The assessment of spatial distribution of soil salinity risk using neural network |
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Authors: | Akmal Akramkhanov Paul L G Vlek |
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Institution: | Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, Bonn, Germany, 53113. akmal@zef.uzpak.uz |
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Abstract: | Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts
before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly
to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate
from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate
timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity
based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains,
and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km2) results were used to upscale soil salinity to a district area (∼300 km2). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity
from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94
vs. 1.04 dS m−1). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling
proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated. |
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