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Application of Passive Microwave Data in Estimating Freeze-Thaw Dates of a Small Lake

机译:无源微波数据在小湖的冻融日期中的应用

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Our method uses coarse resolution passive microwave data to estimate freeze-thaw dates of soil in the region surrounding the lake. Using the soil freeze-thaw dates we then determine a high probability 'window' of days during which the lake freeze-thaw dates may occur. This paper mainly focuses on using passive microwave data to estimate this high probability window of days for the freeze and the thaw seasons of a lake. The importance of this project in our broader research goal of estimating freeze-thaw dates for small lakes can be explained as follows. Given a classifier that can classify SAR images of small lakes into 'ice' and 'no-ice' images (a parallel research project that we are involved in), we demonstrate that using passive microwave data to estimate the 'window' will improve the practical use of the classifier in detecting freeze-thaw dates of the lake, by reducing the number of datasets required (using only the datasets that fall within the window). By reducing the number of SAR datasets required for the algorithm we reduce both financial and computational cost of the classification algorithm. This in turn will make the method more affordable and improve the usability of the classification algorithm to keep track of lake freeze-thaw dates for a larger number of lakes. The passive microwave data collected, results obtained till date and related literature will be discussed in this paper.
机译:我们的方法使用粗糙的分辨率被动微波数据来估计湖周围地区的土壤的冻融日期。使用土壤冻融日期,我们确定可能发生湖冻冻融日期的高概率“窗口”。本文主要侧重于使用被动微波数据来估算冻结和湖泊的季节的天数的高概率窗口。本项目在我们更广泛的研究目标中估算小湖泊的冻融日期的重要性可以解释如下。给定可以将小湖的SAR图像分类为“ICE”和“无冰”图像(我们参与的并行研究项目),我们证明使用被动微波数据来估计“窗口”将改善“窗口”通过减少所需的数据集数量来检测湖泊的冻融日期的分类器的实际使用(仅使用窗口内的数据集)。通过减少算法所需的SAR数据集的数量,我们降低了分类算法的财务和计算成本。这反过来将使该方法更实惠,提高分类算法的可用性,以跟踪冻融湖泊日期的较大数量的湖泊。收集的被动微波数据,在本文中将讨论迄今为止的结果和相关文献。

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