ISRIC - World Soil Information Database
© European Communities, 1995-2007 | Since its beginning in 1966, ISRIC - World Soil Information has built up a collection of more than 20.000 articles, country reports, books and maps with emphasis on the developing countries. The subject emphasis is on soils, but related geographic information on climate, geology, geomorphology, vegetation, land use, and land suitability is also important. The map collection contains over 6000, mainly small-scale (1:250.000 or smaller) maps. more... |
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Many of these maps are completed by reports and related thematic and derived maps.
The collection is housed at Duivendaal 9, 6701 AR Wageningen, The Netherlands and is publicly accessible. The references of all items in the collection are included in the ISRIC - World Soil Information Database. New items are added regularly.
The functionality of the ISRIC - World Soil Information Database is being improved in collaboration with Wageningen UR library. New Features include: on-line access to over 3600 digital maps that can be down-loaded at high resolution and viewed on screen with a zoom facility; over 600 full-text reports in PDF format; country-specific searches based on Google maps, as well as basic and advanced search facility.
The website of the ISRIC - World Soil Information Database provides access to links of databases, national and international organizations, electronic books, newsletters , journals, and reference materials related to soil science.
Five thousand maps in the ISRIC collection scanned as a foundation for the European Digital Archive of Soil Maps (EuDASM) are available through the website of the ISRIC - World Soil Database as well. | |
November 23, 2009
Spatial Modeling of Soil Salinity Using Remote Sensing, GIS, and Field Data
This study has shown the benefit of using satellite images in generating accurate soil salinity maps. 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. Observed data was used in conjunction with satellite images. 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.
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Reports and maps

The main purpose of our acquisition policy, as
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