Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Comparisons of Collection 5 and 6 Aqua MODIS07_L2 air and Dew Temperature Products with Ground-Based Observation Dataset

Collection 5와 Collection 6 Aqua MODIS07_L2 기온과 이슬점온도 산출물간의 비교 및 지상 관측 자료와의 비교

  • Jang, Keunchang (Department of Environmental Science, Kangwon National University) ;
  • Kang, Sinkyu (Department of Environmental Science, Kangwon National University) ;
  • Hong, Suk Young (Department of Agricultural Environment, National Academy of Agricultural Science)
  • Received : 2014.08.10
  • Accepted : 2014.10.18
  • Published : 2014.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) provides air temperature (Tair) and dew point temperature (Tdew) profiles at a spatial resolution of 5 km. New Collection 6 (C006) MODIS07_L2 atmospheric profile product has been produced since 2012. The Collection 6 algorithm has several modifications from the previous Collection 5 (C005) algorithm. This study evaluated reliabilities of two alternative datasets of surface-level Tair and Tdew derived from C005 and C006 Aqua MODIS07_L2 (MYD07_L2) products using ground measured temperatures from 77 National Weather Stations (NWS). Saturated and actual vapor pressures were calculated using MYD07_L2 Tair and Tdew. The C006 Tair showed lower mean error (ME, -0.76 K) and root mean square error (RMSE, 3.34 K) than the C005 Tair (ME = -1.89 K, RMSE = 4.06 K). In contrasts, ME and RMSE of C006 Tdew were higher than those (ME = -0.39 K, RMSE = 5.65 K) of C005 product. Application of ambient lapse rate for Tair showed appreciable improvements of estimation accuracy for both of C005 and C006, though this modification slightly increased errors in C006 Tdew. The C006 products provided better estimation of vapor pressure datasets than the C005-derived vapor pressure. Our results indicate that, except for Tdew, C006 MYD07_L2 product showed better reliability for the region of South Korea than the C005 products.

Moderate Resolution Imaging Spectroradiometer(MODIS)로부터 산출된 기온과 이슬점 온도프로파일 자료는 5 km의 공간해상도로 연속적으로 지상을 감시하고 있으며, 2012년부터 기존의 산출 알고리즘(Collection 5, C005)을 개선한 Collection 6(C006) MODIS07_L2 대기프로파일 자료를 생산하고 있다. 이 연구에서는 두 가지 버전의 알고리즘으로 산출된 Aqua MODIS07_L2(MYD07_L2) 대기 프로파일 자료로부터 획득한 기온과 이슬점 온도에 대한 신뢰도를 평가하는 것으로, 전국 77 개소 정규기상관측지점을 대상으로 하였다. 또한 기온과 이슬점 온도를 이용하여 대기수증기압을 추정하여 미기상인자 산출에 대한 MYD07_L2의 적용 가능성을 살펴보았다. C006 기온은 지상 관측 자료와 비교에서 C005 기온의 오차(ME = -1.89 K, RMSE = 4.06 K)보다 개선된 결과를 보였다(ME = -0.76 K, RMSE = 3.34 K). 한편, 이슬점 온도의 경우에는 C006이 C005의 오차(ME = -0.39 K, RMSE = 5.65 K)보다 크게 나타났다. MYD07_L2 산출 고도와 지상 관측지점 간에 발생할 수 있는 고도 차이를 보정하기 위해 대기기온감률 방법을 적용한 결과, 기온의 경우 C005와 C006에서 모두 개선 효과를 확인할 수 있었지만, 이슬점 온도의 경우에는 C006에서 오차가 소폭 증가하였다(1.4%). 두 가지 버전의 MYD07_L2 자료를 이용하여 대기수증기압을 추정한 결과, C006 자료를 이용하였을 때 다소 개선된 결과를 보였다. 이 연구를 통해 한국에 대한 C006 MYD07_L2 산출물 중 기온의 신뢰도가 전반적으로 개선되었음을 확인할 수 있었다.

