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Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests

1
Geography Department, Humboldt Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany
2
Bavarian Forest National Park, Department of Visitor Management and National Park Monitoring Freyunger Str. 2, D-94481 Grafenau, Germany
3
Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Straße 4, D-79106 Freiburg, Germany
4
Bavarian Forest National Park, Department Nature Conservation and Research, Freyunger Str. 2, D-94481 Grafenau, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1160; https://doi.org/10.3390/rs11101160
Received: 28 February 2019 / Revised: 10 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Leaf Area Index (LAI) Retrieval using Remote Sensing)
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Abstract

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′s 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′s 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. View Full-Text
Keywords: leaf area index; Sentinel-2; Landsat-8; vegetation; broadleaf forest; hemispherical photography leaf area index; Sentinel-2; Landsat-8; vegetation; broadleaf forest; hemispherical photography
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Meyer, L.H.; Heurich, M.; Beudert, B.; Premier, J.; Pflugmacher, D. Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests. Remote Sens. 2019, 11, 1160.

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