Machine Learning Model Mapped Permafrost Distribution in Northeast China During 2000–2020

Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of the Xing’an permafrost are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geoenvironmental factors. Thus, this study introduces a new approach that corporates four key driving factors—biotic, climatic, physiographic, and anthropogenic—by integrating multi-source datasets and in-situ observations. Four machine learning (ML) models (Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and XGBoost (XGB)) are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models’ accuracy, ranked from highest to lowest, is as follows: RF (Area Under the Curve (AUC)=0.88, and Accuracy=0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert non-linear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multi-source datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.