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Advances in Atmospheric Sciences

, Volume 31, Issue 3, pp 705–720 | Cite as

Satellite-based estimation of daily average net radiation under clear-sky conditions

  • Jiangtao Hou
  • Gensuo Jia
  • Tianbao Zhao
  • Hesong Wang
  • Bohui Tang
Article

Abstract

Daily average net radiation (DANR) is an important variable for estimating evapotranspiration from satellite data at regional scales, and is used for atmospheric and hydrologic modeling, as well as ecosystem management. A scheme is proposed to estimate the DANR over large heterogeneous areas under clear-sky conditions using only remotely sensed data. The method was designed to overcome the dependence of DANR estimates on ground data, and to map spatially consistent and reasonably distributed DANR, by using various land and atmospheric data products retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) data. An improved sinusoidal model was used to retrieve the diurnal variations of downward shortwave radiation using a single instantaneous value from satellites. The downward shortwave component of DANR was directly obtained from this instantaneous value, and the upward shortwave component was estimated using satellite-derived albedo products. Four observations of air temperature from MOD07_L2 and MYD07_L2 data products were used to derive the downward longwave component of DANR, while the upward longwave component was estimated using the land surface temperature (LST) and the surface emissivity from MOD11_L2. Compared to in situ observations at the cropland and grassland sites located in Tongyu, northern China, the root mean square error (RMSE) of DANR estimated for both sites under clear-sky conditions was 37 W m−2 and 40 W m−2, respectively. The errors in estimation of DANR were comparable to those from previous satellite-based methods. Our estimates can be used for studying the surface radiation balance and evapotranspiration.

Key words

daily average net radiation satellite climate model four-component radiation surface radiation balance 

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References

  1. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103(D24), 32141–32157.CrossRefGoogle Scholar
  2. Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56, United Nations Food and Agriculture Organization, Rome, 15pp.Google Scholar
  3. Allen, R. G., R. Trezza, and M. Tasumi, 2006: Analytical integrated functions for daily solar radiation on slopes. Agricultural and Forest Meteorology, 139, 55–73.CrossRefGoogle Scholar
  4. Bastiaanssen, W., 2000: SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. Hydrol., 229, 87–100.CrossRefGoogle Scholar
  5. Bastiaanssen, W., M. Menenti, R. A. Feddes, and A. Holtslag, 1998: A remote sensing surface energy balance algorithm for land (SEBAL)—1. Formulation. J. Hydrol., 212–213, 198–212.CrossRefGoogle Scholar
  6. 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 Sens. Environ., 114, 1522–1534.CrossRefGoogle Scholar
  7. 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 Sens. Environ., 97(1), 52–67.CrossRefGoogle Scholar
  8. Dong, A., S. R. Grattan, J. J. Carrol, and C. R. K. Prashar, 1992: Estimation of daytime net radiation over well-watered grass. J. Irrig. Drain. Eng., 118(3), 466–479.CrossRefGoogle Scholar
  9. Ellingson, R. G., 1995: Surface longwave fluxes from satellite observations: A critical review. Remote Sens. Environ., 51, 89–97.CrossRefGoogle Scholar
  10. Gao, Y. C., D. Long, and Z. L. Li, 2008: Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China. Inter. J. Remote Sens., 29(11), 3295–3315.CrossRefGoogle Scholar
  11. Gates, D. M., 1980: Biophysical Ecology. Springer-Verlag, New York, 635pp.CrossRefGoogle Scholar
  12. Houborg, R. M., and H. Soegaard, 2004: Regional simulation of ecosystem CO2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data. Application to Zealand, Denmark. Remote Sens. Environ., 93, 150–167.Google Scholar
  13. Hu, Z. L., and Z. X. Chen, 2006: Introduction to Geographic Environment. Science Press, Beijing, 164pp.Google Scholar
  14. Hurtado, E., and J. A. Sobrino, 2001: Daily net radiation estimated from air temperature and NOAA-AVHRR data: A case study for the Iberian Peninsula. Inter. J. Remote Sens., 22(8), 1521–1533.CrossRefGoogle Scholar
  15. Iqbal, M., 1983: An Introduction to Solar Radiation. Academic Press, Orlando, 390pp.Google Scholar
  16. Irmak, S., A. Irmak, J. Jones, T. Howell, J. Jacobs, R. Allen, and G. Hoogenboom, 2003: Predicting daily net radiation using minimum climatological data. J. Irrig. Drain. Eng., 129(4), 256–269.CrossRefGoogle Scholar
  17. Jackson, R. D., J. L. Hatfield, R. J. Reginato, S. B. Idso, and Jr. P. J. Pinter, 1983: Estimation of daily evapotranspiration from one time-of-day measurements. Agricultural Water Management, 7, 351–362.CrossRefGoogle Scholar
  18. Jacobs, J. M., D. A. Myers, M. C. Anderson, and G. R. Diak, 2000: GOES surface insolation to estimate wetlands evapotranspiration. J. Hydrol., 266, 53–65.CrossRefGoogle Scholar
  19. Jegede, O. O., 1997: Daily averages of net radiation measured at Osu, Nigeria in 1995. Int. J. Climatol., 17, 1357–1367.CrossRefGoogle Scholar
  20. Jiang, L., and S. Islam, 2001: Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resour. Res., 37(2), 329–340.CrossRefGoogle Scholar
  21. Jin, Y. F., C. B. Schaaf, C. E. Woodcock, F. Gao, X. W. Li, A. H. Strahler, W. Lucht, and S. L. Liang, 2003: Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation. J. Geophys. Res., 108(D5), 4159, doi:10.1029/2002JD002804.CrossRefGoogle Scholar
  22. Kasten, F., and A. T. Young, 1989: Revised optical air mass tables and approximation formula. Appl. Opt., 28, 4735–4738.CrossRefGoogle Scholar
  23. Kjaersgaard, J. H., R. H. Cuenca, F. L. Plauborg, and S. Hansen, 2007: Long-term comparisons of net radiation calculation schemes. Bound.-Layer Meteor., 123, 417–431.CrossRefGoogle Scholar
  24. Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the Earth’s atmosphere. J. Atmos. Sci., 31, 118–133.CrossRefGoogle Scholar
  25. Levy, R. C., L. A. Remer, D. Tanré, S. Mattoo, and Y. J. Kaufman, 2009: Algorithm for remote sensing of tropospheric aerosol from MODIS: Collections 005 and 051. [Available online at http://modis-atmos.gsfc.nasa.gov/_docs/ATBD_MOD04_C005_rev2.pdf.]Google Scholar
  26. Li, X. W., and A. H. Strahler, 1992: Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy—effect of crown shape and mutual shadowing. IEEE Trans. Geosci. Remote Sens., 30(2), 276–292.CrossRefGoogle Scholar
  27. Liang, S. L., 2004: Quantitative Remote Sensing of Land Surfaces. John Wiley and Sons, 560pp.Google Scholar
  28. Liang, S. L., A. H. Strahler, and C. Walthall, 1999: Retrieval of land surface albedo from satellite observations: A simulation study. J. Appl. Meteor., 38(6), 712–725.CrossRefGoogle Scholar
  29. Liu, B. Y. H., and R. C. Jordan, 1960: The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Solar Energy, 4(3), 1–19.CrossRefGoogle Scholar
  30. Long, D., Y. C. Gao, and V. P. Singh, 2010: Estimation of daily average net radiation from MODIS data and DEM over the Baiyangdian watershed in North China for clear sky days. J. Hydrol., 388(3–4), 217–233.CrossRefGoogle Scholar
  31. Lucht, W., C. B. Schaaf, and A. H. Strahler, 2000: An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Trans. Geosci. Remote Sens., 38(2), 977–998.CrossRefGoogle Scholar
