Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data

Abstract

ABSTRACTThe near-surface soil freeze–thaw (FT) transition is an important factor affecting land-atmosphere exchanges, hydrology and carbon cycles. Thus, effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research. Several approaches have been developed to detect the soil FT state from satellite observations. The discriminant function algorithm (DFA) uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced (AMSR-E) passive microwave satellite observations. Although it is well validated, it was shown to be insufficiently robust for all land conditions. In this study, we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection. We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference. Considering the distinction between ascending and descending orbits, two different sets of parameters were acquired for each frequency pair. The validation results indicate that the overall discriminant accuracy of the new function can reach 90%. We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product, and a reasonable consistency between them was found.