High-resolution surface freeze/thaw (F/T) information is valuable for hydrological, frost creep and gelifluction/solifluction, and climate prediction studies. Currently, large-scale, high-resolution F/T detection is restricted by low spatial resolution of passive microwave remote sensing sensors or low temporal resolution of synthetic aperture radar (SAR) data. In this study, we propose a new method for detecting daily land surface F/T state at 1 km spatial resolution by combining the Sentinel-1 radar and the Advanced Microwave Scanning Radiometer 2 (AMSR2) with leaf area index (LAI) data. A non-linear relationship is established between the 1 km F/T index from Sentinel-1 with 1 km F/T index from AMSR2 (FTI) and 1 km LAI data. The 1 km FTI is a disaggregation of the 25 km FTI obtained from AMSR2. This non-linear relationship is then applied to daily 1 km FTI and LAI data to predict the 1 km daily F/T index, based on which the F/T status is detected with grid-cell-based F/T thresholds. The overall accuracy of this daily 1 km F/T is more than 88.1% when evaluated with the in situ 5 cm soil temperature over China and Canada. This study is valuable for detecting daily, high-resolution F/T status and is helpful for studies related to disaster and climate prediction.