High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

DC FieldValueLanguage
dc.contributor.authorKanning, Martin
dc.contributor.authorKuehling, Insa
dc.contributor.authorTrautz, Dieter
dc.contributor.authorJarmer, Thomas
dc.date.accessioned2021-12-23T16:14:01Z-
dc.date.available2021-12-23T16:14:01Z-
dc.date.issued2018
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/10861-
dc.description.abstractThe efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400-1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R-LAI(2) = 0.79, RMSE(LAI [m)(2)m(-2)] = 0.18, R-CHL(2) = 0.77, RMSECHL [mu g cm-2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R-yield(2) = 0.88, RMSEyield [dt ha-1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.
dc.description.sponsorshipGerman Aerospace Center (DLR) of the Federal Ministry of Economics and Technology [50EE1014]; This work was funded by the German Aerospace Center (DLR), using the financial resources of the Federal Ministry of Economics and Technology on the basis of a decision by the German Parliament, under grant number 50EE1014.
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofREMOTE SENSING
dc.subjectAIRBORNE
dc.subjectCANOPY
dc.subjectchlorophyll
dc.subjectEnvironmental Sciences
dc.subjectEnvironmental Sciences & Ecology
dc.subjectGeology
dc.subjectGeosciences, Multidisciplinary
dc.subjectgrain yield
dc.subjectGRAIN-YIELD
dc.subjecthyperspectral
dc.subjectImaging Science & Photographic Technology
dc.subjectLAI
dc.subjectLEAF-AREA INDEX
dc.subjectMANAGEMENT
dc.subjectNITROGEN
dc.subjectpushbroom
dc.subjectQUALITY
dc.subjectREGRESSION
dc.subjectRemote Sensing
dc.subjectSPECTROSCOPY
dc.subjectUAV
dc.subjectWINTER-WHEAT
dc.titleHigh-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction
dc.typejournal article
dc.identifier.doi10.3390/rs10122000
dc.identifier.isiISI:000455637600142
dc.description.volume10
dc.description.issue12
dc.contributor.orcid0000-0003-2873-2425
dc.contributor.researcheridN-3571-2018
dc.identifier.eissn20724292
dc.publisher.placeST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
dcterms.isPartOf.abbreviationRemote Sens.
dcterms.oaStatusgold, Green Published
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.orcid0000-0002-4652-1640-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidJaTh054-
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