{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:15:50Z","timestamp":1771190150551,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"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>With the launch of the Sentinel-2 satellites, a European capacity has been created to ensure continuity of Landsat and SPOT observations. In contrast to previous sensors, Sentinel-2\u2032s multispectral imager (MSI) incorporates three additional spectral bands in the red-edge (RE) region, which are expected to improve the mapping of vegetation traits. The objective of this study was to compare Sentinel-2 MSI and Landsat-8 OLI data for the estimation of leaf area index (LAI) in temperate, deciduous broadleaf forests. We used hemispherical photography to estimate effective LAI at 36 field plots. We then built and compared simple and multiple linear regression models between field-based LAI and spectral bands and vegetation indices derived from Landsat-8 and Sentinel-2, respectively. Our main findings are that Sentinel-2 predicts LAI with comparable accuracy to Landsat-8. The best Landsat-8 models predicted LAI with a root-mean-square error (RMSE) of 0.877, and the best Sentinel-2 model achieved an RMSE of 0.879. In addition, Sentinel-2\u2032s RE bands and RE-based indices did not improve LAI prediction. Thirdly, LAI models showed a high sensitivity to understory vegetation when tree cover was sparse. According to our findings, Sentinel-2 is capable of delivering data continuity at high temporal resolution.<\/jats:p>","DOI":"10.3390\/rs11101160","type":"journal-article","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T11:37:40Z","timestamp":1557920260000},"page":"1160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests"],"prefix":"10.3390","volume":"11","author":[{"given":"Lorenz Hans","family":"Meyer","sequence":"first","affiliation":[{"name":"Geography Department, Humboldt Universit\u00e4t zu Berlin Unter den Linden 6, 10099 Berlin, Germany"}]},{"given":"Marco","family":"Heurich","sequence":"additional","affiliation":[{"name":"Bavarian Forest National Park, Department of Visitor Management and National Park Monitoring Freyunger Str. 2, D-94481 Grafenau, Germany"},{"name":"Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Stra\u00dfe 4, D-79106 Freiburg, Germany"}]},{"given":"Burkhard","family":"Beudert","sequence":"additional","affiliation":[{"name":"Bavarian Forest National Park, Department of Visitor Management and National Park Monitoring Freyunger Str. 2, D-94481 Grafenau, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-6038","authenticated-orcid":false,"given":"Joseph","family":"Premier","sequence":"additional","affiliation":[{"name":"Bavarian Forest National Park, Department of Visitor Management and National Park Monitoring Freyunger Str. 2, D-94481 Grafenau, Germany"}]},{"given":"Dirk","family":"Pflugmacher","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt Universit\u00e4t zu Berlin Unter den Linden 6, 10099 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","author":"Breda","year":"2003","journal-title":"J. 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