Freeze/thaw onset detection combining SMAP and ASCAT data over Alaska: A machine learning approach

Abstract

In the context of global warming, permafrost degrades gradually. To cope with the instability of the cryosphere, it is very important to strengthen the monitoring of the seasonal freeze–thaw cycle. At present, active and passive microwave remote sensing data are widely used in freeze/thaw (F/T) onset detection. There is some potential to improve accuracy through the combination of active and passive microwave data. Compared with the traditional method for combination, the machine learning algorithm has a stronger nonlinear expression ability. Therefore, it is advisable to use machine learning to combine multi-source data for freeze/thaw onset detection. In this study, the temporal change detection method is applied to SMAP data and ASCAT data respectively for preliminary detection. Then the Random Forest algorithm (RF) is used to combine the preliminary results of active and passive microwave data with site observation to estimate the freeze/thaw onsets more accurately. The method is validated with data obtained in Alaska from 2015 to 2019. The accuracy evaluation shows that the proposed method can effectively improve the accuracy of freeze/thaw onset detection. The predicted distribution of the freeze/thaw cycle indicates that the variation of the freeze–thaw cycle is closely related to latitude. In general, the proposed method based on machine learning is promising in the research of freeze–thaw state monitoring.