ICESat-2 Arctic sea ice thickness retrieved using multi-source snow depth data during freezing seasons (October to April)
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This dataset provides Arctic sea ice thickness (SIT) estimates retrieved using multi-source snow depth data during the Arctic freezing seasons from November 2018 to December 2020. The dataset includes six cases of sea ice thickness, which were derived from various snow depth schemes, including: passive microwave algorithms (FY3/MWRI, ASD_MR), reanalysis (TOPAZ4b), modified climatology (MW99/AMSR2), a Lagrangian snow model (SMLG), and dual-satellite altimetry (KuLa). The data format is MATLAB (.mat), which can be opened and analyzed using MATLAB. This dataset serves as a valuable resource for improving the accuracy of ICESat-2 Arctic SIT retrievals, particularly by addressing biases introduced by snowpack interpretation, and offers insights into the seasonal and spatial variations of Arctic sea ice thickness. Li, L., Chen, H., Guan, L., 2021. Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI. Remote Sensing 13, 1457. https://doi.org/10.3390/rs13081457. Copernicus Marine Service (CMEMS), 2022. “Product User Manual for Arctic Ocean Physical and BGC Analysis and Forecasting Products.” Hendricks, S., Paul, S., 2023. Product User Guide & Algorithm Specification - AWI CryoSat-2 Sea Ice Thickness (version 2.6) Issued by. He, L., Xue, B., Hui, F., Xu, S., Chen, Z., Cheng, X., 2024. Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data. IEEE Transactions on Geoscience and Remote Sensing 62, 1–15. https://doi.org/10.1109/TGRS.2024.3358340. Liston, G.E., Polashenski, C., Rösel, A., Itkin, P., King, J., Merkouriadi, I., Haapala, J., 2018. A Distributed Snow-Evolution Model for Sea-Ice Applications (SnowModel). Journal of Geophysical Research: Oceans 123, 3786–3810. https://doi.org/10.1002/2017JC013706. Landy, J. C., de Rijke-Thomas, C., Nab, C., Lawrence, I., Glissenaar, I. A., Mallett, R. D. C., Fredensborg Hansen, R. M., Petty, A., Tsamados, M., Macfarlane, A. R., and Braakmann-Folgmann, A.: Anticipating CRISTAL: an exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023, The Cryosphere, 20, 183–208, https://doi.org/10.5194/tc-20-183-2026, 2026. Product Temporal resolution Grid resolution Estimation Method Reference FY3/MWRI Daily 12.5 km passive microwave algorithm (Li et al., 2021) TOPAZ4b Daily 12.5 km reanalysis (CMEMS et al., 2022) MW99/AMSR2 Monthly 25 km modified climatology (Hendricks and Paul, 2023) ASD_MR Daily 25 km passive microwave algorithm (He et al., 2024) SMLG Daily 25 km Lagrangian snow evolution model (Liston et al., 2018) KuLa Monthly 25 km dual-satellite altimetry (Landy et al., 2026)
本数据集提供了2018年11月至2020年12月北极冰封季期间，基于多源积雪深度数据反演得到的北极海冰厚度（Sea Ice Thickness, SIT）估算结果。本数据集包含6组海冰厚度反演结果，均源自不同的积雪深度反演方案，具体包括：被动微波算法（FY3/MWRI、ASD_MR）、再分析数据（TOPAZ4b）、修正气候学方法（MW99/AMSR2）、拉格朗日积雪模型（SMLG）以及双卫星测高法（KuLa）。 本数据集的数据格式为MATLAB（.mat），可通过MATLAB软件打开并开展分析。本数据集对于提升ICESat-2北极海冰厚度反演的精度具有重要应用价值，尤其可用于校正由积雪层解释带来的反演偏差，同时可为揭示北极海冰厚度的季节与空间变化规律提供参考依据。 ### 参考文献 1. Li, L., Chen, H., Guan, L., 2021. 《基于FY3B/MWRI反演北极海冰积雪深度》，《遥感》(Remote Sensing) 13, 1457. https://doi.org/10.3390/rs13081457. 2. 哥白尼海洋服务中心（Copernicus Marine Service, CMEMS）, 2022. 《北极海洋物理与生物地球化学分析及预报产品用户手册》. 3. Hendricks, S., Paul, S., 2023. 《AWI CryoSat-2海冰厚度产品用户指南与算法规范（版本2.6）》. 4. He, L., Xue, B., Hui, F., Xu, S., Chen, Z., Cheng, X., 2024. 《基于被动微波辐射计数据实现全冬季北极海冰逐日积雪深度反演》，《IEEE地球科学与遥感汇刊》(IEEE Transactions on Geoscience and Remote Sensing) 62, 1–15. https://doi.org/10.1109/TGRS.2024.3358340. 5. Liston, G.E., Polashenski, C., Rösel, A., Itkin, P., King, J., Merkouriadi, I., Haapala, J., 2018. 《适用于海冰应用的分布式积雪演化模型（SnowModel）》，《地球物理研究杂志：海洋》(Journal of Geophysical Research: Oceans) 123, 3786–3810. https://doi.org/10.1002/2017JC013706. 6. Landy, J. C., de Rijke-Thomas, C., Nab, C., Lawrence, I., Glissenaar, I. A., Mallett, R. D. C., Fredensborg Hansen, R. M., Petty, A., Tsamados, M., Macfarlane, A. R., and Braakmann-Folgmann, A.: 《展望CRISTAL：2018–2023年北极海冰上空多频卫星测高积雪深度反演探索》，《冰冻圈》(The Cryosphere) 20, 183–208, https://doi.org/10.5194/tc-20-183-2026, 2026. ### 产品参数详情 | 产品标识 | 时间分辨率 | 网格分辨率 | 反演方法 | 参考文献 | | ---- | ---- | ---- | ---- | ---- | | FY3/MWRI | 逐日 | 12.5 km | 被动微波算法 | (Li et al., 2021) | | TOPAZ4b | 逐日 | 12.5 km | 再分析数据 | (CMEMS et al., 2022) | | MW99/AMSR2 | 逐月 | 25 km | 修正气候学方法 | (Hendricks and Paul, 2023) | | ASD_MR | 逐日 | 25 km | 被动微波算法 | (He et al., 2024) | | SMLG | 逐日 | 25 km | 拉格朗日积雪演化模型 | (Liston et al., 2018) | | KuLa | 逐月 | 25 km | 双卫星测高法 | (Landy et al., 2026) |
