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

Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201904101447
Open Access logo originally created by the Public Library of Science (PLoS)
Titel: High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction
Autor(en): Kanning, Martin
Kühling, Insa
Trautz, Dieter
Jarmer, Thomas
ORCID des Autors: https://orcid.org/0000-0003-2873-2425
Zusammenfassung: The 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 (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 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.
Bibliografische Angaben: Remote Sensing, 10(12), 2000, 2018, MDPI, S. 1-17
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201904101447
Schlagworte: hyperspectral; pushbroom; UAV; regression; LAI; chlorophyll; nitrogen; grain yield
Erscheinungsdatum: 10-Dez-2018
Lizenzbezeichnung: Attribution 4.0 International
URL der Lizenz: http://creativecommons.org/licenses/by/4.0/
Publikationstyp: Einzelbeitrag in einer wissenschaftlichen Zeitschrift [article]
Enthalten in den Sammlungen:FB06 - Hochschulschriften

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
Remotesensing_10_12_2000_2018_Kanning.pdfResearch article7,25 MBAdobe PDF
Remotesensing_10_12_2000_2018_Kanning.pdf
Miniaturbild
Öffnen/Anzeigen


Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons