Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)
Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)
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DOI：
10.1109/tgrs.2023.3268073
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发表时间：
2023
影响因子：
8.2
通讯作者：
U. Herzfeld;T. Trantow;Huiling Han;Ellen M. Buckley;S. Farrell;Matthew Lawson
中科院分区：
文献类型：
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作者：
U. Herzfeld;T. Trantow;Huiling Han;Ellen M. Buckley;S. Farrell;Matthew Lawson
随着气候变暖，北极海冰覆盖从常年性向季节性转变迫在眉睫，了解融化积水对于了解新北极的变化至关重要。美国国家航空和航天局（美国航天局）的冰、云和陆地高程卫星（ICESat-2）有能力测量和监测北极融化的开始和融化的进展。然而，ICESat-2标准海冰产品目前没有确定池塘，其中只有一个表面被确定。本文的目的是介绍一种数学算法，便于自动检测的ICESat-2先进地形激光高度计系统（ATLAS）的数据，检索两个表面的高度，池塘表面和底部，和测量的深度和宽度的融化池。ICESat-2搭载了第一个星载多光束微脉冲光子计数激光高度计系统，工作频率为532纳米。ATLAS数据被记录为离散光子点云。分叉海冰反射体的密度-维数算法（DDA-分叉海冰）是一种自适应算法，它利用径向基函数计算密度场和阈值函数，自动适应背景、表观表面反射率和某些仪器效应的变化，解决了ATLAS数据沿航迹标称间距0.7 m附近的池塘探测问题。DDA分叉海冰应用于2019年和2020年北极海冰多年融化季节的大型ICESat-2数据集。通过与手动强制算法的比较来评估结果。
As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. National Aeronautics and Space Administration (NASA)’s Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not identified on the ICESat-2 standard sea-ice products, in which only a single surface is determined. The objective of this article is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With ATLAS, ICESat-2 carries the first spaceborne multibeam micropulse photon-counting laser altimeter system, operating at 532-nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an autoadaptive algorithm that solves the problem of pond detection near the 0.7-m nominal along-track spacing of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in the background, apparent surface reflectance, and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 datasets from the 2019 and 2020 melt seasons in the multiyear Arctic sea-ice region. Results are evaluated by comparison with those from a manually forced algorithm.