Study Of Near-surface Freeze-thaw State Monitoring Based On Multi-source Remote Sensing Data

The 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.Based on the actual needs of relevant researches,the discrimination of the freeze-thaw state of the surface with high spatial-temporal resolution is the key point and hot issue of the surface freeze-thaw study..In order to achieve the above objectives,this paper mainly studies the following aspects:(1)The development of the discriminant function algorithm(DFA).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.(2)The linear regression model of MODIS surface temperature data and freeze-thaw coefficient FTI was established on the global scale of long time series.We studied the linear regression relationship between determination coefficient(FTI)and MODIS land surface temperature(LST)data in different pixels on a global scale,itwas shown that there exists a strong negative correlation between FTI and MODIS LST except the some regions of Africa and Brazil which have no effects on the freeze-thaw discrimination.The proportion of correlation coefficient between-0.8 and-1 within the above 30 degrees north latitude area on ascending and descending orbit can reach 95.6% and 93.7% respectively.It's verified from the side that there may occur large deviation when establishing linear regression model ignoring the difference among different regions.This study provides a theoretical basis for the establishment for a long time series freeze-thaw datasets with high spatial resolution.(3)The establishment of freeze-thaw datasets for a long time series with different spatial resolution.We can acquire two different long time series freeze-thaw datasets calculated by the new discriminant function algorithm and the linear regression model between FTI and MODIS LST respectively.According to the study of subsistent problems for discriminating surface freeze-thaw state,the paper not only develops a new discriminant function algorithm but also improves the freeze-thaw discriminant fidelity on a global scale based on the fusion of FTI and MODIS LST,which can provide more effective information support for the earth's climate change,hydrological process,land surface process and other related researches.