Modern remote sensing techniques, such as Synthetic Aperture Radar (SAR), can measure the direction and intensity of glacier flow. Yet the question remains as to what these measurements reveal about glaciers' adjustment to the warming climate. Here, we present a technique that addresses this question by linking the SAR-derived velocity measurements with the glacier elevation change and the specific mass balance (i.e. mass balance per unit area). The technique computes the speckle offset tracking results from the north, east and vertical flow displacement time series, with the vertical component further split into a Surface Parallel Flow (SPF) advection component due to the motion along a glacier surface slope and a non-Surface Parallel Flow (nSPF). The latter links the glacier surface elevation change with the specific mass balance and strain rates. We apply this technique to ascending and descending Sentinel-1 data to derive the four-dimensional flow displacement time series for glaciers in southeast Alaska during 2016–2019. Time series extracted for a few characteristic regions demonstrate remarkable temporal variability in flow velocities. The seasonal signal observed in the nSPF component is modeled using the Positive Degree Day model. This method can be used for computing either mass balance or glacier surface elevation change if one of these two parameters is known from external observations.

诸如合成孔径雷达（SAR）之类的现代遥感技术可以测量冰川流动的方向和强度。然而，这些测量结果揭示了冰川对气候变暖的调节仍存在问题。在这里，我们提出了一种技术，通过将SAR得出的速度测量值与冰川高程变化和特定质量平衡（即每单位面积的质量平衡）联系起来，解决了这个问题。该技术从北流，东流和垂直流位移时间序列计算散斑偏移跟踪结果，由于沿冰川表面坡度和非表面运动，垂直分量进一步分为表面平行流（SPF）对流分量并行流（nSPF）。后者将冰川表面高程的变化与特定的质量平衡和应变率联系在一起。我们将此技术应用于Sentinel-1数据的上升和下降，以得出2016-2019年阿拉斯加东南部冰川的三维流位移时间序列。从几个特征区域提取的时间序列显示出流速的显着时间变化。使用正度日模型对在nSPF组件中观察到的季节性信号进行建模。如果从外部观察中知道这两个参数之一，则该方法可用于计算质量平衡或冰川表面高程变化。从几个特征区域提取的时间序列显示出流速的显着时间变化。使用正度日模型对在nSPF组件中观察到的季节性信号进行建模。如果从外部观察中知道这两个参数之一，则该方法可用于计算质量平衡或冰川表面高程变化。从几个特征区域提取的时间序列显示出流速的显着时间变化。使用正度日模型对在nSPF组件中观察到的季节性信号进行建模。如果从外部观察中知道这两个参数之一，则该方法可用于计算质量平衡或冰川表面高程变化。