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首页> 外文期刊>International journal of remote sensing >The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas
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The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

机译：紧凑型极化分解算法在农业地区L波段PolSAR数据中的应用

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摘要

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-alpha) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter alpha(s). In addition to the parameter alpha(s) from m-a decomposition, the applicability of other scattering mechanism parameters, such as delta (relative phase) and chi (degree of circularity) from m-delta and m-chi decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR'2006 campaign over the Gormin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9-12 and 9-15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters alpha(s) and chi for epsilon(s) 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (5 vol.%) over the various crop growing season.
机译：本文利用模拟的L波段紧凑型极化合成孔径雷达（PolSAR）数据，研究了最近开发的紧凑型极化分解和反演算法在低农业植被覆盖率下估算土壤水分的适用性。在随机定向的植被体积和反射对称性的假设下，通过基于模型的紧凑极化分解（m-alpha）将表面散射成分与植被的体积成分分离。将提取的表面散射成分与两个基于物理学的低频表面散射模型（例如扩展的Bragg（X-Bragg）和极化两尺度模型（PTSM））进行比较，以便将土壤水分转化为相应的基于模型和数据的表面散射机制参数alpha。除了来自ma分解的参数alpha之外，还研究了其他散射机制参数的适用性，例如m-delta和m-chi分解的delta（相对相位）和chi（圆度）转化土壤水分。该算法应用于来自德国北部Gormin测试地点的AgriSAR'2006战役的模拟L波段紧凑型极化E-SAR数据的时间序列。紧凑的PolSAR衍生的土壤水分已针对原位时域反射法（TDR）测量进行了验证。包括三种不同作物类型的不同生长阶段，使用X-Bragg模型和PTSM估算的土壤湿度值分别表示9-12％和9-15 vol。％的总体均方根误差（RMSE）。包括作物的所有物候期和不同的土壤水分条件（4％至34％（体积））的植被覆盖土壤的转化率范围为5％至40％。使用紧凑型极化仪对土壤水分反演的时间序列结果表明，由于散射机理参数α和chi对> 20的敏感性较低，因此该算法对生长中的农作物对湿润土壤的敏感性较低。需要进一步的发展和研究，以在紧凑的PolSAR数据上反演土壤水分，该数据具有很高的反演率，并且在各个作物生长期均具有较低的RMSE（<5 vol。％）。

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  • 来源
    《International journal of remote sensing》 |2018年第22期|8337-8360|共24页
  • 作者

    Ponnurangam G. G.; Rao Y. S.;

  • 作者单位

    Indian Inst Technol, Ctr Studies Resources Engn, Microwave Remote Sensing Lab, Mumbai 400076, Maharashtra, India;

    Indian Inst Technol, Ctr Studies Resources Engn, Microwave Remote Sensing Lab, Mumbai 400076, Maharashtra, India;

  • 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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