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Soil moisture monitoring in mountain areas by using high-resolution SAR images: results from a feasibility study

机译:利用高分辨率SAR图像监测山区土壤湿度:一项可行性研究的结果

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This paper presents the results of a two-year experiment carried out in mountain areas on soil moisture retrieval from remotely sensed images. In particular, fully polarimetric synthetic aperture radar (SAR) RADARSAT and single polarization ASAR images were used. During acquisitions of SAR over the test area in Alto Adige, Sudtirol region, northern Italy, extensive field measurements were carried out to characterize the area in terms of soil moisture variability, vegetation cover and soil properties. The area is equipped with 17 meteorological stations that also provide information on soil moisture on an hourly basis. To deal with the process of soil moisture retrieval from SAR images in this challenging environment, an advanced algorithm based on the support vector regression (SVR) technique and the integration of ancillary data were successfully proposed. The selected method was also combined with an innovative multi-objective strategy for model selection. The results indicate that the use of polarimetric features such as horizontal-horizontal polarization (HH) and horizontal-vertical polarization (HV) channels improves the estimation of SMC in the mountain area investigated, in particular because the HV channel contributes to disentangling the effect of vegetation on the radar signal. The root mean square error (RMSE) indicates accuracy of retrieval for soil moisture values of around 5%, which is well within the requirements for estimating soil moisture from remotely sensed data. This work was carried out with the main aim of demonstrating the feasibility of soil moisture retrieval in mountain areas with high-resolution images in view of the upcoming Sentinel 1 mission.
机译:本文介绍了一项为期两年的在山区进行的从遥感影像中获取土壤水分的实验结果。特别是,使用了全极化合成孔径雷达(SAR)RADARSAT和单极化ASAR图像。在意大利北部Sudtirol地区Alto Adige的测试区域上采集SAR期间,进行了广泛的野外测量,以从土壤湿度变化,植被覆盖和土壤特性等方面对该区域进行表征。该地区设有17个气象站,它们还每小时提供一次土壤湿度信息。为了应对这种挑战性环境下从SAR图像中获取土壤水分的过程,成功地提出了一种基于支持向量回归(SVR)技术和辅助数据集成的高级算法。所选方法还与创新的多目标策略进行了模型选择。结果表明,使用极化特征(例如,水平-水平极化(HH)和水平-垂直极化(HV)通道)可以改善所研究山区的SMC估算,特别是因为HV通道有助于消除干扰的影响。雷达信号上的植被。均方根误差(RMSE)表示大约5%的土壤水分值的检索精度,这完全符合从遥感数据估算土壤水分的要求。开展这项工作的主要目的是鉴于即将到来的Sentinel 1任务,以高分辨率图像展示山区土壤水汽回收的可行性。

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