New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator
New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator
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DOI：
10.5194/tc-11-1987-2017
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发表时间：
2017-08
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影响因子：
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通讯作者：
C. Gabarró;A. Turiel;P. Elosegui;Joaquim A. Pla-Resina;M. Portabella
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文献类型：
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作者：
C. Gabarró;A. Turiel;P. Elosegui;Joaquim A. Pla-Resina;M. Portabella
抽象的。北极海的业务和气候研究需要监测海冰密集度。目前用于估计海冰密集度的技术有一些局限性，例如大气的影响、冰的物理温度以及雪和融化的存在。在过去的几年里，L波段辐射测量已成功地用于研究海冰的一些性质，特别是海冰厚度。然而，尚未探索卫星L波段观测在获取海冰密集度方面的潜力。在本文中，我们提出了初步的证据表明，土壤水分海洋盐度（SMOS）使命的数据可以用来估计海冰浓度。我们的方法，最大似然估计（MLE）的基础上，利用海冰和海水的辐射特性的显着差异。此外，100%海冰和100%海水的亮温以及它们的组合值（偏振和角差）在冬季和春季都非常稳定，因此它们对物理温度和其他地球物理参数的变化具有鲁棒性。因此，我们可以只使用两组连接点，一个用于夏季，另一个用于冬季，来计算海冰浓度，从而得到更可靠的估计。在分析了整个北极地区2014年全年的情况后，我们发现，与海洋和海冰卫星应用设施（OSI SAF）数据集相比，用我们的方法获得的海冰浓度得到了很好的确定。然而，当薄海冰存在（冰厚l0.6米），该方法低估了实际的海冰浓度。我们的研究结果开辟了一个系统的利用SMOS数据监测海冰浓度，至少在特定的季节。此外，SMOS数据可以与其他传感器的数据协同结合，以监测泛北极海冰状况。
Abstract. Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness l 0.6 m), the method underestimates the actual sea ice concentration. Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor pan-Arctic sea ice conditions.