Keywords

References

  1. Aumann, H.H., M.T. Chahine, C. Gautier, M.D. Goldberg, E. Kalnay, L.M. Mc Millin, H. Revercomb, P.W. Rosenkranz, W.L. Smith, D.H. Staelin, L.L. Strow, and J. Susskind, 2003. AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems, IEEE Transactions on Geoscience and Remote Sensing, 41(2): 253-264. https://doi.org/10.1109/TGRS.2002.808356
  2. Batra, N., S. Islam, V. Venturini, G. Bisht, and L. Jiang, 2006. Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains, Remote Sensing of Environment, 103(1): 1-15. https://doi.org/10.1016/j.rse.2006.02.019
  3. Berg, A.A., J.S. Famiglietti, J.P. Walker, and P.R. Houser, 2003. Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes, Journal of Geophysical Research: Atmospheres, 108(D16): 4490, doi:10.1029/2002JD003334.
  4. Bisht, G. and R.L. Bras, 2010. Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study, Remote Sensing of Environment, 114(7): 1522-1534. https://doi.org/10.1016/j.rse.2010.02.007
  5. Bisht, G., V. Venturini, S. Islam, and L. Jiang, 2005. Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days, Remote Sensing of Environment, 97(1): 52-67. https://doi.org/10.1016/j.rse.2005.03.014
  6. Borbas, E.E., S.W. Seemann, A. Kern, L. Moy, J. Li, L.E. Gumley, and W.P. Menzel, 2011. MODIS atmospheric profile retrieval algorithm theoretical basis document, 1-32.
  7. Choi, G., 2011. Variability of Temperature Lapse Rate with Height and Aspect over Halla Mountain, Journal of climate research, 6(3): 171-186.
  8. Choi, G., B. Lee, S. Kang, and J. Tenhunen, 2010. Variations of summertime temperature lapse rate within a mountainous basin in the Republic of Korea -A case study of Punch Bowl, Yanggu in 2009, Journal of the Korean Association of Regional Geographers, 16(4): 339-354.
  9. Chung, U., H.H. Seo, K.H. Hwang, B.S. Hwang, J. Choi, J.T. Lee, and J.I. Yun, 2006. Minimum temperature mapping over complex terrain by estimating cold air accumulation potential, Agricultural and Forest Meteorology, 137(1-2): 15-24. https://doi.org/10.1016/j.agrformet.2005.12.011
  10. Cosgrove, B.A., D. Lohmann, K.E. Mitchell, P.R. Houser, E.F. Wood, J.C. Schaake, A. Robock, C. Marshall, J. Sheffield, Q. Duan, L. Luo, W. Higgins, R.T. Pinker, J.D. Tarpley, and J. Meng, 2003. Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, Journal of Geophysical Research: Atmospheres, 108(D22): - 8842. https://doi.org/10.1029/2002JD003118
  11. Dingman, L. 2008. Physical hydrology, Second Edition ed. Long Grove, Illinois, USA: Waveland Press. 646 p.
  12. Do, N., S. Kang, S. Myeong, T. Chun, J. Lee, and C.B. Lee, 2012. The Estimation of Gross Primary Productivity over North Korea Using MODIS FPAR and WRF Meteorological Data, Korean Journal of Remote Sensing, 28 215-226. https://doi.org/10.7780/kjrs.2012.28.2.215
  13. Glassy, J.M. and S.W. Running, 1994. Validating Diurnal Climatology Logic of the MT-CLIM Model Across a Climatic Gradient in Oregon, Ecological Applications, 4(2): 248-257. https://doi.org/10.2307/1941931
  14. Goward, S.N., Y. Xue, and K.P. Czajkowski, 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model, Remote Sensing of Environment, 79(2-3): 225-242. https://doi.org/10.1016/S0034-4257(01)00275-9
  15. Hill, D.J., 2013. An assessment of spatial models for daily minimum and maximum air temperature, GIScience & Remote Sensing, 50(3): 281-300. https://doi.org/10.1080/15481603.2013.808459
  16. Houborg, R.M. and H. Soegaard, 2004. Regional simulation of ecosystem $CO_2$ and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data, Application to Zealand, Denmark. Remote Sensing of Environment, 93(1-2): 150-167. https://doi.org/10.1016/j.rse.2004.07.001
  17. IPCC. 2007. Climate change 2007: The physical science basis. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
