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

Please use this identifier to cite or link to this item: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201904101447
Title: High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction
Authors: Kanning, Martin
Kühling, Insa
Trautz, Dieter
Jarmer, Thomas
ORCID of the author: https://orcid.org/0000-0003-2873-2425
Abstract: 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.
Citations: Remote Sensing, 10(12), 2000, 2018, MDPI, S. 1-17
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201904101447
Subject Keywords: hyperspectral; pushbroom; UAV; regression; LAI; chlorophyll; nitrogen; grain yield
Issue Date: 10-Dec-2018
License name: Attribution 4.0 International
License url: http://creativecommons.org/licenses/by/4.0/
Appears in Collections:FB06 - Hochschulschriften

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