Remote Sensing | |
The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI | |
Bastian Siegmann1  Thomas Jarmer2  Florian Beyer2  Manfred Ehlers2  Saskia Foerster2  Véronique Carrere2  Michael Rast2  Karl Staenz2  Clement Atzberger2  Magaly Koch2  | |
[1] Institute for Geoinformatics and Remote Sensing, University of Osnabrueck, Barbarastraße 22b, Osnabrueck 49076, Germany; | |
关键词: hyperspectral; aisaEAGLE; EnMAP; Sentinel-2; pan-sharpening; partial least squares regression; leaf area index; | |
DOI : 10.3390/rs71012737 | |
来源: mdpi | |
【 摘 要 】
In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (αspec) and the correlation coefficient (
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190005771ZK.pdf | 6354KB | download |