DOI QR코드

DOI QR Code

Downscaling of AMSR2 Sea Ice Concentration Using a Weighting Scheme Derived from MODIS Sea Ice Cover Product

MODIS 해빙피복 기반의 가중치체계를 이용한 AMSR2 해빙면적비의 다운스케일링

  • Ahn, Jihye (Department of Spatial Information Engineering, Pukyong National University) ;
  • Hong, Sungwook (Satellite Analysis Division, National Meteorological Satellite Center) ;
  • Cho, Jaeil (Geospatial Information Research Division, Korea Research Institute for Human Settlements) ;
  • Lee, Yang-Won (Department of Spatial Information Engineering, Pukyong National University)
  • 안지혜 (부경대학교 공간정보시스템공학과) ;
  • 홍성욱 (국가기상위성센터 위성분석과) ;
  • 조재일 (국토연구원 국토정보연구본부) ;
  • 이양원 (부경대학교 공간정보시스템공학과)
  • Received : 2014.09.23
  • Accepted : 2014.10.23
  • Published : 2014.10.31

Abstract

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

해빙은 일반적으로 지구기후 변화 과정을 이해할 수 있는 중요한 요인으로 인식되고 있으며, 기후변화 분석 및 예측을 위한 지구시스템 모델의 기반이 되는 중요한 인자로 대표되고 있다. 수 km 급의 작은 규모로 발생하는 해빙의 변화를 지속적으로 파악하기 위해서는 현재의 제한된 해빙자료로부터 보다 정확한 격자자료를 생산할 것이 요구된다. 본 연구에서는 Advanced Microwave Scanning Radiometer 2(AMSR2)의 월간 해빙면적비(Sea Ice Concentration: SIC) 자료와 상관성이 높은 Moderate Resolution Imaging Spectroradiometer(MODIS) 기반의 월간 해빙일수비율(sea ice days ratio)를 지점별 가중치로 이용하는 상세화 기법을 고안하여 10 km 공간해상도의 SIC 자료를 1 km 공간해상도로 상세화하였다. 오호츠크 해역의 분석 결과, 기존의 공간해상도 10 km 자료와 상세화한 1 km 자료에서 해빙면적은 동일하였으며, 월별 SIC 평균과 표준편차 역시 거의 동일한 값의 분포를 나타냈다. 또한 EOF 분석을 통해 기후모델의 SIC 재분석자료 및 AMSR2 상세화 전후 자료에서 공간적, 시간적 변동성의 주성분이 매우 유사한 경향을 가지는 것으로 나타났다. 본 연구에서 제시한 상세화 기법은 다른 백분율 등으로 표현되는 연속형 비율자료의 상세화에 적용 가능할 것으로 사료되며, 보다 세밀한 해상도의 SIC 자료를 제공함으로써 작은 규모로 발생하는 해빙변화 감시에 기여할 가능성을 보여준다.

