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Remote Sens. 2018, 10(12), 2000; https://doi.org/10.3390/rs10122000

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

1
Institute of Computer Science, Working Group, Remote Sensing and Digital Image Analysis, University of Osnabrück, Wachsbleiche 27, D-49090 Osnabrück, Germany
2
Institute of Agricultural and Nutritional Sciences, Department of Agronomy and Organic Farming, Martin Luther University Halle-Wittenberg, D-06120 Halle (Saale), Germany
3
Faculty of Agricultural Sciences and Landscape Architecture, Working Group Sustainable Agro-Ecosystems, Osnabrück University of Applied Sciences, D-49090 Osnabrück, Germany
*
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 7 December 2018 / Accepted: 8 December 2018 / Published: 10 December 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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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. View Full-Text
Keywords: hyperspectral; pushbroom; UAV; regression; LAI; chlorophyll; nitrogen; grain yield hyperspectral; pushbroom; UAV; regression; LAI; chlorophyll; nitrogen; grain yield
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Kanning, M.; Kühling, I.; Trautz, D.; Jarmer, T. High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction. Remote Sens. 2018, 10, 2000.

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