# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Frozen Soil  
#### 2.3.1 Landscape Freeze/Thaw States  
The landscape FT status is closely linked to ecosystem carbon, water and energy exchanges, snow melt dynamics, and permafrost extent and stability [21; 34; 149; 150]. Global FT observational data records spanning the modern satellite era have been used to document environmental trends from global warming, including earlier and longer non-frozen seasons as a driver of northern vegetation greening, increased trends in damaging frost events in early spring [9], degrading permafrost, and an earlier spring flood pulse across the pan-Arctic [151]. By availing of the high sensitivity of microwaves  
Figure 1: Components of snow emissions and scattering observed by space-borne microwave radiometer and radar observations. By neglecting atmosphere scattering and upward emission, the satellite signals mainly consist of contributions from the air–snow surface (as), snow pack (v), underlying soil (g), and snow–soil interactions (gv). to significant dielectric shifts between solid and liquid water phase transitions, the FT signals can be captured by classifying Tb or radar backscatter coefficient (\(\sigma^{0}\)) changes relative to frozen or non-frozen reference conditions [35]. FT algorithms applied to both active and passive microwave observations include threshold-based methods [35; 38; 152], change-detection approaches [48; 153], and multi-channel data fusion or machine-learning methods [154; 155; 156; 157; 158]. The threshold-based methods determine FT conditions by comparing the satellite observations with reference Tb or \(\sigma^{0}\) values representative of seasonal frozen and non-frozen conditions. Such approaches are robust and relatively easy to implement for operational FT retrievals. More sophisticated multi-channel combinations, decision tree, and probabilistic model methods can effectively distinguish FT conditions from precipitation events in sparsely vegetated and drier climate zones. As a recent development, data fusion, and machine learning methods show promise in providing potentially enhanced FT retrievals by exploiting massive archives of satellite observations and ancillary data [157; 158; 159]. Multiple global FT data records have been developed using observations from satellite microwave radiometers and scatterometers, including SMMR and SSM/I[S] [160], AMSR-E/2 [161], Aquarius [162], SMOS (Soil Moisture and Ocean Salinity) [46], SMAP [163; 164], and ASCAT [165]. Long-term (\(>\)39-year) global daily FT data records have been developed using similar overlapping 37 GHz Tb retrievals from SMMR and SSM/I[S] sensors with moderate (\(\sim\)25 km) spatial resolution [160]. Finer (\(\sim\)12 km) resolution FT data have also been developed using calibrated 36.5 GHz orbital swath Tb records from the AMSR-E/2 sensors [161; 166]. The SMAP mission provides an operational FT data record derived from L-band (1.4 GHz) Tb retrievals with global coverage, 1-3-day temporal fidelity, and 9-km and 36-km resolution gridding [163; 164; 167]. Example maps of the observed non-frozen days in 2017 are shown (Figure 2) from three different FT data products and operational satellite sensors, including SMAP [167], SSM/I [160], and AMSR2 [161]. The SSM/I and AMSR2 FT records are derived from vertically polarized Tb retrievals and a modified single channel algorithm. The SMAP FT record is derived using a dual algorithm approach including a normalized polarization ratio (NPR) of vertically and horizontally polarized Tb differences from NPR reference states, and a single channel algorithm where conditions are unfavorable for the NPR. All of these records show similar FT regional patterns, including generally fewer frost days at lower latitudes and altitudes, and in coastal areas relative to higher latitude, alpine, and inland areas.