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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #343361

Research Project: Aerial Application Technology for Sustainable Crop Production

Location: Aerial Application Technology Research

Title: Evaluation of Sentinel-2A satellite imagery for mapping cotton root rot

Author
item SONG, XIAOYU - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item Yang, Chenghai
item WU, MINQUAN - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item YANG, GUIJUN - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item ZHAO, CHUNJIANG - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item Hoffmann, Wesley
item HUANG, WENJIANG - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/28/2017
Publication Date: 8/31/2017
Citation: Song, X., Yang, C., Wu, M., Yang, G., Zhao, C., Hoffmann, W.C., Huang, W. 2017. Evaluation of Sentinel-2A satellite imagery for mapping cotton root rot. Remote Sensing. 9(9):1-17.

Interpretive Summary: Remote sensing imagery has been successfully used for mapping cotton root rot within cotton fields for site-specific management of the disease, but most airborne and high-resolution satellite imagery suitable for this application needs to be purchased. Recently, 10-m resolution imagery from two Sentinel-2 satellite sensors launched by the European Space Agency has become freely available. This research evaluated the usefulness of Sentinel-2A satellite imagery and compared it with airborne imagery for mapping cotton root rot at both field and regional levels. Accuracy assessment showed that Sentinel-2A imagery could in general accurately identify infested areas within fields, but some small infested areas were undetectable due to the coarse spatial resolution of the imagery. The results from this study demonstrate that Sentinel-2 imagery can be an alternative image source for site-specific management of cotton root rot if finer resolution imagery is not available.

Technical 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 the Iterative Self-Organizing Data Analysis (ISODATA) unsupervised classification method at both field and regional levels. Accuracy assessment showed that the classification maps from the Sentinel-2A imagery had high overall accuracy, but some small cotton root rot areas were undetectable and some small non-infested areas within large root rot areas were incorrectly classified as infested due to its coarse spatial resolution. Further analysis showed that field subset Sentinel-2A images could more accurately identify cotton root rot-infested areas than the regional Sentinel-2A imagery. The results from this study demonstrate that Sentinel-2 imagery can be used for cotton root rot identification when the imagery is taken during the optimum root rot discrimination period for a given region.