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22 October 2010 Remote sensing indicators to identify low and moderately salt-affected soils based on MODIS Terra and geochemical data
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Soluble salts in soils seriously compromise agricultural productivity around the world. Arid and semi-arid regions are most prone to salinization. Careful monitoring and surveying of salt-affected soils is needed to ensure sustainable development in such regions. Remote sensing techniques are being increasingly applied to investigate this phenomenon. Our approach is to map low and moderately salt-affected soils in northeast Brazil through the combination of remote sensing data and geochemical ground-based measurements. Spectral properties, salinity, vegetation and brightness indices were used to extract salinization features and patterns from the Brazilian soils. MODIS Terra data were selected to cover the 1.7 million km2 area and the images were taken during the summer 2008 sampling campaign. The electrical conductivity (EC) from 112 sites was determined (1:5 soil/water suspension method) to test the capability of each indicator to identify salt-affected areas based on correlations between indicators and electrical conductivity (ground truth). Eighteen indices emerged from the MODIS Terra images. A moderate correlation was found between electrical conductivity and the spectral indices. Salinity emerged as the most significant index. Spectral properties were used to define soil classes based on their degree of salinization. Near infrared (NIR) region from the electromagnetic spectrum showed high potential to separate different categories of salt-affected soil from MODIS multispectral data. A low correlation between vegetation indices and electrical conductivity indicates that these indices are inadequate when trying to discern features and patterns of salt affected areas on a large scale
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Moncef Bouaziz, Jörg Matschullat, and Richard Gloaguen "Remote sensing indicators to identify low and moderately salt-affected soils based on MODIS Terra and geochemical data", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78241I (22 October 2010);

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