Estimation of snow accumulation over frozen Arctic lakes using repeat ICESat laser altimetry observations – A case study in northern Alaska

Song Shu, Hongxing Liu, Frédéric Frappart, Yan Huang, Shujie Wang, Kenneth M. Hinkel, Richard A. Beck, Bailang Yu, Benjamin M. Jones, Christopher D. Arp, Lei Wang, Zhaoxia Ye

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The spatial distribution and temporal dynamics of snow in Arctic regions have a direct impact on the regional energy balance and hydrologic cycle. However, our knowledge of snow cover in Arctic regions is very limited due to the sparseness of in situ measurements. This study presents a new method to derive snow accumulation information for Arctic regions through tracking the surface elevation changes of numerous frozen arctic lakes measured by Ice, Cloud, and land Elevation satellite (ICESat) repeat altimetry observations. The original ICESat elevation product for continental surface was generated by tracking the centroids of the emitted and the returned laser waveforms. This product contains many biased measurements over frozen arctic lake surfaces due to the scattering effects induced by thin clouds and blowing snow. This study derives an operational approach that produces more reliable altimetry observations by converting the elevation measurements from the centroid scheme to the max-amplitude-peak scheme. Time-variable biases exist between the repeat elevation measurements acquired in different ICESat campaigns. The correction of these inter-campaign biases in this study significantly improves the quantification of the surface elevation change, thus enabling more consistent subsequent snow accumulation estimates. Besides snow fall, lake ice growth also contributes to the surface elevation change. We developed a method to measure and remove this contribution from the total lake surface elevation change, which leads to more accurate estimates of snow accumulation on frozen surfaces of 277 lakes in Arctic regions of northern Alaska. The results were validated using in situ snow depth observations from terrestrial stations on the Arctic coastal plain of Alaska. After the correction of the ICESat inter-campaign biases and the removal of contribution by lake surface phase transformation, the snow accumulation derived from the repeat ICESat elevation measurements are highly correlated with in situ snow depth observations with a Pearson's correlation coefficient r of 0.88. In comparison with ground-based measurements, the root mean square error (RMSE) of our snow accumulation estimates is approximately 5 cm. Our method makes it possible to provide much denser snow accumulation information as compared to the existing in situ observations for the Circum-Arctic coastal regions and also the Qinghai-Tibet Plateau where seasonal frozen lakes are abundant.

Original languageEnglish (US)
Pages (from-to)529-543
Number of pages15
JournalRemote Sensing of Environment
StatePublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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