Improving Freeze/Thaw Onsets Retrieval by Combining SMAP and AMSR2 Based on Xgboost: a Case Study in Alaska

Abstract

Passive microwave remote sensing can effectively capture the near-surface soil freeze/thaw onsets. Accurately understanding the transition of permafrost freeze/thaw state is helpful for us to respond to climate change in time. In order to improve the retrieval accuracy of freeze/thaw onsets, we propose an XGBoost modeling method that combines SMAP and AMSR2 for freeze/thaw onsets detection. We conducted experiments using data covering Alaska from 2015 to 2020 to demonstrate the effectiveness of our method. The proposed model was applied to the whole study area to obtain the spatial and temporal distribution of freezing periods. During the study period, the shortening of the freezing period has been most evident in 2018–2019. The variation of the freezing period is related to climate anomalies.