Exploring the merging potential of high temporal resolution and high spatial resolution microwave remote sensing data

Microwave remote sensing can provide effective monitoring of landscape FT dynamics. Its sensitivity to surface permittivity, which is predominantly influenced by the phases of water, can be used to measure landscape freeze/thaw state information. The technique of Interferometric Synthetic Aperture Radar (InSAR) enables to map the ground movement through the use of Synthetic Aperture Radar (SAR). Compared to optical imagery, microwave data has advantages that it would not be affected by cloud cover, smoke or daylight and exhibits useful penetration depths of soil and vegetation.  
Both active and passive microwave remote sensing with different wavelengths have shown their principal capacity in many studies and have complementary advantages to each other. While many passive sensors (such as SMAP and SMOS) are providing observations with high temporal resolution and good worldwide coverage at the deca-kilometer scale, there are a series of active sensors providing observations with worse temporal resolution but much better spatial resolution at the scale from a few meters to a few deca-meters, for example, the Sentinel-1 mission from the European Space Agency (ESA) with 5 x 5 m spatial resolution and 6-12 days repeat cycle. Hence, the combined use of different microwave data can be expected further to promote the monitoring of permafrost-related phenomena and permafrost-dominated landscapes.
An assumption of near-linear relation between the measurements from the passive and active sensors has been used in NASA’s Soil Moisture Active Passive (SMAP) active-passive baseline algorithm for downscaling coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ 0). Recent research proved that a good linear relationship could be found at a global scale (Zeng et al., 2021). However, the relation is significantly affected by environmental factors, for example, the density of vegetation cover. 
Based on the findings, we attempt to explore the possibility of merging microwave remote-sensing data from different platforms in this work. We are committed to exploring suitable data sources for merging, as well as the possibility of taking environmental factors into consideration. The capacity and limitation of the merging process will be discussed.