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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Soil Moisture Estimation Using Hybrid Polarimetric SAR Data of RISAT-1
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Soil Moisture Estimation Using Hybrid Polarimetric SAR Data of RISAT-1

机译：RISAT-1的混合极化SAR数据估算土壤湿度

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摘要

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils ( –8.9 vol.%) with inversion rates of around 30–70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions.
机译：本文研究了混合极化合成孔径雷达的能力，以估算裸露和植被繁茂的农业土壤上的土壤水分。开发了一种基于紧凑极化分解以及表面成分反演的新方法，以获取表层土壤水分。应用基于模型的紧凑分解技术，以在随机定向的植被体积的假设下获得表面散射分量。去除植被后，通过与两个基于物理学的散射模型（积分方程方法（IEM）和扩展的布拉格模型（X-Bragg））建模的表面分量进行比较，可以将表面散射分量转化为土壤水分（植被下）。基于两层（地面上的随机体积）散射模型的已开发算法被应用于在2014年4月至2014年10月通过Wallerfing测试获得的混合极化C波段RISAT-1右圆形发射线性接收数据的时间序列在德国下巴伐利亚行政区。相对于频域反射法测量结果验证了所取回的土壤水分。包括整个生长期（所有采集的日期）和所有作物类型，使用X-Bragg模型估算的土壤水分值表明总有效值为7％（体积），使用IEM模型则为10％（体积）。拟议的混合极化土壤水分反演算法适用于裸土（–8.9 vol。％），反演率约为30–70％。植被覆盖土壤的转化率介于5％至40％之间，包括作物的所有物候期和不同的土壤湿度条件。

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