Shortwave Albedo

  • Shunlin LiangEmail author
  • Xiaotong Zhang
  • Zhiqiang Xiao
  • Jie Cheng
  • Qiang Liu
  • Xiang Zhao
Part of the SpringerBriefs in Earth Sciences book series (BRIEFSEARTH)


This chapter describes the algorithm, analysis, preliminary validation, and application of the GLASS albedo product. Unlike traditional remote sensing products, the GLASS albedo product was generated in two steps: the first step retrieved albedo values from remote sensing data using two direct-estimation algorithms, and the second step applied a statistics-based temporal filter to the directly estimated albedo values to generate a high-quality, gapless final product. The GLASS albedo product has been validated using FLUXNET observation data and compared with the MODIS instrument Bidirectional Reflectance Distribution Function (BRDF)/albedo product. The results show the high quality and accuracy of the GLASS albedo product and its suitability for long-term global environmental change studies. It is one of the longest duration (1981–2010) satellite shortwave albedo products in the world.


Albedo Shortwave radiation Angular bin Temporal filter MODIS AVHRR GLASS 


  1. Abdalati W, Steffen K (1997) Snowmelt on the Greenland ice sheet as derived from passive microwave satellite data. J Clim 10:165–175CrossRefGoogle Scholar
  2. Bacour C, Breon F (2005) Variability of biome reflectance directional signatures as seen by POLDER. Remote Sens Environ 98:80–95CrossRefGoogle Scholar
  3. Baldocchi D, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434CrossRefGoogle Scholar
  4. Baret F, Morissette JT, Fernandes RA, Champeaux JL, Myneni RB, Chen J, Plummer S, Weiss M, Bacour C, Garrigues S, Nickeson JE (2006) Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Trans Geosci Remote Sens 44:1794–1803CrossRefGoogle Scholar
  5. Box JE, Fettweis X, Stroeve JC, Tedesco M, Hall DK, Steffen K (2012) Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers. Cryosphere 6:821–839CrossRefGoogle Scholar
  6. Bromwich DH, Chen QS, Li YF, Cullather RI (1999) Precipitation over Greenland and its relation to the North Atlantic oscillation. J Geophys Research-Atmos 104:22103–22115CrossRefGoogle Scholar
  7. Cescatti A, Marcolla B, Vannan SKS, Pan JY, Roman MO, Yang X, Ciais P, Cook RB, Law BE, Matteucci G, Migliavacca M, Moors E, Richardson AD, Seufert G, Schaaf CB (2012) Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens Environ 121:323–334CrossRefGoogle Scholar
  8. Chapin FS, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH, Schimel JP, Beringer J, Chapman WL, Epstein HE, Euskirchen ES, Hinzman LD, Jia G, Ping CL, Tape KD, Thompson CDC, Walker DA, Welker JM (2005) Role of land-surface changes in Arctic summer warming. Science 310:657–660CrossRefGoogle Scholar
  9. Comiso JC (2003) Warming trends in the Arctic from clear sky satellite observations. J Clim 16:3498–3510CrossRefGoogle Scholar
  10. Cui Y, Mitomi Y, Takamura T (2009) An empirical anisotropy correction model for estimating land surface albedo for radiation budget studies. Remote Sens Environ 113:24–39CrossRefGoogle Scholar
  11. Danielson JJ, Gesch DB (2011) Global multi-resolution terrain elevation data 2010. In: U.S. Department of the Interior and U.S. Geological SurveyGoogle Scholar
  12. Dickinson RE (1983) Land surface processes and climate surface albedos and energy-balance. