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Snow depth inversion based on D-InSAR method

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

  • Email：2587625936@qq.com
  • Introduction：刘洋，1987年生，男，博士研究生，研究方向为积雪遥感研究。E-mail：2587625936@qq.com
Yang LIU1,  
  • Affiliation：

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

Lanhai LI1,  
  • Affiliation：

    College of Resource and Environment Science, Xinjiang University, Urumqi 830046, China

Jinming YANG2,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

  • Email：chenxi@ms.xjb.ac.cn
  • Introduction：陈曦，1964年生，男，研究员，研究方向为干旱区生态安全遥感监测、水文空间模拟以及生态用水的调度与调控方法研究。E-mail：chenxi@ms.xjb.ac.cn
Xi CHEN1*,  
  • Affiliation：

    Key Laboratory of Tibetan Environment Changes amd Land Surface Processes, Institute of Tibetan Plateau Research,, Chinese Academy of Sciences, Beijing 100101, China

Run ZHANG3

Resümee

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. This study adopts the Sentinel-1 C-band of the European Space Agency using the two-pass method of differential interferometry to conduct an inversion study of the snow depth distribution in typical areas of Bayanbulak Basin in the Middle Tianshan Mountains of Xinjiang, China. Based on Sentinel-1 SAR image, the image of day October 31, 2016 is selected as the master image and the image of day December 18, 2016 is used as the slave image to form the image pair. After the interferogram is formed, the orbit phases, terrain, ground effect, and noise effect are removed. The phase unwrapping of the remaining phase aims to obtain the distribution of snow depth with the spatial resolution of 13.89 m on day December 18, 2016 by relying on the relationship between snow depth and snow phase in the typical Bayanbulak region. The study demonstrates the following: (1) After proper preprocessing of Sentinel-1 data, snow depth distribution inversion is achieved by utilizing the InSAR-based two-pass method. However, owing to the difference of image-pair coherence and snow accumulation conditions, a relatively accurate inversion result of snow depth is available when the snow depth is larger than 10 cm (R=0.65, RMSE=4.52 cm, and average relative error is 22.42%). The estimated snow depth is slightly lower than the actual depth. When the snow depth is less than 10 cm, the inversion result is not accurate: it is larger than the actual depth, and the average relative error is higher than 34.52%. (2) The inversion accuracy of snow depth is also significantly influenced by the height and actual snow depth. Moreover, the inversion result of snow depth is influenced by coherence losses. This study demonstrates that the InSAR method is more promising in obtaining and estimating snow depth compared with optical technology and passive microwave remote sensing.

Schlüsselwort

snow depth;Sentinel-1;D-InSAR;error analysis;coherence

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