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Article

Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

1
Institute of Geography, GIS & RS, University of Cologne, 50923 Cologne, Germany
2
ICASD-International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(11), 10395-10412; https://doi.org/10.3390/rs61110395
Received: 16 July 2014 / Revised: 20 October 2014 / Accepted: 21 October 2014 / Published: 28 October 2014
Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers. View Full-Text
Keywords: UAV; optical; remote sensing; RGB; 3D; biomass estimation; crop surface model; plant height; summer barley; precision agriculture UAV; optical; remote sensing; RGB; 3D; biomass estimation; crop surface model; plant height; summer barley; precision agriculture
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MDPI and ACS Style

Bendig, J.; Bolten, A.; Bennertz, S.; Broscheit, J.; Eichfuss, S.; Bareth, G. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sens. 2014, 6, 10395-10412. https://doi.org/10.3390/rs61110395

AMA Style

Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing. 2014; 6(11):10395-10412. https://doi.org/10.3390/rs61110395

Chicago/Turabian Style

Bendig, Juliane, Andreas Bolten, Simon Bennertz, Janis Broscheit, Silas Eichfuss, and Georg Bareth. 2014. "Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging" Remote Sensing 6, no. 11: 10395-10412. https://doi.org/10.3390/rs61110395

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