Keywords

References

  1. Ahn, J., S. Hong, J. Cho, Y. Lee, and H. Lee, 2014. Statistical modeling of sea ice concentration using satellite imagery and climate reanalysis data in the Barents and Kara Seas, 1979-2012, Remote Sensing, 6(6): 5520-5540. https://doi.org/10.3390/rs6065520
  2. Bjornsson, H. and S.A. Venegas, 1997. A manual for EOF and SVD analyses of climatic data, McGill University.
  3. Budgell, W.P., 2005. Numerical simulation of ice-ocean variability in the Barents Sea region towards dynamical downscaling, Ocean Dynamics, 55(3-4): 370-387. https://doi.org/10.1007/s10236-005-0008-3
  4. Chauhan, N.S., S. Miller, and P. Ardanuy, 2003. Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach, International Journal of Remote Sensing, 24(22): 4599-4622. https://doi.org/10.1080/0143116031000156837
  5. Chen, D. and X. Li, 2004. Scale dependent relationship between maximum ice extent in the Baltic Sea and atmospheric circulation, Global and Planetary Change, 41(3-4): 275-283. https://doi.org/10.1016/j.gloplacha.2004.01.012
  6. Cho, H., S. Hwang, Y. Cho, and M. Choi, 2013. Analysis of Spatial Precipitation Field Using Downscaling on the Korean Peninsula, Journal of Korea Water Resources Association, 46(11): 1129-1140 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2013.46.11.1129
  7. Choi, M. and Y. Hur, 2012. A microwaveoptical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products, Remote Sensing of Environment, 124: 259-269. https://doi.org/10.1016/j.rse.2012.05.009
  8. Comiso, J.C. and K. Cho, 2013. Chapter 6: Description of GCOM-W1 AMSR2 sea ice concentration algorithm(Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 algorithm), Japan Aerospace Exploration Agency Earth Observation Research Center.
  9. Duan, Z. and W.G.M. Bastiaanssen, 2013. First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure, Remote Sensing of Environment, 131: 1-13. https://doi.org/10.1016/j.rse.2012.12.002
  10. Garcia, M., I. Sandholt, P. Ceccato, M. Ridler, E. Mougin, L. Kergoat, L. Morillas, F. Timouk, R. Fensholt, and F. Domingo, 2013. Actual evapotranspiration in drylands derived from insitu and satellite data: Assessing biophysical constrains, Remote Sensing of Environment, 131: 103-118. https://doi.org/10.1016/j.rse.2012.12.016
  11. Giorgi, F., 1990. Simulation of regional climate using a limited area model nested in a general circulation model, Journal of Climate, 3(9): 941-963. https://doi.org/10.1175/1520-0442(1990)003<0941:SORCUA>2.0.CO;2
  12. Grenfell, T.C., 1983. A theoretical model of the optical properties of sea ice in the visible and near infrared. Journal of Geophysical Research, 88(C14): 9723-9735. https://doi.org/10.1029/JC088iC14p09723
  13. Hong, S., 2010. Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing, Remote Sensing of Environment, 114(5): 1136-1140. https://doi.org/10.1016/j.rse.2009.12.015
  14. Hong, S. and I. Shin, 2010. Global trends of sea ice: small-scale roughness and refractive index, Journal of Climate, 23(17): 4669-4676. https://doi.org/10.1175/2010JCLI3697.1
  15. Hong, S., I. Shin, Y. Byun, H. Seo, and Y. Kim, 2014. Analysis of sea ice surface properties using ASH and Hong approximations in passive satellite microwave remote sensing, Remote Sensing Letters, 5(2): 139-147. https://doi.org/10.1080/2150704X.2014.888106
  16. Imaoka, K., M. Kachi, H. Fujii, H. Murakami, M. Hori, A. Ono, T. Igarashi, K. Nakagawa, T. Oki, Y. Honda, and H. Shimoda, 2010. Global change observation mission (GCOM) for monitoring carbon, water cycles, and climate change, Proc. of The IEEE, 98(5): 717-734. https://doi.org/10.1109/JPROC.2009.2036869
  17. Jeong, H.R. and H.S. Lim, 2009. Technical development trend of international synthetic aperture radar satellite, Current Industrial and Technological Trends in Aerospace, 7(2): 25-32 (in Korean with English abstract).
  18. Jung, J.S. and C.S. Yang, 2011. Polarimetric scattering of sea ice and snow using L-band quadpolarized PALSAR data in Kongsfjorden, Svalbard, Ocean and Polar Research, 33(1): 1-11 (in Korean with English abstract).
  19. Katsuki, K., B.K. Khim, T. Itaki, Y. Okazaki, K. Ikehara, Y. Shin, H.I. Yoon, and C.Y. Kang, 2010. Sea-ice distribution and atmospheric pressure patterns in southwestern Okhotsk Sea since the last glacial maximum, Global and Planetary Change, 72(3): 99-107. https://doi.org/10.1016/j.gloplacha.2009.12.005
