Soil moisture, as an important variable affecting water–heat exchange between land and atmosphere, is an important feedback to climate change. Soil moisture is of great concern in Northern China, where arable land is extensive, but water resources are distributed unevenly and extremely sensitive to climate change. Using measured soil moisture data collected by the China Meteorological Administration from 164 stations during 1980–2021, we explored the drivers of soil moisture variation by analyzing its spatiotemporal variability using linear regression, partial correlation analysis, and geostatistical methods. The results indicated that (1) soil moisture increased from northwest to southeast in Northern China, with the lowest soil moisture in the IM; (2) the overall trend of soil moisture in most regions decreased, especially in the arid northwest and northeast China. However, soil moisture in some regions began to increase gradually in recent years, such as in northwestern Xinjiang and the central-eastern part of IM; and (3) soil moisture in the whole region was negatively correlated with temperature and sunshine duration and positively correlated with precipitation and relative humidity. The results of the study can provide valuable guidance for timely agricultural irrigation and the adjustment of cropping structures, thereby ensuring agricultural production and food security.

Scientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-layer minimum temperatures in plastic greenhouses and selection of optimal forecasting methods are important for safe strawberry production. However, the identification of important drivers of minimum temperatures in plastic greenhouses and the prediction of potential drivers of use are still unclear. In this study, we used Classification and Regression Tree (CART) to identify the importance of the potential factors affecting the minimum temperatures at different depths and different heights of plastic greenhouses. Random forest (RF), back-propagation (BP), and multiple linear regression (MLR) were used to establish the minimum temperature prediction models for plastic greenhouses at different depths and heights, respectively. The results showed that T_smin10, T_smin25, T_amin150, T_amin320, and T_amin150 were the most important variables explaining the changes in minimum temperatures at heights T_smin25, T_smin10, T_smin2, T_amin150, and T_amin320 respectively. RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R² than MLR. The superiority of RF and BP in predicting minimum temperatures is related to their ability to deal with non-linear and hierarchical relationships between minimum temperatures and predictors. The low-temperature frost protection and fine management of strawberries in the Changfeng area can be related to the prediction method of minimum temperature in plastic greenhouses constructed in this study.