利用雪微波辐射传输模型(SMRT)对湖冰融化条件下的合成孔径雷达(SAR)后向散射进行前瞻性建模

Justin Murfitt, Claude Duguay, G. Picard, Juha Lemmetyinen
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摘要

摘要监测湖冰对维护运输路线非常重要,但近几十年来,现场观测的数量却在减少。遥感技术一直在努力填补这一观测空白,其中有源微波传感器,特别是合成孔径雷达(SAR)是一项重要技术。然而,潮湿条件对雷达的影响以及在这些条件下相互作用的变化在很大程度上被忽视了。了解这些相互作用非常重要,因为气候变暖很可能导致泥泞层的增加。本研究利用雪微波辐射传输(SMRT)模型对芬兰奥卢杰尔维湖的后向散射进行了前向模拟实验,以弥补这一不足。实验在干燥、中度潮湿和饱和的条件下进行。这些实验反映了 2020-2021 年冰季期间的实地观测结果。干雪实验结果支持冰水界面表面散射的主导地位。然而,在引入湿雪层的条件下,主要散射界面会根据湿雪层的位置发生变化。在冰表面添加饱和层会导致最高的反向散射值,这是因为上覆干雪和泥泞层之间产生了较大的介电对比。在 SMRT 中改进这些条件的表示也有助于更准确地检索湖冰属性(如粗糙度),这对于冰厚度等其他属性的反演建模至关重要。
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Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model
Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.
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