TR32-Database: Database of Transregio 32

[866] - Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Yu, K., Leufen, G., Hunsche, M., Noga, G., Chen, X., Bareth, G., 2013. Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices. Remote Sensing, 6 (1), 64 - 86. DOI: 10.3390/rs6010064.
Identification
Title(s):Main Title: Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices
Description(s):Abstract: Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R2 of 0.72 and 0.79. Partial least squares (PLS) and support vector machines (SVM) regression models further improved the use of fluorescence signals for the estimation of LCC, yielding R2 of 0.81 and 0.84, respectively. Our results demonstrate that non-destructive spectral measurements are able to detect mild disease symptoms before significant losses in LCC due to diseases under natural conditions.
Identifier(s):DOI: 10.3390/rs6010064
Citation Advice:Yu K, Leufen G, Hunsche M, Noga G, Chen X, Bareth G. Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices. Remote Sensing. 2014; 6(1):64-86.
Responsible Party
Creator(s):Author: Kang Yu
Author: Georg Leufen
Author: Mauricio Hunsche
Author: Georg Noga
Author: Xinping Chen
Author: Georg Bareth
Publisher:MDPI AG, Basel, Switzerland
Topic
TR32 Topic:Remote Sensing
Subject(s):CRC/TR32 Keywords: Remote Sensing, Chlorophyll, Fluorescence, Vegetation Index, Winter Barley, Plant Growth
File Details
File Name:Yu_et_al_RS_2014.pdf
Data Type:Text
Size(s):18 Pages
File Size:719 kB (0.702 MB)
Date(s):Issued: 2013-12-19
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
Download Information:Remote Sensing Open Access Option: http://www.mdpi.com/2072-4292/6/1/64
General Access and Use Conditions:no conditions apply
Access Limitations:no limitations
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:NorthRhine-Westphalia
Measurement Location:--NorthRhine-Westphalia--
Specific Informations - Publication
Status:Published
Review:NoPeerReview
Year:2013
Type:Article
Article Type:Journal
Source:Remote Sensing
Issue:1
Volume:6
Number Of Pages:18
Page Range:64 - 86
Metadata Details
Metadata Creator:Georg Bareth
Metadata Created:2014-05-14
Metadata Last Updated:2014-05-14
Subproject:Z1
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:111
Metadata Downloads:0
Dataset Downloads:11
Dataset Activity
Features