Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop. Issue 11 (2nd November 2018)

• Record Type: Journal Article
• Authors: Dhau, Inos; Adam, Elhadi; Mutanga, Onisimo; Ayisi, Kwabena; Abdel-Rahman, Elfatih Mohamed; Odindi, John; Masocha, Mhosisi
• Is Part Of: Geocarto international. Volume 33:Issue 11(2018)
• Page Start: 1223
• Page End: 1236
• Publication Date: 2018-11-02
• Subjects: Field spectroscopy -- grey leaf spot -- spectral re-sampling -- multispectral remote sensing; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2017.1343391
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: In this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral data re-sampled to WorldView-2, Quickbird, RapidEye and Sentinel-2 resolutions. To achieve this objective, Random Forest algorithm was used to classify the 2013 re-sampled spectra to represent the three identified disease severity categories. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively, while the WorldView-2, with eight spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710 nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the re-sampled WorldView 2 and Quickbird sensor resolutions, the respective 608 and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection.