# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Ice
#### 2.1.2 Lake Ice Cover
The emissivity of ice, and therefore Tb, further increases during ice formation, as the influence of radiometrically cold water under the ice cover decreases with ice thickening [84]. [PERSON] et al. (2012) found AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) 18.7 GHz (H- pol) Tb data (interpolated onto a 10-km grid) to be the most suitable for estimating ice dates (freeze-onset, ice-on dates, melt-onset, and ice-off dates), as well as the duration of ice cover and ice- free seasons using a thresholding approach. [PERSON] et al. [17] further demonstrated that ice dates could be derived from AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer 2) 36.5 GHz (H-pol) Tb (re-gridded at 5-km) data using a moving \(t\)-test algorithm. Derived ice dates compared favorably with those obtained from ground-based observations and other satellite products such as IMS. Threshold-based and semi-automated (region-based segmentation followed by manual labelling of ice/open water) approaches have also been developed to generate ice cover extent and phenology products using SAR data. [PERSON] et al. [85] evaluated the semi-automated segmentation algorithm \"glocal\" Iterative Region Growing with Semantics (IRGS) [86] for lake ice classification using dual polarized (HH and HV) RADARSAT-2 imagery acquired over Lake Erie. Their analysis showed that the algorithm could provide reliable discrimination between ice and open water with high overall classification accuracy (90.4%) when compared to Great Lakes image analysis charts from the Canadian Ice Service. [PERSON] et al. [87] developed a threshold-based approach for estimating ice phenology events for mid-latitude lakes in Central Ontario by tracking the temporal evolution in backscatter from HH-polarization RADARSAT-2 imagery (2008-2017). The authors reported mean absolute errors of 2.5-10 days for freeze events and 1.5-7.1 days for water clear-of-ice when compared to MODIS imagery. The method was also successful in detecting multiple freeze (high backscatter) and melt (low backscatter) events throughout the ice season. By combining acquisitions from ENVISAT Advanced SAR (ASAR) wide swath and RADARSAT-2 ScanSAR data, [PERSON] et al. [88] showed the advantage of more frequent sampling (i.e., every 2-5 days over the 2005-2011 study period), but also the need for sensor incidence angle correction for more precise ice phenology detection from backscatter thresholds over Alaskan North Slope lakes. Ice thickness--Field measurements of ice thickness are spatially and temporally sparse in cold regions. Recent investigations have developed approaches to estimate ice thickness from passive microwave, active microwave (altimetry and SAR) and thermal remote sensing data. [PERSON] et al. [84] showed that the temporal evolution of Tb measurements from AMSR-E at 10.7 GHz and 18.7 GHz frequency (V polarization) during the ice growth season on Great Bear Lake (GBL) and Great Slave Lake (GSL), Canada, is strongly related to ice thickness. The authors proposed simple linear regression equations to estimate ice thickness for the lakes using 18.7 GHz V-pol data (2002-2009), while the estimated ice thicknesses compared favorably with in situ measurements (Mean Bias Error, MBE, 6 cm; Root Mean Square Error, RMSE, 19 cm). [PERSON] et al. [89] explored waveforms from CryoSat-2 Ku-band radar altimetry to estimate ice thickness also on GBL and GSL. Their study obtained ice thickness estimates with RMSE of 33 cm when compared to in-situ measurements obtained at GSL. [PERSON] et al. [90] evaluated RADARSAT-2 data for estimating lake ice thickness in Central Ontario, Canada. They reported RMSE values of 11.7 cm and attributed the uncertainty to unexplored questions about scattering mechanisms and the interaction of the radar signal with lake ice having complex structure within the ice layer and at the ice-water interface.