The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI
DC Element | Wert | Sprache |
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dc.contributor.author | Siegmann, Bastian | |
dc.contributor.author | Jarmer, Thomas | |
dc.contributor.author | Beyer, Florian | |
dc.contributor.author | Ehlers, Manfred | |
dc.date.accessioned | 2021-12-23T15:59:57Z | - |
dc.date.available | 2021-12-23T15:59:57Z | - |
dc.date.issued | 2015 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/4254 | - |
dc.description.abstract | 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 ((spec)) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R-cv(2) = 0.87) followed by the models built with the fused datasets (EnMAP-aisaEAGLE and EnMAP-Sentinel-2 fusion each with a R-cv(2) of 0.75) and the simulated EnMAP data (R-cv(2) = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications. | |
dc.description.sponsorship | German Aerospace Center (DLR)Helmholtz AssociationGerman Aerospace Centre (DLR); Federal Ministry of Economics and TechnologyFederal Ministry for Economic Affairs and Energy (BMWi) [50 EE 1014]; This work was funded by the German Aerospace Center (DLR) with financial resources of the Federal Ministry of Economics and Technology on the basis of a decision of the German Parliament, grant number 50 EE 1014. We would like to thank the Helmholtz Centre for Environmental Research Leipzig (UFZ) and the Humboldt University at Berlin for making their field instruments available. Special thanks go to Mr. Wagner and the Wimex GmbH, owner of the investigated fields, for their cooperation and their support. Additionally, we want to thank Karl Segl, Rudolf Richter, Daniel Doktor, Sascha Klonus, Sabine Hornberg, Holger Lilienthal, Nicole Richter, Thomas Selige, Anne Bodemann, Martin Kanning, Thorben Jensen and Yevgeniya Filippovska for their assistance in field during data collection and data pre-processing. | |
dc.language.iso | en | |
dc.publisher | MDPI | |
dc.relation.ispartof | REMOTE SENSING | |
dc.subject | aisaEAGLE | |
dc.subject | CROP MODELS | |
dc.subject | EnMAP | |
dc.subject | Environmental Sciences | |
dc.subject | Environmental Sciences & Ecology | |
dc.subject | FIELD | |
dc.subject | Geology | |
dc.subject | Geosciences, Multidisciplinary | |
dc.subject | hyperspectral | |
dc.subject | IMAGE FUSION | |
dc.subject | Imaging Science & Photographic Technology | |
dc.subject | leaf area index | |
dc.subject | pan-sharpening | |
dc.subject | partial least squares regression | |
dc.subject | PRECISION AGRICULTURE | |
dc.subject | Remote Sensing | |
dc.subject | Sentinel-2 | |
dc.subject | SPECTROSCOPY | |
dc.subject | TM | |
dc.title | The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI | |
dc.type | journal article | |
dc.identifier.doi | 10.3390/rs71012737 | |
dc.identifier.isi | ISI:000364328600007 | |
dc.description.volume | 7 | |
dc.description.issue | 10 | |
dc.description.startpage | 12737 | |
dc.description.endpage | 12762 | |
dc.contributor.orcid | 0000-0002-9203-320X | |
dc.contributor.orcid | 0000-0002-9203-320X | |
dc.contributor.researcherid | F-6117-2019 | |
dc.contributor.researcherid | AAV-6509-2020 | |
dc.identifier.eissn | 20724292 | |
dc.publisher.place | ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND | |
dcterms.isPartOf.abbreviation | Remote Sens. | |
dcterms.oaStatus | gold, Green Submitted | |
crisitem.author.dept | FB 06 - Mathematik/Informatik | - |
crisitem.author.deptid | fb06 | - |
crisitem.author.orcid | 0000-0002-4652-1640 | - |
crisitem.author.parentorg | Universität Osnabrück | - |
crisitem.author.netid | JaTh054 | - |
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geprüft am 23.05.2024