| Research Articles |
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| Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery |
| Fei WANG1,2, Xi CHEN1, GePing LUO1, JianLi DING3*, XianFeng CHEN1,4 |
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China;
4 Slippery Rock University of Pennsylvania, Slippery Rock, PA16057, USA |
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Abstract 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 opera-tional 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 (R2>0.86, RMSE<6.86) compared to COSRI (R2=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.
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Received: 08 February 2013
Published: 10 September 2013
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| Fund: This research was financially supported by the National Basic Research Program of China (2009CB825105) and the National Natural Science Foundation of China (41261090). |
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Corresponding Authors:
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