{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:55:30Z","timestamp":1780088130330,"version":"3.54.0"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"German Academic Exchange Service","doi-asserted-by":"publisher","award":["91704516"],"award-info":[{"award-number":["91704516"]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multispectral sensors are important instruments for Earth observation. In remote sensing applications, the near-infrared (NIR) band, together with the visible spectrum (RGB), provide abundant information about ground objects. However, the NIR band is typically not available on low-cost camera systems, which presents challenges for the vegetation extraction. To this end, this paper presents a conditional generative adversarial network (cGAN) method to simulate the NIR band from RGB bands of Sentinel-2 multispectral data. We adapt a robust loss function and a structural similarity index loss (SSIM) in addition to the GAN loss to improve the model performance. With 45,529 multi-seasonal test images across the globe, the simulated NIR band had a mean absolute error of 0.02378 and an SSIM of 89.98%. A rule-based landcover classification using the simulated normalized difference vegetation index (NDVI) achieved a Jaccard score of 89.50%. The evaluation metrics demonstrated the versatility of the learning-based paradigm in remote sensing applications. Our simulation approach is flexible and can be easily adapted to other spectral bands.<\/jats:p>","DOI":"10.3390\/s23094179","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7648-5938","authenticated-orcid":false,"given":"Xiangtian","family":"Yuan","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), M\u00fcnchner Str. 20, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8407-5098","authenticated-orcid":false,"given":"Jiaojiao","family":"Tian","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), M\u00fcnchner Str. 20, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), M\u00fcnchner Str. 20, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance dataset","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. 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