  32. Ma, Y. M., Z. B. Su, Z. L. Li, T. Koike, and M. Menenti, 2002: Determination of regional net radiation and soil heat flux over a heterogeneous landscape of the Tibetan Plateau. Hydrol. Process, 16(15), 2963–2971.CrossRefGoogle Scholar
  33. Monteith, J. L., 1973: Principles of Environmental Physics. Edward Arnold, London, 241pp.Google Scholar
  34. Niemela, S., P. Raisanen, and H. Savijarvi, 2001: Comparison of surface radiative flux parameterizations—Part II. Shortwave radiation. Atmospheric Research, 58, 141–154.CrossRefGoogle Scholar
  35. Nishida, K., R. R. Nemani, S. W. Running, and J. M. Glassy, 2003: An operational remote sensing algorithm of land surface evaporation. J. Geophys. Res., 108(D9), 4270, doi:10.1029/2002JD002062.CrossRefGoogle Scholar
  36. Norman, J. M., and Coauthors, 2003: Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resour. Res., 39(8), 1221, doi:10.1029/2002WR001775.CrossRefGoogle Scholar
  37. Roerink, G. J., Z. Su, and M. Menenti, 2000: S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth (B), 25(2), 147–157.CrossRefGoogle Scholar
  38. Ross, J. K., 1981: The radiation regime and architecture of plant stands. Vol. 3, Tasks for Vegetation Sciences, Dr. W. Junk, Norwell, Mass., 392pp.CrossRefGoogle Scholar
  39. Roujean, J. L., M. Leroy, and P. Y. Deschamps, 1992: A directional reflectance model of the Earth’s surface for the correction of remote sensing. J. Geophys. Res., 97(D18), 20 455–20 468.CrossRefGoogle Scholar
  40. Ryu, Y., S. Kang, S. K. 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.CrossRefGoogle Scholar
  41. Salomon, J. G., C. B. Schaaf, A. H. Strahler, F. Gao, and Y. F. Jin, 2006: Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the Aqua and Terra platforms. IEEE Trans. Geosci. Remote Sens, 44(6), 1555–1565.CrossRefGoogle Scholar
  42. Samani, Z., A. S. Bawazir, M. Bleiweiss, R. Skaggs, and V. D. Tran, 2007: Estimating daily net radiation over vegetation canopy through remote sensing and climatic data. J. Irrig. Drain. Eng., 133, 291–297.CrossRefGoogle Scholar
  43. Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ, 83, 135–148.CrossRefGoogle Scholar
  44. 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. J. Appl. Meteor., 42(8), 1072–1091.CrossRefGoogle Scholar
  45. Snyder, W. C., and Z. M. Wan, 1998: BRDF models to predict spectral reflectance and emissivity in the thermal infrared. IEEE Trans. Geosci. Remote Sens., 36(1), 214–225.CrossRefGoogle Scholar
  46. Sobrino, J. A., M. Gómez, J. C. Jiménez-Muñoz, and A. Olioso, 2007: Application of a simple algorithm to estimate daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula. Remote Sens. Environ., 110, 139–148.CrossRefGoogle Scholar
  47. Su, H. B., M. F. McCabe, E. F. Wood, Z. Su, and J. H. Prueger, 2005: Modeling evapotranspiration during SMACEX: Comparing two approaches for local-and regional-scale prediction. J. Hydrometeor., 6, 910–922.CrossRefGoogle Scholar
  48. Su, Z., 2002: The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 6, 85–99.CrossRefGoogle Scholar
  49. Swinbank, W. C., 1963: Long-wave radiation from clear skies. Quart. J. Roy. Meteor. Soc., 89, 339–348.CrossRefGoogle Scholar
  50. Tang, B., and Z.-L. Li, 2008: Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data. Remote Sens. Environ., 112, 3482–3492.CrossRefGoogle Scholar
  51. Wan, Z. M., and J. Dozier, 1996: A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens., 34(4), 892–905.CrossRefGoogle Scholar
  52. Wang, H., G. Jia, C. Fu, J. Feng, T. Zhao, and Z. Ma, 2010: Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ., 114, 2248–2258.CrossRefGoogle Scholar
  53. Wang, K. C., and S. Liang, 2009a: Estimation of daytime net radiation from shortwave radiation measurements and meteorological observations. J. Appl. Meteor. Climatol., 48, 634–643.CrossRefGoogle Scholar
  54. Wang, W., and S. Liang, 2009b: Estimating high spatial resolution clear-sky surface downwelling longwave radiation and net longwave radiation from MODIS Data. Remote Sens. Environ., 113, 745–754.CrossRefGoogle Scholar
  55. Wanner, W., X. Li, and A. H. Strahler, 1995: On the derivation of kernels for kernel-driven models of bidirectional reflectance. J. Geophys. Res., 100(D10), 21077–21089.CrossRefGoogle Scholar
  56. Wu, H., X. Zhang, S. Liang, H. Yang, and G. Zhou, 2012: Estimation of clear-sky land surface longwave radiation from MODIS data products by merging multiple models. J. Geophys. Res., 117, D22107, doi:10.1029/2012JD017567.Google Scholar
  57. Zhou, Y., D. P. Kratz, A. C. Wilber, S. K. Gupta, and R. D. Cess, 2007: An improved algorithm for retrieving surface downwelling longwave radiation from satellite measurements. J. Geophys. Res., 112, D15102, doi:10.1029/2006JD008159.CrossRefGoogle Scholar
  58. Zillman, J. W., 1972: A study of some aspects of the radiation and heat budgets of the southern hemisphere oceans. Report 26, Department of International Meteorological Studies, Bureau of Meteorology, Canberra, 562pp.Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jiangtao Hou
    • 1
    • 2
  • Gensuo Jia
    • 1
  • Tianbao Zhao
    • 1
  • Hesong Wang
    • 1
  • Bohui Tang
    • 3
  1. 1.Key Laboratory of Regional Climate-Environment Research for East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of the Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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