Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

被引:126
作者
Sidike, Ayetiguli [1 ,2 ,3 ]
Zhao, Shuhe [2 ]
Wen, Yuming [4 ]
机构
[1] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Xinjiang, Peoples R China
[2] Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210093, Jiangsu, Peoples R China
[3] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
[4] Univ Guam, Water & Environm Res Inst, Mangilao, GU 96923 USA
来源
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION | 2014年 / 26卷
关键词
Soil salinity; QuickBird data; Measured reflectance spectra; Pingluo County; PARTIAL LEAST-SQUARES; SALT-AFFECTED SOILS; SUPPORT VECTOR MACHINE; YELLOW-RIVER DELTA; ELECTRICAL-CONDUCTIVITY; QUANTITATIVE-ANALYSIS; MULTISPECTRAL DATA; SPECTROSCOPY; REGRESSION; VEGETATION;
D O I
10.1016/j.jag.2013.06.002
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this 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 relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird 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 these sensors can be-used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R-2 = 0.992, RMSE= 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SD, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:156 / 175
页数:20
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