Arid
DOI10.1007/s40333-013-0183-x
Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery
Wang, Fei1,2; Chen, Xi1; Luo, GePing1; Ding, JianLi3; Chen, XianFeng1,4
通讯作者Ding, JianLi
来源期刊JOURNAL OF ARID LAND
ISSN1674-6767
出版年2013
卷号5期号:3页码:340-353
英文摘要

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context (vegetation cover, moisture, surface roughness, and organic matter) and the weak spectral features of salinized soil. Therefore, an index such as the salinity index (SI) that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index (NDVI), as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index (AFII) was created as supported by the spectral mixture analysis (SMA), which is more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model (SDM) that combines AFII and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFII and SI using a scatterplot. The SDMs were then compared to the combined spectral response index (COSRI) from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs (R (2)> 0.86, RMSE < 6.86) compared to COSRI (R (2)=0.71, RMSE=16.21). These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.


英文关键词soil salinity spectrum halophytes Landsat TM spectral mixture analysis feature space model
类型Article
语种英语
国家Peoples R China ; USA
收录类别SCI-E
WOS记录号WOS:000321558300008
WOS关键词SPECTRAL MIXTURE ANALYSIS ; REMOTE-SENSING DATA ; VEGETATION COVER ; REFLECTANCE ; NDVI ; INDICATORS ; MEXICO ; OASIS
WOS类目Environmental Sciences
WOS研究方向Environmental Sciences & Ecology
来源机构中国科学院新疆生态与地理研究所 ; 新疆大学
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/178129
作者单位1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China;
4.Slippery Rock Univ Penn, Slippery Rock, PA 16057 USA
推荐引用方式
GB/T 7714
Wang, Fei,Chen, Xi,Luo, GePing,et al. Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery[J]. 中国科学院新疆生态与地理研究所, 新疆大学,2013,5(3):340-353.
APA Wang, Fei,Chen, Xi,Luo, GePing,Ding, JianLi,&Chen, XianFeng.(2013).Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery.JOURNAL OF ARID LAND,5(3),340-353.
MLA Wang, Fei,et al."Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery".JOURNAL OF ARID LAND 5.3(2013):340-353.
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