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首页> 外文期刊>International journal of remote sensing >Potential of soil moisture retrieval for tropical peatlands in Indonesia using ALOS-2 L-band full-polarimetric SAR data
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Potential of soil moisture retrieval for tropical peatlands in Indonesia using ALOS-2 L-band full-polarimetric SAR data

机译：印度尼西亚热带泥土土壤水分检索的潜力使用Alos-2 L-BAND全极化SAR数据

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摘要

In this paper, a soil moisture retrieval from full-polarimetric synthetic aperture radar (SAR) data is investigated for sparsely vegetated soil surfaces. An improved retrieval method adapting the variations in vegetation is proposed by incorporating the generalized volume model into the polarimetric two-scale two-component model (PTSTCM). The feasibility of the method, termed as the adaptive PTSTCM, has been tested for tropical peatland sites in Indonesia which exhibit a variety of sparse vegetation cover on soil after land clearing activities. The data were collected in March and August 2017 with the time domain reflectometry (TDR) probe for a total of 18 sample points over 11 regions. The method was applied to ALOS-2 L-band quad-pol SAR data that were acquired simultaneously with field measurements. We compared the results between the proposed adaptive PTSTCM and the original PTSTCM that utilizes specific types of volume model (i.e., randomly, horizontally, and vertically oriented volume models). Scatterplots of estimated versus measured results reveal that the adaptive PTSTCM yields a root-mean-square error (RMSE) of 5.1vol. and inversion rate of 35.0 and 58.5 for March and August data, respectively, which are found to be superior to those of the original PTSTCM.
机译：本文研究了来自全极性合成孔径雷达（SAR）数据的土壤水分检索，对稀疏植被的土壤表面进行了研究。提出了一种改进的检索方法，通过将广义的体积模型结合到极化的双级两组分模型（PTSTCM）中来提出适应植被的变化。作为适应性PTSTCM称为适应性PTSTCM的方法的可行性已经在印度尼西亚的热带泥炭地遗址进行了测试，在土地清算活动后在土壤上展示了各种稀疏植被覆盖。该数据于2017年3月和2017年8月收集，时域反射测量仪（TDR）探针总共18个样本点，超过11个区域。该方法应用于与现场测量同时获取的Alos-2 L波段四极SAR数据。我们将所提出的自适应PTSTCM和原始PTSTCM之间的结果进行比较，其利用特定类型的体积模型（即，随机，水平和垂直定向的体积模型）。估计与测量结果的散点片表明，自适应PTSTCM产生5.1Vol的根均方误差（RMSE）。 3月份和8月数据的反演率分别为35.0和58.5，发现其被发现优于原始PTSTCM的数据。

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  • 来源
    《International journal of remote sensing》 |2019年第16期|5938-5956|共19页
  • 作者

    Izumi Y.; Widodo J.; Kausarian H.; Demirci S.; Takahashi A.; Razi P.; Nasucha M.; Yang H.; Tetuko J. S. S.;

  • 作者单位

    Tohoku Univ Grad Sch Environm Studies Sendai Miyagi Japan;

    Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan;

    Univ Islam Riau Engn Geol Program Fac Engn Kota Pekanbaru Indonesia;

    Mersin Univ Elect & Elect Fac Engn Mersin Turkey;

    Chiba Univ Ctr Environm Remote Sensing CEReS Chiba Japan;

    Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan;

    Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan;

    Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan;

    Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan|Chiba Univ Ctr Environm Remote Sensing CEReS Chiba Japan;

  • 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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