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Multifrequency soil moisture inversion from SAR measurements with the use of IEM

机译:使用IEM通过SAR测量进行多频土壤水分反演

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This study focuses on the development of a consistent methodology for soil-moisture inversion from synthetic aperture radar (SAR) data with the use of the integral equation model (IEM), developed by A. K. Fung and colleagues, without the need to prescribe time-varying land-surface attributes as constraining parameters. Specifically, the dependence of backscatter coefficients obtained from synthetic aperture radar (SAR) on the soil dielectric constant, surface-roughness height, and correlation length was investigated. The IEM was used in conjunction with an inversion model to retrieve soil moisture by using multifrequency and multipolarization data (L-, C-, and X-bands) simultaneously. The results were cross validated with gravimetric observations obtained during the Washita '94 field experiment in the Little Washita Watershed, Oklahoma. The average error in the estimated soil moisture was of the order of 3.4%, which is comparable to that expected due to noise in the SAR data. The retrieval algorithm performed very well for low incidence angles and over bare soil fields, and it deteriorated slightly for vegetated areas and overall for very dry soil conditions. Although the original IEM model was developed for bare soil conditions only, one important result of this study was the fact that the retrieval algorithm performed well for vegetated conditions, as demonstrated by the fact that the convergence ratio varied between 92% (dry conditions) and 98% (wet conditions) of all pixels for all days of the experiment. The sensitivity of soil-moisture estimates to spatial aggregation of remote-sensing data before and after the retrieval also was investigated. The results suggest that there is potential to improve the operational utility of high-resolution SAR data for soil-moisture monitoring by compressing the SAR data (pre-aggregation) to a spatial resolution at least one order of magnitude above that of measurement. (C) Elsevier Science Inc., 2000. [References: 37]
机译:这项研究的重点是使用由AK Fung及其同事开发的积分方程模型(IEM),利用合成孔径雷达(SAR)数据开发的合成孔径雷达(SAR)数据反演土壤水分的一致方法,而无需规定时变地表属性作为约束参数。具体来说,研究了从合成孔径雷达(SAR)获得的反向散射系数对土壤介电常数,表面粗糙度高度和相关长度的依赖性。 IEM与反演模型结合使用,可以同时使用多频和多极化数据(L波段,C波段和X波段)来获取土壤水分。结果与在俄克拉荷马州Little Washita流域的Washita '94田间试验中获得的重量分析结果进行了交叉验证。估计的土壤水分的平均误差约为3.4%,与SAR数据中的噪声所预期的误差相当。对于低入射角和在裸露的土壤田地上,该检索算法的效果都很好,对于植被覆盖的地区以及在非常干燥的土壤条件下,总体而言,检索算法的性能都有所下降。尽管原始IEM模型仅针对裸露土壤条件开发,但这项研究的一个重要结果是,对于植被条件,检索算法的效果很好,这一事实证明了收敛比率在92%(干燥条件)和在实验的所有天中,所有像素的98%(潮湿条件)。还研究了土壤含水量估计值对检索前后遥感数据空间聚集的敏感性。结果表明,通过将SAR数据(预聚集)压缩到比测量分辨率高至少一个数量级的空间分辨率,有可能提高高分辨率SAR数据在土壤水分监测中的操作效用。 (C)Elsevier Science Inc.,2000。[参考:37]

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