Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, Bonn, Germany, 53113.
Environ Monit Assess. 2012 Apr;184(4):2475-85. doi: 10.1007/s10661-011-2132-5. Epub 2011 Jun 2.
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 km(2)) results were used to upscale soil salinity to a district area (∼300 km(2)). 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.
咸海流域的土壤盐度是可持续作物生产的主要限制因素之一。在种植季节前淋洗盐分通常是作物种植的前提条件，而且通常会均匀地应用预定水量，而不考虑盐分水平。淋洗时使用预定水量可能部分源于传统土壤盐度调查（基于采集土壤样本、实验室分析）无法及时生成高分辨率的盐度图。本文的目的是基于易于或廉价获得的环境参数（地形指数、遥感数据、到排水渠的距离和长期地下水观测数据），使用神经网络模型估算土壤盐分的空间分布。使用农场尺度（约 15 平方公里）的结果将土壤盐分放大到一个区（约 300 平方公里）的尺度。利用环境属性和土壤盐分关系，将土壤盐分的空间分布从农场尺度放大到区尺度，估算得到的平均土壤盐分值基本相似（估计值为 0.94 与 1.04 dS m(-1)）。地图的直观比较表明，估计的地图中土壤盐分分布均匀。放大效果令人满意；根据临界盐度阈值，大约 70-90%的位置的估计是正确的。