  18. Jang, K., S. Kang, H. Kim, and H. Kwon, 2009. Evaluation of Shortwave Irradiance and Evapotranspiration Derived from Moderate Resolution Imaging Spectroradiometer (MODIS), Asia-Pacific Journal of Atmospheric Sciences, 45(2): 233-246.
  19. Jang, J.D., A.A. Viau, and F. Anctil, 2004. Neural network estimation of air temperatures from AVHRR data, International Journal of Remote Sensing, 25(21): 4541-4554.
  20. Jang, K., S. Kang, J. Kim, C.B. Lee, T. Kim, J. Kim, R. Hirata, and N. Saigusa, 2010. Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation, Remote Sensing of Environment, 114(3): 657-673. https://doi.org/10.1016/j.rse.2009.11.010
  21. Jang, K., S. Kang, J. Kimball, and S.Y. Hong, 2014. Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data, Remote Sensing, 6(9): 8387-8404. https://doi.org/10.3390/rs6098387
  22. Jang, K., S. Kang, Y. Lim, S. Jeong, J. Kim, J.S. Kimball, and S.Y. Hong, 2013. Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System, Journal of Geophysical Research: Atmospheres, 118(23): 12927-12940, doi:10.1002/2013JD020639.
  23. Jeong, S., K. Jang, S. Kang, J. Kim, H. Kondo, M. Gamo, J. Asanuma, N. Saigusa, S. Wang, and S. Han, 2009. Evaluation of MODIS-derived Evapotranspiration at the Flux Tower Sites in East Asia, Korean, Korean Journal of Agricultural and Forest Meteorology, 11(4): 174-184. https://doi.org/10.5532/KJAFM.2009.11.4.174
  24. Jones, L.A., C.R. Ferguson, J.S. Kimball, Ke Zhang, S.T.K. Chan, K.C. McDonald, E.G. Njoku, and E.F. Wood, 2010. Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 111-123. https://doi.org/10.1109/JSTARS.2010.2041530
  25. Kim, D.S. and B.H. Kwon, 2007. Vertical Structure of the Coastal Atmospheric Boundary Layer Based on Terra/MODIS Data, Atmosphere, 17(3): 281-289.
  26. Lakshmi, V., K. Czajkowski, R. Dubayah, and J. Susskind, 2001. Land surface air temperature mapping using TOVS and AVHRR, International Journal of Remote Sensing, 22(4): 643-662. https://doi.org/10.1080/01431160050505900
  27. Lee, J., S. Kang, K. Jang, J. Ko, and S.Y. Hong, 2011. The Evaluation of Meteorological Inputs retrieved from MODIS for Estimation of Gross Primary Productivity in the US Corn Belt Region, Korean Journal of Remote Sensing, 27(4): 481-494. https://doi.org/10.7780/kjrs.2011.27.4.481
  28. Masuoka, E., A. Fleig, R.E. Wolfe, and F. Patt, 1998. Key characteristics of MODIS data products, IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1313-1323. https://doi.org/10.1109/36.701081
  29. McElroy, M.B., 2002. The atmospheric environment: Effects of human activity, Princeton, NJ: Princeton University Press. 71 p.
  30. Menzel, WP, Frey RA, Baum BA, and Zhang H. 2006. Cloud top properties and cloud phase algorithm theoretical basis document. 1 p.
  31. Menzel, WP, Seemann SW, and Gumley LE. 2002. MODIS atmospheric profile retrieval algorithm theoretical basis document, University of Wisconsin-Madison.
  32. Mitra, A.K., A.K. Sharma, I. Bajpai, and P.K. Kundu, 2012. An atmospheric instability derived with MODIS profile using real-time direct broadcast data over the Indian region, Natural Hazards, 63(2): 1007-1023. https://doi.org/10.1007/s11069-012-0202-9
  33. Mu, Q., M. Zhao, and S.W. Running, 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sensing of Environment, 115(8): 1781-1800. https://doi.org/10.1016/j.rse.2011.02.019
  34. Niclos, R., J.A. Valiente, M.J. Barbera, and V. Caselles, 2014. Land Surface Air Temperature Retrieval From EOS-MODIS Images, Geoscience and Remote Sensing Letters, IEEE, 11(8): 1380-1384. https://doi.org/10.1109/LGRS.2013.2293540
  35. Park, H., B. Sohn, and E. Chung, 2008. Estimation of total precipitable water from MODIS infrared measurements over East Asia, Korean Journal of Remote Sensing, 24(4): 309-324. https://doi.org/10.7780/kjrs.2008.24.4.309