  20. Kim, B.K., T. Sakamoto, K. Ikehara, and H.S. Shin, 2011. Interglacial-glacial variations of opal and water contents in the central part of the Okhotsk Sea during the last 500 ka, Journal of the Geological Society of Korea, 47(5): 459-470 (in Korean with English abstract).
  21. Kim, M.G. and D.G. Lee, 2011. Korea Climate Change Assessment Report 2010, National Institute of Environmental Research (in Korean).
  22. Kwok, R., A. Schweiger, D.A. Rothrock, S. Pang, and C. Kottmeier, 1998. Sea ice motion from satellite passive microwave imagery assessed with ERS SAR and buoy motions, Journal of Geophysical Research, 103(C4): 8191-8214. https://doi.org/10.1029/97JC03334
  23. Lee, C.W., 2002. Temporal Variability of Wind Stress Curl over the Northeastern part of The East Sea in Winter Season based on EOF analysis, Master's thesis of Hanyang University. (in Korean)
  24. Mustapha, M.A. and S.I. Saitoh, 2008. Observations of sea ice interannual variations and spring bloom occurrences at the Japanese scallop farming area in the Okhotsk Sea using satellite imageries, Estuarine, Coastal and Shelf Science, 77(4): 577-588. https://doi.org/10.1016/j.ecss.2007.10.021
  25. Ogi, M. and Y. Tachibana, 2006. Influence of the annual Arctic Oscillation on the negative correlation between Okhotsk Sea ice and Amur River discharge, Geophysical Research Letters, 33(8).
  26. Ohshima, K.I., S.C. Riser, and M. Wakatsuchi, 2005. Mixed layer evolution in the Sea of Okhotsk observed with profiling floats and its relation to sea ice formation, Geophysical Research Letters, 32(6).
  27. Omstedt, A. and D. Chen, 2001. Influence of atmospheric circulation on the maximum ice extent in the Baltic Sea, Journal of Geophysical Research, 106(C3): 4493-4500. https://doi.org/10.1029/1999JC000173
  28. Parkinson, C.L., 2003. Aqua: an earth-observing satellite mission to examine water and other climate variables, IEEE Transactions on Geoscience and Remote Sensing, 41(2): 173-183. https://doi.org/10.1109/TGRS.2002.808319
  29. Riggs, G.A., D.K. Hall, and V.V. Salomonson, 2006. MODIS Sea Ice Products User Guide to Collection 5, NASA.
  30. Singarayer, J.S. and J.L. Bamber, 2003. EOF analysis of three records of sea-ice concentration spanning the last 30 years, Geophysical Research Letters, 30(5): 55/1-55/4.
  31. Srivastava, P.K., D. Han, M.R. Ramirez, and T. Islam, 2013. Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application, Water Resources Management, 27(8): 3127-3144. https://doi.org/10.1007/s11269-013-0337-9
  32. Stathopoulou, M. and C. Cartalis, 2009. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation, Remote Sensing of Environment, 113: 2592-2605. https://doi.org/10.1016/j.rse.2009.07.017
  33. Stroeve, J., J. Maslanik, and L. Xiaoming, 1998. An Intercomparison of DMSP F11- and F13-derived sea ice products, Remote Sensing of Environment, 64(2): 132-152. https://doi.org/10.1016/S0034-4257(97)00174-0
  34. Talley, L.D., 1991. An Okhotsk Sea water anomaly: implications for ventilation in the North Pacific, Deep Sea Research Part A. Oceanographic Research Papers, 38: S171-S190.
  35. Wagner, W., C. Pathe, M. Doubkova, D. Sabel, A. Bartsch, S. Hasenauer, G. Bloschl, K. Scipal, J. Martinez-Fernandez, and A. Low, 2008. Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR), Sensors, 8(2): 1174-1197. https://doi.org/10.3390/s8021174
  36. Wang, M., N.A. Bond, and J.E. Overland, 2007. Comparison of atmospheric forcing in four subarctic seas, Deep Sea Research Part II: Topical Studies in Oceanography, 54(23-26): 2543-2559. https://doi.org/10.1016/j.dsr2.2007.08.014
  37. Yang, C.S. and J.H. Na, 2009. Seasonal and interannual variations of sea ice distribution in the Arctic using AMSR-E data: July 2002 to May 2009, Korean Journal of Remote Sensing, 25(5): 423-434. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2009.25.5.423
  38. Zaksek, K. and K. Ostir, 2012. Downscaling land surface temperature for urban heat island diurnal cycle analysis, Remote Sensing of Environment, 117: 114-124. https://doi.org/10.1016/j.rse.2011.05.027

Cited by

  1. 한국의 극지 원격탐사 vol.34, pp.6, 2018, https://doi.org/10.7780/kjrs.2018.34.6.2.1