Estimating Soil Salinity Under Various Moisture Conditions: An Experimental Study
Estimating Soil Salinity Under Various Moisture Conditions: An Experimental Study
复制标题
估算不同湿度条件下的土壤盐分：实验研究
DOI：
10.1109/tgrs.2016.2646420
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发表时间：
2017-05-01
影响因子：
8.2
通讯作者：
Yu, Ying
中科院分区：
文献类型：
--
作者：
Yang, Xiguang;Yu, Ying
土壤盐渍化是世界范围内最常见的土地荒漠化过程之一。它是一种严重的环境危害，威胁着生态系统的稳定。遥感技术作为一种快速、廉价的工具，结合土壤光谱测量，在土地盐渍化的识别和制图中受到广泛关注。然而，由于土壤水分的影响往往淹没了盐分对土壤反射光谱的影响，土壤水分成为限制利用土壤反射光谱监测土壤盐分的主要因素。土壤含水量过高会导致利用土壤反射率数据估算土壤盐分的失效。本文在室内对照试验的基础上，建立了一个基于指数函数的半解析模型，用于估算不同水分条件下的土壤盐分含量。将该模型应用于湿土壤反射率估算土壤干物质含量时，其均方根误差和平均相对误差分别为0.799 g/kg和31.294%。综合考虑了土壤水分和盐分对土壤反射率的影响，半解析模型减小了SSC的估计误差。本文提出的方法提供了一种新的方法来估计土壤盐分的土壤光谱在不同的土壤水分条件下，它将是一个潜在的应用大尺度SSC制图。
Soil salinization is one of the most common land desertification processes that can be found worldwide. It is a certainly severe environment hazard and threatens the stability of ecosystems. As a rapid and inexpensive tool, remote sensing technology combining with the measurements of soil spectra has been widely concerned on identifying and mapping salt effect on lands. However, as effects of the soil moisture often immerge the effects of salt to soil reflectance spectra, soil moisture became a major factor to restrict soil salinity monitoring from soil reflectance. High soil moisture content will lead to failure on soil salinity estimation from soil reflectance data. In this paper, a semianalytical model using an exponent function was developed to estimate soil salt content (SSC) under different moisture levels based on a control laboratory experiment. And the root-mean-square error and mean relative error were 0.799 g/kg and 31.294%, respectively, when the model was applied to estimate SSCs by wet soil reflectance. To sum up, considering both effects of soil moisture and soil salt on soil reflectance, the semianalytical model reduced SSC estimated error. The approach presented in this paper provides a new way of estimating soil salinity from soil spectra under various soil moisture conditions, and it will be a potential application for large-scale SSC mapping.