  36. Park, S., B. Sohn, E. Chung, and M. Koenig, 2006. Estimating Stability Indices from the MODIS Infrared Measurements over the Korean Peninsula, Korean Journal of Remote Sensing, 22(6): 469-483. https://doi.org/10.7780/kjrs.2006.22.6.469
  37. Prihodko, L. and S.N. Goward, 1997. Estimation of air temperature from remotely sensed surface observations, Remote Sensing of Environment, 60(3): 335-346. https://doi.org/10.1016/S0034-4257(96)00216-7
  38. Rhee, J. and J. Im, 2014. Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products, Remote Sensing, 6(8): 7360-7378. https://doi.org/10.3390/rs6087360
  39. Ryu, Y., D.D. Baldocchi, H. Kobayashi, C. Ingen, J. Li, T.A. Black, J. Beringer, E. Gorsel, A. Knohl, and B.E. Law, 2011. Integration of MODIS land and atmosphere products with a coupled process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales, Global Biogeochemical Cycles, 25(4):.
  40. Ryu, Y., S. Kang, S. Moon, and J. Kim, 2008. Evaluation of land surface radiation balance derived from moderate resolution imaging spectroradiometer (MODIS) over complex terrain and heterogeneous landscape on clear sky days, Agricultural and Forest Meteorology, 148(10): 1538-1552. https://doi.org/10.1016/j.agrformet.2008.05.008
  41. Seemann, S.W., J. Li, W.P. Menzel, and L.E. Gumley, 2003. Operational Retrieval of Atmospheric Temperature, Moisture, and Ozone from MODIS Infrared Radiances, Journal of Applied Meteorology, 42(8): 1072-1091. https://doi.org/10.1175/1520-0450(2003)042<1072:OROATM>2.0.CO;2
  42. Stisen, S., I. Sandholt, A. Norgaard, R. Fensholt, and L. Eklundh, 2007. Estimation of diurnal air temperature using MSG SEVIRI data in West Africa, Remote Sensing of Environment, 110(2): 262-274. https://doi.org/10.1016/j.rse.2007.02.025
  43. Sun, Y., J. Wang, R. Zhang, R.R. Gillies, Y. Xue, and Y. Bo, 2005. Air temperature retrieval from remote sensing data based on thermodynamics, Theoretical and Applied Climatology, 80(1): 37-48. https://doi.org/10.1007/s00704-004-0079-y
  44. Tang, B. and Z. Li, 2008. Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data, Remote Sensing of Environment, 112(9): 3482-3492. https://doi.org/10.1016/j.rse.2008.04.004
  45. Urban, M., J. Eberle, C. Huttich, C. Schmullius, and M. Herold, 2013. Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale, Remote Sensing, 5(5): 2348-2367. https://doi.org/10.3390/rs5052348
  46. Vancutsem, C., P. Ceccato, T. Dinku, and S.J. Connor, 2010. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa, Remote Sensing of Environment, 114(2): 449-465. https://doi.org/10.1016/j.rse.2009.10.002
  47. Vogt, J.V., A.A. Viau, and F. Paquet, 1997. Mapping regional air temperature fields using satellitederived surface skin temperatures, International Journal of Climatology, 17(14): 1559-1579. https://doi.org/10.1002/(SICI)1097-0088(19971130)17:14<1559::AID-JOC211>3.0.CO;2-5
  48. Williamson, S., D. Hik, J. Gamon, J. Kavanaugh, and G. Flowers, 2014. Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment, Remote Sensing, 6(2): 946-963. https://doi.org/10.3390/rs6020946
  49. Zhao, M. and S.W. Running, 2010. Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009, Science, 329(5994): 940-943. https://doi.org/10.1126/science.1192666

Cited by

  1. 산악기상관측정보를 이용한 위성정보 기반의 전천후 기온 자료의 평가 - 강원권역을 중심으로 vol.19, pp.1, 2017, https://doi.org/10.5532/kjafm.2017.19.1.19
  2. MODIS 전천후 기상자료 기반의 생물리학적 벼 수량 모형 개발 vol.33, pp.5, 2014, https://doi.org/10.7780/kjrs.2017.33.5.2.11
  3. MODIS 식생지수와 임상도를 활용한 산림 식물계절 분석 vol.34, pp.2, 2014, https://doi.org/10.7780/kjrs.2018.34.2.1.9
  4. 머신러닝 기법의 산림 총일차생산성 예측 모델 비교 vol.21, pp.1, 2014, https://doi.org/10.5532/kjafm.2019.21.1.29