# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 4 Challenges and Opportunities
### Opportunities  
Progress in remote sensing and information technologies such as the Advanced Topographic Laser Altimeter System (ATLAS) on-board ICESat-2 [289; 290]; near-nadir SAR [291; 292]; C-, L- and P-band SAR [293; 294; 295]; small satellites [181; 296]; and cloud computation and artificial intelligence [297] will likely provide opportunities to overcome current challenges.  
It is anticipated that SAR will play a greater role in cold land studies. The European Space Agency's Sentinel-1A/B satellites and the Canadian Space Agency's RADARSAT Constellation Mission (RCM) launched on 12 June 2019 [293] can provide multi-resolution (1-3 m to 100-m) C-band SAR and InSAR measurements every 1-6 days globally and at low cost for monitoring glacier movement, riverice conditions, water body dynamics, snow, and vegetation parameters [298]. The next generation NISAR (National Aeronautics and Space Administration - Indian Space Research Organisation SAR) mission is expected to improve capabilities for dynamic mapping of global surface water at resolutions from 3-10 m [294] and provide new opportunities to derive SWE in complex terrain from single- and repeat-pass radar (L-band) interferometry [16]. The surface water ocean topography (SWOT) mission has a projected launch in 2021 and will enable estimation of the changing volumes of global water bodies whose surface area exceeds 250 m by 250 m at sub-monthly time scales [178; 291]. The upcoming BIOMASS mission (launch 2021) offers opportunities for mapping vegetation biomass with enhanced P-band penetration capability [299], though National restrictions on P-band transmissions will eliminate BIOMASS coverage over North America [300]. Another future direction for analyzing environmental changes in remote areas under harsh climatic conditions could also involve relatively low-cost multi-temporal remote sensing surveys. Unmanned aerial vehicle (UAV)-based remote sensing (e.g., thermal imaging, structure from motion photogrammetric techniques), given relatively low application barriers (e.g., low cost for repeat seasonal or yearly surveys), is expected to become a strategic tool for better monitoring and understanding of the geomorphologic dynamics of cold regions, and related impacts on ecosystems and infrastructures.  
Improved retrieval algorithms will better leverage the remote sensing observations. For example, entropy-based multi-scale image matching and optical flow techniques potentially help overcome the limitations of conventional image matching approaches for monitoring fast moving glaciers [301; 302]. Backward reconstruction techniques using a temperature-index or energy-balance model provide another promising tool to estimate SWE through the melt season in mountainous regions and elsewhere [303; 304]. There is also great potential for improving snow depth and SWE retrievals using LIDAR measurements [305; 306], microwave interferometry [147; 307], and GNSS techniques [308], which largely avoid issues related to snow microstructure. Considering the complementarity of LIDAR, optical-IR, and microwave remote sensing, the use of data integration techniques such as deep-learning approaches [208] from a collection of multi-sensor observations may enable enhanced delineations of snow, water, soil, and vegetation elements, and accurate mapping of environmental variables at high spatial-temporal resolutions.