물과 미래 (Water for future)

한국수자원학회 (Korea Water Resources Association)
권호 표지

기후변화 및 수자원 분야 연구를 위한 인공위성 원격탐사의 역할

  • 김석현 (경희대학교 사회기반시스템공학과)
  • 발행 : 2022.03.31
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초록

키워드

참고문헌

  1. De Groeve, T., Z. Kugler and G. R. Brakenridge, 2007. Near Real Time Flood Alerting for the Global Disaster Alert and Coordination System. In Proceedings of the 4th International ISCRAM Conference (B. Van de Walle, P. Burghardt, and C. Nieuwenhuis, eds., Delft, the Netherlands).
  2. Dorigo, W., S. Dietrich, F. Aires, L. Brocca, S. Carter, J.-F. Cretaux, D. Dunkerley, H. Enomoto, R. Forsberg and A. Guntner, 2021. Closing the water cycle from observations across scales: Where do we stand? Bulletin of the American Meteorological Society:1-95.
  3. Dorigo, W. A., A. Gruber, R. A. M. De Jeu, W. Wagner, T. Stacke, A. Loew, C. Albergel, L. Brocca, D. Chung, R. M. Parinussa and R. Kidd, 2015. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment 162:380-395. https://doi.org/10.1016/j.rse.2014.07.023
  4. Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, M. Drusch, S. Mecklenburg, P. van Oevelen, A. Robock and T. Jackson, 2011. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrology and Earth System Sciences 15:1675-1698. https://doi.org/10.5194/hess-15-1675-2011
  5. Entekhabi, D., E. G. Njoku, P. E. O'Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, S. D. Goodman, T. J. Jackson and J. Johnson, 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE 98:704-716. https://doi.org/10.1109/JPROC.2010.2043918
  6. Gruber, A., C. H. Su, S. Zwieback, W. Crowd, W. Dorigo and W. Wagner, 2016. Recent advances in (soil moisture) triple collocation analysis. Int J Appl Earth Obs 45:200-211.
  7. Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao and L. G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment 83:195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  8. Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, P. Xie and S.-H. Yoo, 2015. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 4:26.
  9. IPCC, 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekci, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
  10. Kim, S., H. Ajami and A. Sharma, 2020. Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia. Remote Sens-Basel 12:3051. https://doi.org/10.3390/rs12183051
  11. Kim, S., K. Balakrishnan, Y. Liu, F. Johnson and A. Sharma, 2017. Spatial Disaggregation of Coarse Soil Moisture Data by Using High-Resolution Remotely Sensed Vegetation Products. IEEE Geoscience and Remote Sensing Letters 14:1604-1608. https://doi.org/10.1109/LGRS.2017.2725945
  12. Kim, S., K. Paik, F. M. Johnson and A. Sharma, 2018. Building a Flood-Warning Framework for Ungauged Locations Using Low Resolution, Open-Access Remotely Sensed Surface Soil Moisture, Precipitation, Soil, and Topographic Information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11:375-387. https://doi.org/10.1109/jstars.2018.2790409
  13. Kim, S., H. T. Pham, Y. Y. Liu, L. Marshall and A. Sharma, 2021. Improving the Combination of Satellite Soil Moisture Data Sets by Considering Error Cross Correlation: A Comparison Between Triple Collocation (TC) and Extended Double Instrumental Variable (EIVD) Alternatives. IEEE Transactions on Geoscience and Remote Sensing 59:7285-7295. https://doi.org/10.1109/TGRS.2020.3032418
  14. Kim, S. and A. Sharma, 2019. The Role of Floodplain Topography in Deriving Basin Discharge Using Passive Microwave Remote Sensing. Water Resources Research 55:1707-1716. https://doi.org/10.1029/2018wr023627
  15. Kim, S., A. Sharma, Y. Y. Liu and S. I. Young, 2022a. Rethinking Satellite Data Merging: From Averaging to SNR Optimization. IEEE Transactions on Geoscience and Remote Sensing 60:4405215.
  16. Kim, S., A. Sharma, C. Wasko and R. Nathan, 2022b. Linking total precipitable water to precipitation extremes globally. Earths Future.
  17. Lenton, T. M., H. Held, E. Kriegler, J. W. Hall, W. Lucht, S. Rahmstorf and H. J. Schellnhuber, 2008. Tipping elements in the Earth's climate system. Proceedings of the national Academy of Sciences 105:1786-1793. https://doi.org/10.1073/pnas.0705414105
  18. Ma, Y., H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya and W. Jie, 2015. Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems 51:47-60. https://doi.org/10.1016/j.future.2014.10.029
  19. Pham, H. T., S. Kim, L. Marshall and F. Johnson, 2019. Using 3D robust smoothing to fill land surface temperature gaps at the continental scale. Int J Appl Earth Obs 82:101879.
  20. Yang, J., P. Gong, R. Fu, M. Zhang, J. Chen, S. Liang, B. Xu, J. Shi and R. Dickinson, 2013. The role of satellite remote sensing in climate change studies. Nature climate change 3:875-883. https://doi.org/10.1038/nclimate1908
  21. Yoon, H. N., L. Marshall, A. Sharma and S. Kim, 2022. Bayesian model calibration using surrogate streamflow in ungauged catchments. Water Resources Research 58:e2021WR031287.
  22. Zhang, Q., Q. Yuan, J. Li, Y. Wang, F. Sun and L. Zhang, 2021. Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013-2019. Earth Syst Sci Data 13:1385-1401. https://doi.org/10.5194/essd-13-1385-2021
  23. Zhang, R., S. Kim and A. Sharma, 2019. A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sensing of Environment 223:82-94. https://doi.org/10.1016/j.rse.2019.01.015