Estimating the surface mass balance of the Greenland Ice Sheet based on remote sensing data and ice flux divergence
WEI Jianing1(), LUO Kai1, CHEN Yourong1, LI Peigen1, YANG Kang1,2,3()
1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China 2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China 3. Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China
近几十年,格陵兰冰盖表面物质平衡(surface mass balance,SMB)和溢出冰川崩解造成冰盖物质损失加速,其中SMB的贡献近年来持续增大。因此,掌握SMB时空分布对于理解格陵兰冰盖物质平衡具有重要意义。然而,研究格陵兰冰盖SMB的2种主要手段中,区域气候模型模拟的SMB存在较大不确定性,溢出冰川通量门遥感观测仅能间接获得通量门上游流域整体的SMB值,难以反映SMB的空间分布。本研究提出了一种综合冰通量散度的格陵兰冰盖表面物质平衡遥感估算方法,能够较为准确地估算SMB空间分布: ①利用ICESat-2卫星激光测高数据获取格陵兰冰盖高程年际变化量; ②利用MEaSUREs冰流速遥感数据和BedMachine冰厚度数据,采用基于像元的有限差分法计算冰通量散度,估算冰流造成的冰盖高程变化,进而从ICESat-2冰盖高程变化中减去由冰流造成的冰盖高程变化,获得由SMB引起的冰盖高程变化; ③利用粒雪密实化模型将SMB引起的高程变化转换为质量变化,即可反映格陵兰冰盖年际SMB空间分布。研究采用该方法估算了2019年与2020年格陵兰冰盖SMB空间分布,通过与观测站点实测SMB对比分析,表明本方法估算SMB的精度较高(RMSE为0.519 m w.e.),优于区域气候模型(RMSE为0.565~0.877 m w.e.),是一种较为可靠的格陵兰冰盖表面物质平衡时空分布遥感估算方法。
In recent decades, the surface mass balance (SMB) and the calving of outlet glaciers have accelerated the mass loss of the Greenland Ice Sheet (GrIS), with SMB’s contribution continuing to increase. Therefore, determining SMB’s spatiotemporal distribution is critical for understanding the mass balance of the GrIS. Currently, the regional climate model and the remote sensing observation of outlet glacier flux gates serve as two primary calculation methods for the GrIS’s SMB. However, the former method results in large uncertainties in the SMB simulation. The latter method can only indirectly estimate the overall SMB value for the upper reaches of the flux gate, failing to reflect the spatial distribution of SMB. This study proposed a method for estimating the GrIS’s SMB based on remote sensing data and ice flux divergence, obtaining the relatively accurate spatial distribution of SMB. First, the interannual variation in the elevation of the GrIS was derived from ICESat-2 laser altimetry data. Second, based on MEaSUREs-derived glacier flow velocity data and BedMachine-derived ice thickness data, the ice flux divergence was calculated using the pixel-based finite difference method to estimate the GrIS’s elevation changes caused by glacier flow. The GrIS’s elevation changes caused by SMB were then obtained by subtracting the elevation changes caused by glacier flow from the ICESat-2 elevation data. Third, through the firn densification model, the elevation changes caused by SMB were converted into mass changes to reflect the interannual spatial distribution of the GrIS’s SMB. The proposed method was applied to estimate the spatial distribution of the GrIS’s SMB in 2019 and 2020, yielding relatively high accuracy (RMSE=0.519 m w.e.) in comparison with the measured SMB from the observation station, and outperforming the regional climate model (RMSE=0.565 m to 0.877 m w.e.), ultimately demonstrating its reliability.
魏佳宁, 罗凯, 陈又榕, 李培根, 杨康. 综合冰通量散度的格陵兰冰盖表面物质平衡遥感估算[J]. 自然资源遥感, 2025, 37(2): 80-87.
WEI Jianing, LUO Kai, CHEN Yourong, LI Peigen, YANG Kang. Estimating the surface mass balance of the Greenland Ice Sheet based on remote sensing data and ice flux divergence. Remote Sensing for Natural Resources, 2025, 37(2): 80-87.
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2025, Vol. 37 
), 罗凯1, 陈又榕1, 李培根1, 杨康1,2,3(
