“…Several studies have shown the potential of statistical modeling to estimate permafrost distribution (Aalto, Karjalainen, et al., 2018; Boeckli et al., 2012; Etzelmüller et al., 2001; Gruber & Hoelzle, 2001) and to capture the multivariate nature of periglacial processes at landscape‐scale by documenting how their distribution and dynamics are influenced by environmental factors (Hjort & Luoto, 2011, 2013; Hjort et al., 2014; Karjalainen et al., 2019; Rudy et al., 2017). By statistically identifying the variables influencing the ground thermal regime and periglacial processes, it becomes possible to better explain their current distribution and predict their future evolution based on climate change scenarios (Aalto et al., 2017; Blois et al., 2013; Hjort et al., 2018; Karjalainen et al., 2020). However, previous research has been primarily based on in‐situ measurements (e.g., ground temperature from boreholes) or mapped landforms (e.g., inventory of solifluction lobes and palsas) and few studies have integrated advanced remote sensing data documenting periglacial activity, such as InSAR‐based ground velocity.…”