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Remote Sens. 2017, 9(9), 906; https://doi.org/10.3390/rs9090906

Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot

1
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2
National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3
United States Department of Agriculture, Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845-4966, USA
4
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, Datun Road, Chaoyang, Beijing 100101, China
5
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 12 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential of 10-m Sentinel-2A satellite imagery for cotton root rot detection and compared it with airborne multispectral imagery using unsupervised classification at both field and regional levels. Accuracy assessment showed that the classification maps from the Sentinel-2A imagery had an overall accuracy of 94.1% for field subset images and 91.2% for the whole image, compared with the airborne image classification results. However, some small cotton root rot areas were undetectable and some non-infested areas within large root rot areas were incorrectly classified as infested due to the images’ coarse spatial resolution. Classification maps based on field subset Sentinel-2A images missed 16.6% of the infested areas and the classification map based on the whole Sentinel-2A image for the study area omitted 19.7% of the infested areas. These results demonstrate that freely-available Sentinel-2 imagery can be used as an alternative data source for identifying cotton root rot and creating prescription maps for site-specific management of the disease. View Full-Text
Keywords: cotton root rot; Sentinel-2A; ISODATA; spatial resolution; airborne multispectral imagery cotton root rot; Sentinel-2A; ISODATA; spatial resolution; airborne multispectral imagery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Song, X.; Yang, C.; Wu, M.; Zhao, C.; Yang, G.; Hoffmann, W.C.; Huang, W. Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot. Remote Sens. 2017, 9, 906.

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