Poreh Davod, Iodice Antonio, Natale Antonio, Riccio Daniele
Department of Electrical Engineering and Information Technology, University of Napoli Federico II, 80125 Napoli, Italy.
Institute of Remote Sensing of Environment (IREA), National Research Council (CNR), 80124 Napoli, Italy.
Sensors (Basel). 2020 Sep 7;20(18):5085. doi: 10.3390/s20185085.
The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany's Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature.
基于合成孔径雷达(SAR)数据反演土壤表面参数,尤其是土壤湿度和粗糙度,已成为大量研究的主题,科学文献中已有相关研究成果。然而,尽管基于理论/分析散射模型的精细方法已被提出并成功应用于实验研究,但在实际操作层面,通常采用的是具有多个可调参数的非常简单的经验模型。造成这种情况的原因之一是,基于分析散射模型的反演方法不易实现,非专业用户难以使用。与此相关的是,商业和免费提供的用于处理SAR数据的软件工具,虽然包括用于极化SAR数据基本处理的例程(例如相干性和协方差矩阵计算、保利分解等),但并未实现易于使用的表面参数反演方法。为了填补这一空白,本文提出了一个用户友好的计算机程序,用于从极化合成孔径雷达(PolSAR)图像中反演土壤表面参数。该程序基于极化双尺度模型(PTSM)和极化双尺度双分量模型(PTSTCM)提供的电磁散射理论预测,评估土壤介电常数、土壤湿度和土壤粗糙度。特别地,讨论了九种不同的反演方法,其适用性取决于所使用的极化数据(双极化或全极化)以及观测场景的特征(例如其地形和植被覆盖情况),以及它们在交互式数据语言(IDL)平台上的实现。详细介绍了来自德国德明试验场的一个具体例子,以便为该工具的使用提供初步指导。获得的反演结果与现有文献预期的结果一致。