DOI: 10.1016/j.jag.2016.06.024
Scopus记录号: 2-s2.0-84997701227
论文题名: Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields
作者: Hamzeh S ; , Naseri A ; A ; , AlaviPanah S ; K ; , Bartholomeus H ; , Herold M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 412
结束页码: 421
语种: 英语
英文关键词: Categorical mapping
; Hyperion
; Landsat ETM+
; Quantitative mapping
; Salinity stress
Scopus关键词: accuracy assessment
; environmental stress
; Landsat thematic mapper
; mapping
; quantitative analysis
; salinity
; satellite imagery
; spectral analysis
; sugar cane
; Iran
英文摘要: This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80049
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, P.O. Box 14155-6465, Tehran, Iran; Department of Irrigation and drainage, Faculty of Water science, Shahid Chamran University of Ahvaz, Iran; Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, Wageningen, Netherlands
Recommended Citation:
Hamzeh S,, Naseri A,A,et al. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52