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Correction published on 19 December 2015, see Remote Sens. 2015, 7(12), 17291-17296.

Open AccessArticle
Remote Sens. 2015, 7(9), 11449-11480; doi:10.3390/rs70911449

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Institute of Geography, GIS & RS, University of Cologne, D-50923 Cologne, Germany
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Author to whom correspondence should be addressed.
Academic Editors: Mutlu Ozdogan, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 22 June 2015 / Revised: 23 August 2015 / Accepted: 2 September 2015 / Published: 9 September 2015
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Abstract

Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R2: 0.07–0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations. View Full-Text
Keywords: terrestrial laser scanning; spectrometer; plant height; hyperspectral vegetation indices; biomass; precision agriculture; plot level; multi-temporal terrestrial laser scanning; spectrometer; plant height; hyperspectral vegetation indices; biomass; precision agriculture; plot level; multi-temporal
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tilly, N.; Aasen, H.; Bareth, G. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449-11480.

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