DOI: 10.1016/j.jag.2013.06.002
Scopus记录号: 2-s2.0-84897870159
论文题名: Estimating soil salinity in Pingluo County of China using QuickBirddata and soil reflectance spectra
作者: Sidike A ; , Zhao S ; , Wen Y
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 26, 期: 1 起始页码: 156
结束页码: 175
语种: 英语
英文关键词: Measured reflectance spectra
; Pingluo county
; QuickBird data
; Soil salinity
Scopus关键词: accuracy assessment
; arid region
; estimation method
; least squares method
; model test
; model validation
; NDVI
; prediction
; QuickBird
; salinity
; satellite data
; semiarid region
; sensitivity analysis
; soil analysis
; soil cover
; spectral reflectance
; China
; Ningxia Huizu
; Pingluo
英文摘要: Soil salinization is a worldwide environmental problem with severe economic and social consequences. Inthis paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR)predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationshipbetween the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectralcoverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBirddata in estimating soil salinity by analyzing the correlations between the measured reflectance spectraand reflectance spectra derived from QuickBird data and analyzing the contributions of each band of Quick Bird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data.The results indicated that the sensitive bands covered several bands of each optical sensor and thesesensors can be used for soil salinity estimation. The result of estimation model showed that an accurateprediction of soil salinity can be made based on the PLSR method (R2= 0.992, RMSE = 0.195). The PLSRmodel's performance was better than that of the stepwise multiple regression (SMR) method. The resultsalso indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinityindices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) andthe ratio vegetation index (RVI) as independent model variables can help to increase the accuracy ofsoil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soilmoisture on prediction accuracy. The method developed in this paper can be applied in other arid andsemi-arid areas, such as western China. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79621
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China; School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093, China; Graduate University of the Chinese Academy of Sciences, Beijing, 100049, China; Water and Environmental Research Institute, University of Guam, Mangilao, GU 96923, United States
Recommended Citation:
Sidike A,, Zhao S,, Wen Y. Estimating soil salinity in Pingluo County of China using QuickBirddata and soil reflectance spectra[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,26(1)