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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2015-09, Vol.7 (9), p.11449-11480
Main Authors: Tilly, Nora, Aasen, Helge, Bareth, George
Format: Article
Language:English
Subjects:
Barley
Biomass
Crop management
Crops
Estimators
Growing season
Hordeum vulgare
hyperspectral vegetation indices
Management
Mathematical models
multi-temporal
Plant biomass
plant height
Plants (organisms)
plot level
precision agriculture
Remote sensing
spectrometer
terrestrial laser scanning
Vegetation
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs70911449