Global High Resolution (0.05 degree) Mapping of Soil Freeze-Thaw Dynamics via Optimized Microwave-Optical Fusion and DFA Algorithm Tianjie Zhao, Defeng Feng, Pei Yu, Ziqian Zhang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences
Abstract
Near-surface soil freeze-thaw (F/T) cycles critically regulate global hydrological, ecological, and climatic processes. While passive microwave remote sensing enables all-weather F/T monitoring, its coarse spatial resolution limits fine-scale applications, and performance disparities among microwave indicators remain unquantified. To address these gaps, this study first systematically evaluated eight key microwave indicators-including liquid water indices (NPR, MPR, QE, NFDI) and surface temperature indices (TbV6.9-36.5)-and their 16 combinations across six soil networks on the Tibetan and Inner Mongolian Plateaus. Results identified the quasi-emissivity (QE) and TbV36.5 as optimal universal indicators, with their synergy achieving robust performance. The discriminant function algorithm (DFA) significantly outperformed threshold-based methods, particularly under snow/vegetation interference. Building on this optimized detection framework, we developed a novel downscaling approach integrating passive microwave and optical data to generate the first long-term (2002-2023), high-resolution (0.05 degree), daily seamless global F/T dataset. Validation against in situ networks confirmed an overall accuracy of 83.78%-matching coarse-resolution fidelity while enhancing spatial detail. This dataset revealed new global dynamics: regions north of 45 degreeN exhibit 187.8 +- 12.7 mean annual frost days, with 14.35% showing significant declines in frozen persistence- freeze onset occurs on day 240.3 +- 7.2 annually, delayed across 9.10% of areas. By elucidating microwave indicator synergies, advancing algorithm robustness (DFA), and delivering a high-resolution global F/T product, this research enables refined quantification of land-atmosphere interactions critical for hydrological, erosion, and climate models. The dataset is openly available at https://doi.org/10.11888/Cryos.tpdc.301551.
