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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Polarimetric Two-Scale Two-Component Model for the Retrieval of Soil Moisture Under Moderate Vegetation via L-Band SAR Data
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Polarimetric Two-Scale Two-Component Model for the Retrieval of Soil Moisture Under Moderate Vegetation via L-Band SAR Data

机译：利用L波段SAR数据反演植被中等土壤水分的极化两分量两分量模型。

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摘要

Recently, we have proposed a retrieval technique based on an original polarimetric two-scale model (PTSM), which is able to estimate the volumetric water content of bare soils from polarimetric synthetic aperture radar (SAR) data. In this paper, to extend the field of application of our retrieval technique to moderately vegetated soils, we combine the PTSM with a randomly oriented dipole-cloud volumetric scattering model, thus obtaining a polarimetric two-scale two-component model (PTSTCM). By using this model we show that, in principle, suitable combinations of the polarimetric SAR channels, i.e., “modified copolarized ratio” and “modified copolarized correlation coefficient,” are related only to the surface parameters because the dependence on the unknown volumetric contribution intensity cancels out. This allows us to retrieve soil moisture from L-band SAR data not only for bare soils but also in moderately vegetated areas, interested by a nonnegligible volumetric scattering contribution, provided that the double-bounce scattering component is negligible. In addition, describing the surface component by using the PTSM allows us to mitigate the well-known problem of overestimating the volume component, which affects most model-based target decompositions and that may lead to the so-called “negative power problem.” Both the performance and validity limits of the estimation method are assessed by comparing the obtained soil-moisture retrieval results to “” measurements. To this aim, data from SMEX'03 and AGRISAR'06 campaigns available in literature are considered. They refer to sites with a flat topography. In particular, we employ the AGRISAR database, which includes data from several fields covering a period that spans all the phases of vegetation growth, to explore the validity range of the method in terms of vegetation height. Results of PTSTCM are also co- pared with those of available three-component methods (3CMs) employing more simplified surface scattering models. It has turned out that the use of the PTSTCM provides more accurate results for low vegetation (average modulus of soil moisture relative error for vegetation height smaller than 50 cm: 18.5% for the PTSTCM and 34% for the 3CM). Conversely, for higher vegetation, 3CMs should be more conveniently employed (average modulus of relative error for vegetation height greater than 50 cm: 17.5% for the 3CM and about 100% for the PTSTCM). A simple method to adaptively and automatically (i.e., based on measured data) select between PTSTCM and 3CM on a pixel-by-pixel basis is finally suggested, leading to a less than 20% average modulus of relative error on the retrieved soil moisture for the considered fields over the entire vegetation growth cycle.
机译：最近，我们提出了一种基于原始极化两尺度模型（PTSM）的检索技术，该技术能够根据极化合成孔径雷达（SAR）数据估算裸土的体积含水量。在本文中，为了将我们的检索技术的应用领域扩展到中等植被的土壤，我们将PTSM与随机定向的偶极云体积散射模型相结合，从而获得了极化两尺度两分量模型（PTSTCM）。通过使用该模型，我们表明，原则上，极化SAR通道的适当组合（即“修正的共极化比”和“修正的共极化相关系数”）仅与表面参数有关，因为对未知体积贡献强度的依赖性取消。这使我们不仅可以从裸露土壤的L带SAR数据中获取土壤水分，还可以在中等植被区域（对体积散射的贡献不可忽略）感兴趣的条件下获取土壤水分，前提是双反弹散射分量可忽略不计。此外，通过使用PTSM描述表面分量，可以减轻众所周知的高估体积分量的问题，该问题会影响大多数基于模型的目标分解，并可能导致所谓的“负功率问题”。通过将获得的土壤水分取回结果与“”测量值进行比较，评估了评估方法的性能和有效性极限。为此，考虑了文献中提供的SMEX'03和AGRISAR'06活动的数据。他们指的是具有平坦地形的站点。特别是，我们使用了AGRISAR数据库，该数据库包括跨越一个植被生长所有阶段的多个领域的数据，以探讨该方法在植被高度方面的有效性范围。 PTSTCM的结果也与采用更简化的表面散射模型的可用三组分方法（3CM）的结果进行了比较。事实证明，使用PTSTCM可以为低植被提供更准确的结果（植被高度小于50 cm时土壤水分的平均模量相对误差：PTSTCM为18.5％，3CM为34％）。相反，对于较高的植被，应更方便地采用3CM（大于50 cm的植被高度的相对误差平均模数：3CM为17.5％，PTSTCM为约100％）。最终提出了一种简单的方法来自适应地自动（即基于测量数据）在逐像素的基础上在PTSTCM和3CM之间进行选择，从而导致对于土壤水分，相对湿度的平均误差模量小于20％。在整个植被生长周期中考虑的田地。

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