari","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.2113\/econgeo.109.5.1179","article-title":"Mapping advanced argillic alteration at Cuprite, Nevada, using imaging spectroscopy","volume":"109","author":"Swayze","year":"2014","journal-title":"Econ. Geol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1144\/1467-7873\/03-001","article-title":"Spectral reflectance properties (0.4\u20132.5 \u03bcm) of secondary Fe-oxide, Fe-hydroxide, and Fe-sulphate-hydrate minerals associated with sulphide-bearing mine wastes","volume":"3","author":"Crowley","year":"2003","journal-title":"Geochem. Explor. Environ. Anal."},{"key":"ref_45","first-page":"35","article-title":"Discrimination of rock types and detection of hydrothermally altered areas in south-central Nevada by the use of computer-enhanced ERTS images","volume":"883","author":"Rowan","year":"1976","journal-title":"Geol. Surv. Prof. Pap."},{"key":"ref_46","first-page":"83","article-title":"Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer","volume":"56","author":"Kruse","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","first-page":"149","article-title":"Mineral mapping with AVIRIS and EO-1 Hyperion","volume":"Volume 41","author":"Kruse","year":"2003","journal-title":"Proceedings of the 12th JPL Airborne Geoscience Workshop"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1016\/j.rse.2010.04.008","article-title":"Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals","volume":"114","author":"Mars","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.3390\/rs5062688","article-title":"Mineral mapping using simulated Worldview-3 short-wave-infrared imagery","volume":"5","author":"Kruse","year":"2013","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ashley, R.P. (1980). Alteration mapping Using Multispectral Images-Cuprite Mining Districts, Esmeralda County, Nevada. US Geol. Surv. Open File Rep., 80\u2013367.","DOI":"10.3133\/ofr80367"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Van der Werff, H., and van der Meer, F. (2016). Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing. Remote Sens., 8.","DOI":"10.3390\/rs8110883"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.rse.2018.04.031","article-title":"Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences","volume":"215","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","first-page":"653","article-title":"Mapping mineral chemistry of a lateritic outcrop in new Caledonia through generalized regression using Sentinel-2 and field reflectance spectra","volume":"73","author":"Ibrahim","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0169-1368(99)00007-4","article-title":"Remote sensing for mineral exploration","volume":"14","author":"Sabins","year":"1999","journal-title":"Ore Geol. Rev."},{"key":"ref_55","first-page":"36","article-title":"ASTER mineral index processing manual","volume":"37","author":"Kalinowski","year":"2004","journal-title":"Remote Sens. Appl. Geosci. Aust."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3028\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:10:51Z","timestamp":1760177451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,17]]},"references-count":55,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12183028"],"URL":"https:\/\/doi.org\/10.3390\/rs12183028","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,17]]}}}