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首页> 外文期刊>International journal of remote sensing >The effect of soil salinity on the use of the universal triangle method to estimate saline soil moisture from Landsat data: application to the SMAPEx-2 and SMAPEx-3 campaigns
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The effect of soil salinity on the use of the universal triangle method to estimate saline soil moisture from Landsat data: application to the SMAPEx-2 and SMAPEx-3 campaigns

机译：土壤盐分对使用通用三角法从Landsat数据估算盐渍土壤湿度中的影响：应用于SMAPEx-2和SMAPEx-3活动

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摘要

In this article, a method based on UTM called salinity-based soil moisture content (S_SMC) is developed. Since the soil moisture depends on the soil salinity (SS) in semi-arid regions, the S_SMC method employs the SS as an effective and augmented variable in conventional UTM to estimate SMC in these areas. In calibration step, initially, a linear regression model between the land surface temperature (LST), the normalized difference vegetation index (NDVI), and the SS is applied using in situ measurements to assess the influence of the SS in SMC estimation. Then, a non-linearity model is conducted through insertion of more terms in the linear equation and an optimal model of S_SMC is yielded. Moreover, the SS is obtained using a linear model from two selected salinity indices derived from Landsat images and in situ measurements. In estimation step, the LST, NDVI, and the SS are obtained using Landsat data. The S_SMC method is evaluated in the Soil Moisture Active Passive Experiment (SMAPEx)-2 and SMAPEx-3 campaigns in wet and dry conditions, respectively, over two scenes of Landsat images. The results demonstrated that the S_SMC method is appropriate in non-irrigated areas. In these areas, the S_SMC method improves R-2 (coefficient of determination) from 22% to 65% in SMAPEx-2 and from 24% to 50% in SMAPEx-3. Moreover, the results have shown that the SMC can be estimated at satellite level with a root mean square error of 0.06 and 0.02 (m(3) m(-3)) in wet and dry condition, respectively. Therefore, the SS is a key parameter to adjust conventional UTM to improve the SMC estimation by the S_SMC method.
机译：在本文中，开发了一种基于UTM的方法，称为基于盐度的土壤水分含量（S_SMC）。由于土壤湿度取决于半干旱地区的土壤盐分（SS），因此S_SMC方法将SS用作常规UTM中有效的且增加的变量，以估计这些区域的SMC。在校准步骤中，首先，使用原位测量应用地表温度（LST），归一化差异植被指数（NDVI）和SS之间的线性回归模型，以评估SS对SMC估计的影响。然后，通过在线性方程中插入更多项来建立非线性模型，并得出S_SMC的最佳模型。此外，SS是使用线性模型从Landsat影像和原位测量得出的两个选定的盐度指数中获得的。在估算步骤中，使用Landsat数据获得LST，NDVI和SS。在Landsat影像的两个场景中，分别在潮湿和干燥条件下的土壤水分主动被动实验（SMAPEx）-2和SMAPEx-3活动中评估了S_SMC方法。结果表明，S_SMC方法适用于非灌溉区域。在这些方面，S_SMC方法将SMAPEx-2中的R-2（测定系数）从22％提高到65％，并将SMAPEx-3中的R-2（测定系数）从24％提高到50％。此外，结果表明，在潮湿和干燥条件下，可以在卫星水平上分别估计出SMC的均方根误差为0.06和0.02（m（3）m（-3））。因此，SS是调整常规UTM以通过S_SMC方法改进SMC估计的关键参数。

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  • 来源
    《International journal of remote sensing》 |2017年第23期|6623-6652|共30页
  • 作者

    Emami Hassan; Mojaradi Barat; Safari Abdolreza;

  • 作者单位

    Univ Tehran, Univ Coll Engn, Sch Surveying & Geospatial Engn, Tehran, Iran;

    Iran Univ Sci & Technol, Sch Civil Engn, Dept Geomat, Tehran 16765163, Iran;

    Univ Tehran, Univ Coll Engn, Sch Surveying & Geospatial Engn, Tehran, Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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