Remote Sensing-Based Synthesis Index Model For Monitoring Of Soil Salinization In Arid Area

Soil salinization is an important worldwide environmental problem, especially in arid and semi-arid regions.Quantitative remote sensing provides accurate and up-to-date information of spatio-temporal dynamics of salinity. Studies at both home and overseas showed that it is hard to acquire reliable information of salinity by using a single wavelength in visible, near infrared (NIR), thermal or microwave domain, specifically in a sophisticated surface conditions such as agricultural fields, while the reported methods inherited many limitations in practical applications including time-lag effect, being too complex to calculate, being excessively dependent on meteorological observations and field measurements etc. Therefore, developing of simple, effective and operational methods for the satellite estimation of surface salnity, especially vegetation cover is of great interest for both researchers in remote sensing community and policy makers for the sustainable development of eco-environments.Bare salt-affected Soil reflect spectral and halophytic plants spectral are the most direct and important indicator of salinity events and, therefore, using an integrated algorithm of the spectral response of bare soil and vegetation is critical to the soil salinization estimation. In this paper, an improved soil salt-affected monitoring method, the combined fraction spectral response index (SDI), is developed introducing soil and vegetation fraction, which takes into account both salty-affected soil spectral and halophytic plant growth. To validate the salinity indices proposed by this paper, Enhanced Thematic Mapper Plus and ALOS imagese from different times with various salinization conditions are used to calculate the SDI and to correlate ground measuring salt content (SAL))with SDI. SAL was determined in surface soil samples(0-10cm). Multiple endmember spectral mixture analysis (MESMA) uses linear mixture models to provide bare soil and halophytic vegetation fraction abundances respectively. SDI based on SMA increase in the level of salt-affected soil spectral response by eliminating non-halophytic vegetation affected and combining halophytic vegetation indictor of salinity. Correlation coefficients between SDI and soil salinity were obtained and a model was adjusted to predict soil salinity. It is evident from the results that SDI is highly accordant with in-situ soil SAL values with the highly correlation of 0.9149. Variance accounted for by exponential models for SAL was of 83.7%. The SDI demonstrates a much better performance in measuring salinity soil since it takes into account both soil surface and halophytic vegetation growth in the modeling process. The SDI has the potential to provide a simple and low-cost salt-affected areas monitoring method in the remote estimation of salinization phenomena.