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

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.

[1]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery: Part I: Methods , 2001 .

[2]  Y. Kurucu,et al.  Crop Type Classification Using Vegetation Indices of RapidEye Imagery , 2014 .

[3]  Clement Atzberger,et al.  Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples , 2016, Remote. Sens..

[4]  J. A. Thomasson,et al.  Site-Specific Relationships between Cotton Root Rot and Soil Properties , 2016, Journal of Cotton Science.

[5]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[6]  Mingquan Wu,et al.  Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping , 2017, Remote. Sens..

[7]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[8]  Luis Alonso,et al.  Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .

[9]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[11]  Russell G. Congalton,et al.  Evaluating remotely sensed techniques for mapping riparian vegetation , 2002 .

[12]  Chris Sansone.  FLUTRIAFOL CONTROL OF COTTON ROOT ROT CAUSED BY PHYMATOTRICHOPSIS OMNIVORA , 2009 .

[13]  R. G. Percy,et al.  Potential range of Phymatotrichum omnivorum as determined by edaphic factors , 1983 .

[14]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Chenghai Yang,et al.  Comparison of Airborne Multispectral and Hyperspectral Imagery for Mapping Cotton Root Rot , 2009 .

[16]  Chenghai Yang,et al.  Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery , 2016, Comput. Electron. Agric..

[17]  O. Mutanga,et al.  Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments , 2015 .

[18]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[19]  Julien Radoux,et al.  Sentinel-2's Potential for Sub-Pixel Landscape Feature Detection , 2016, Remote. Sens..

[20]  Michael E. Schaepman,et al.  Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land , 2012 .

[21]  J. Alex Thomasson,et al.  ENGINERING AND GINNING Monitoring Cotton Root Rot Progression within a Growing Season Using Airborne Multispectral Imagery , 2014 .

[22]  Alex M. Lechner,et al.  Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping , 2009 .

[23]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[24]  B. Kleinschmit,et al.  Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data , 2012 .

[25]  Chenghai Yang,et al.  Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot , 2015 .

[26]  J. Lacaux,et al.  Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal , 2007 .

[27]  L. H. Pammel.  Root Rot of Cotton or "Cotton Blight" , 2022 .

[28]  Clement Atzberger,et al.  First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..

[29]  Alan A. Ager,et al.  Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland , 2011 .

[30]  Chenghai Yang,et al.  Evaluating unsupervised and supervised image classification methods for mapping cotton root rot , 2014, Precision Agriculture.

[31]  Clayton C. Kingdon,et al.  Spatial pattern analysis for monitoring protected areas , 2009 .

[32]  J. R. Jensen,et al.  Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes , 2011 .

[33]  Chenghai Yang,et al.  Mapping phymatotrichum root rot of cotton using airborne three-band digital imagery , 2005 .

[34]  George Joseph,et al.  How well do we understand Earth observation electro-optical sensor parameters? , 2000 .

[35]  A. Lausch.  Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability , 2002 .