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Detection of Inland Open Water Surfaces Using Dual Polarization L-Band Radar for the Soil Moisture Active Passive Mission

机译:利用双极化L波段雷达探测内陆开放水面,进行土壤水分主动被动任务

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摘要

A dual-copolarization algorithm to classify inland open water bodies free of flooded vegetation using an L-band radar is presented and evaluated, with a view to applying the method to the Soil Moisture Active Passive (SMAP) mission for hydrological science and soil moisture retrieval applications. Past radar-based water body detection algorithms have applied a threshold to a single-polarization measurement, with water body detection declared if the observed cross section is less than the specified threshold. However, such methods are subject to ambiguities associated with scene variability and terrain slopes, making a universal threshold value difficult to derive and complicating the global application of such methods. Because SMAP will provide measurements in both HH and VV polarizations, the copolarization ratio is also available for water body detection. A threshold of −3 dB applied to the HH/VV polarization ratio is found effective in detecting water bodies at 40° incidence angle based on analysis of theoretical model predictions and measurements from airborne synthetic aperture radar and the spaceborne Aquarius scatterometer. When the water surface is calm and its radar response is very small (i.e., at the radar thermal noise level), the HH/VV ratio method fails. However, a combination of an HH/VV threshold (at −3 dB) and an HH threshold (at −25 dB) is shown to allow water body classification even in this situation. This proposed “combined” algorithm is assessed in four different geophysical scenarios. The resulting water body detection error is shown to be less than 10% for these cases, which satisfies SMAP requirements to allow accurate soil moisture retrieval, and the corresponding false alarm rate is smaller than 2%. The robustness of the proposed approach to subpixel heterogeneity has been also investigated. The performance of the algorithm remains sensitive to the noise level of the radar observatio- s: for SMAP, a radar noise-equivalent sigma0 of −28.5 dB or less is required in order to facilitate acceptable performance.
机译:提出并评估了一种使用L波段雷达对内陆开放水域无水淹植被进行分类的双共极化算法,以期将该方法应用于土壤水分主动无源(SMAP)任务,以进行水文科学和土壤水分检索应用程序。过去基于雷达的水体检测算法已将阈值应用于单极化测量,如果观察到的横截面小于指定的阈值,则宣布进行水体检测。然而,这些方法容易受到与场景可变性和地形坡度相关的模糊性的影响,这使得难以获得通用阈值并使这种方法的全球应用变得复杂。因为SMAP将提供HH和VV极化的测量,所以共极化比也可用于水体检测。根据理论模型的预测分析以及机载合成孔径雷达和星载水瓶座散射仪的测量结果,发现对HH / VV极化比施加的-3 dB阈值可有效检测40°入射角的水体。当水面平静且雷达响应非常小(即处于雷达热噪声水平)时,HH / VV比方法将失败。但是,即使在这种情况下,HH / VV阈值(-3 dB)和HH阈值(-25 dB)的组合也显示出可以对水体进行分类。在四种不同的地球物理方案中评估了该提议的“组合”算法。在这些情况下,显示出的水体检测误差小于10%,这满足了SMAP要求,可以准确地取回土壤中的水分,相应的误报率也小于2%。还研究了所提出的亚像素异质性方法的鲁棒性。该算法的性能仍然对雷达观测的噪声水平敏感:对于SMAP,要求雷达噪声等效sigma0为-28.5 dB或更小,以促进可接受的性能。

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