Remote Sensing of Environmental Changes in Cold Regions Ⅱ

Dear Colleagues,

Cold regions including the northern high latitudes, polar regions, and Tibetan Plateau are highly sensitive to global warming, and are undergoing dramatic changes in ecological, hydrologic, and climatic processes. Yet, studies of these regions are restricted by their limited in-situ measurements, and remote sensing provides key supports for monitoring and interpreting the on-going environmental changes.

This Special Issue, “Remote Sensing of Environmental Changes in Cold Regions II”, will host papers focusing on, but not limited to, the following topics:

• Long-term monitoring of the dynamic changes of glacier, snow cover, permafrost, water bodies, vegetation, and carbon emissions using multi-year and multi-source remote sensing data;
• Applying emerging remote sensing techniques (e.g., SmallSat, UAV, lidar, GNSS, and near-nadir SAR imaging techniques) for the enhanced mapping of cold land biophysical/geophysical parameters;
• Investigating the use of current and future satellite missions such as SMAP, SMOS, SWOT, OCO3, and NISAR in monitoring eco-hydrological and cryospheric parameters;
• Interpreting remote sensing data based on cloud computation and machine learning techniques for cold region studies;
• Monitoring, modelling, understanding, and forecasting the interactions among earth system cycles under changing climate.

Related References

• Kim, Y.; Kimball, J.S.; Glassy, J.; Du, J. An extended global earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing. Earth System Science Data, 2017
• Du, J.; Kimball, J.S.; Duguay, C.; Kim, Y.; Watts, J.D. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. The Cryosphere, 2017.
• Cooley, S.W., Smith, L.C., Stepan, L. and Mascaro, J., 2017. Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery. Remote Sensing, 9(12), p.1306.
• Yang, J., Jiang, L., Luojus, K., Pan, J., Lemmetyinen, J., Takala, M. and Wu, S., 2020. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach. The Cryosphere, 14(6), pp.1763-1778.
• Endsley, K.A., Kimball, J.S., Reichle, R.H. and Watts, J.D., 2020. Satellite monitoring of global surface soil organic carbon dynamics using the SMAP Level 4 Carbon product. Journal of Geophysical Research: Biogeosciences, p.e2020JG006100.
• Yao, P., Lu, H., Shi, J., Zhao, T., Yang, K., Cosh, M.H., Gianotti, D.J.S. and Entekhabi, D., 2021. A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019). Scientific Data, 8(1), pp.1-16.

Dr. Youngwook Kim
Dr. Jinyang Du
Dr. Jennifer D. Watts
Dr. Hui Lu
Dr. Lingmei Jiang
Prof. Dr. Paolo Tarolli
Guest Editors

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