Paper Info
Title
Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot.
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.
Year
DOI
Venue
2017
10.3390/rs9090906
REMOTE SENSING
Keywords
Field
DocType
cotton root rot,Sentinel-2A,ISODATA,spatial resolution,airborne multispectral imagery
Data source,Gossypium,Satellite imagery,Root rot,Remote sensing,Multispectral image,Multispectral pattern recognition,Geology,Contextual image classification,Image resolution
Journal
Volume
Issue
ISSN
9
9
2072-4292
Citations 
PageRank 
References 
0
0.34
5
Authors
7
Name
Order
Citations
PageRank
xiaoyu18212.85
Chenghai Yang25411.63
Mingquan Wu316918.49
J.-C. Zhao413552.42
Gui-Jun Yang514833.61
Wesley Clint Hoffmann671.60
Wenjiang Huang717951.84