2017
DOI: 10.1016/j.isprsjprs.2017.05.003
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Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery

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Cited by 480 publications
(359 citation statements)
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References 54 publications
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“…However, there are few studies on LAI estimation using UAV digital images directly in Xinjiang, China. In this paper, the LAI of winter wheat was estimated by only using UAV RGB-based image parameters, which was basically consistent with the existing studies on estimating the LAI of soybean breeding materials, winter wheat and rice by only using UAV RGB-based image parameters [13,15,36]. The VARI based on UAV RGB images is a new parameter constructed according to the VARI calculation principle.…”
Section: Discussionsupporting
confidence: 79%
“…However, there are few studies on LAI estimation using UAV digital images directly in Xinjiang, China. In this paper, the LAI of winter wheat was estimated by only using UAV RGB-based image parameters, which was basically consistent with the existing studies on estimating the LAI of soybean breeding materials, winter wheat and rice by only using UAV RGB-based image parameters [13,15,36]. The VARI based on UAV RGB images is a new parameter constructed according to the VARI calculation principle.…”
Section: Discussionsupporting
confidence: 79%
“…The UAV used in this study was the Mikrokopter OktoXL [37], an eight-rotor aircraft with a maximum payload capacity of 2.5 kg. This UAV has a flight duration of 8-25 min, depending on the battery and actual payload.…”
Section: Uav Campaigns and Sensorsmentioning
confidence: 99%
“…Six spectrally flat calibration canvases (1.2 × 1.2 m) with reflectance intensities at 3%, 6%, 12%, 22%, 48% and 64% were placed within the UAS flight overpass. With these calibration canvases, the digital number (DN) values of the images were transformed into reflectance values by applying an empirical line correction method [37,42]. The empirical line correction coefficients established between the convoluted ASD-FieldSpec4 and the six acquired mini-MCA spectral bands were then applied per pixel to the MS images.…”
Section: Image Preprocessingmentioning
confidence: 99%
“…UAV-based applications in agronomy comprise biomass estimation via plant height measurements [17,18], LAI estimation [19,20], analysis of phenology [21] and yield prediction [22][23][24], amongst others.…”
mentioning
confidence: 99%