Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study

Abstract

This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test (MTT) algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Strong agreement is observed between ice phenology records derived from CYGNSS and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Differences during freeze-up (i.e., the period starting with the first appearance of ice on the lake until the lake becomes fully ice covered) ranged from 3 to 21 days with a mean bias error (MBE) and mean absolute error (MAE) of 10 days, while those during breakup (i.e., the period beginning with the first pixel of open water and ending when the whole lake becomes ice-free) ranged from 3 to 18 days (MBE and MAE:  6 and 7 days, respectively). Observations during the breakup period revealed the sensitivity of GNSS reflected signals to the onset of surface (snow and ice) melt before the appearance of open water conditions as determined from MODIS. While the CYGNSS constellation is limited to the coverage of lakes between 38° S and 38° N, the approach presented herein will be applicable to data from other GNSS-R missions that provide opportunities for the monitoring of ice phenology from large lakes globally (e.g., Spire constellation of satellites).