The Feasibility Study of Using Microwave Emissivity in Detecting Freeze and Thaw States

Abstract:

This study aims to examine the potential use of microwave emissivity (in lieu of brightness temperatures observations) from satellite observations in freeze/thaw prediction studies. Monitoring freeze-thaw transitions in high latitude regions are critical to enhancing our knowledge about the prediction of biogeochemical transitions, carbon dynamics, climate change, and impacts on boreal-arctic ecosystems. Current, freeze/thaw products mainly use direct measurements of microwave brightness temperatures. Since land surface emissivity, which will not be affected by temperature and atmosphere interferance, depends primarily on the surface characteristics, it would contains valuable information about the surface, especially regarding freeze and thaw states. The surface characteristics in terms of microwave emission changes whenever water undergoes phase changes at constant temperature. Transition between freeze and thaw states depends on the amount of heat energy the surface receives or releases and on the corresponding change in seasonal and diurnal temperature. Emissivity estimates from various microwave sensors (such as SSM/I, AMSR2, WindSat) on board available satellites can help to construct diurnal estimates in order to accurately predicting the exact time of the freeze-thaw transition for each land cover type and region. The diurnal cycle of the microwave brightness temperature will be constructed over the globe for different frequencies/polarizations using a multi-sensor / multi-platform data fusion. Emissivities are retrieved and freeze/ thaw state investigation is performed by examining the different thresholds on emissivity estimates that may define these states. The results reveal that microwave emissivity potentially can provide a better understanding about freeze/thaw states since they are not affected by temperature and atmosphere and represent the state of the surface in terms of moisture and their states. Moreover, the data fusion helps to provide more accurate timley estimates of the surface state especially in transition season. Results of this study improve the temporal frequency and the accuracy of the estimates which are necessary for many climate, environmental and hydrological studies.