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Spatial Modeling of Soil Salinity Using Remote Sensing, GIS, and Field Data

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Книга по Требованию    
2008 руб
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In its early stages, soil salinization reduces soil productivity, but in advanced stages, salinization kills all vegetation and transforms fertile and productive land to barren waste. Three models were applied to predict soil salinity from remote sensing: the ordinary least squares model (OLS), spatial autoregressive model (SAR), and modified kriging model. Any integration of field data, GIS, and remote sensing is considered weak unless some strong statistical measures are introduced. Corn and alfalfa crops were selected as indicators of soil salinity. Five images were acquired from Aster, Ikonos, and Landsat to check the correlation between measured soil salinity and remote sensing data. Remote sensing data has great potential for monitoring dynamic processes, including salinization. The environmental damage that salinity can cause is massive. This study has demonstrated a more efficient and accurate way of estimating soil salinity from remote sensing data that should help the efforts toward sustainable agriculture. Observed data was used in conjunction with satellite images. This study has shown the benefit of using satellite images in generating accurate soil salinity maps.
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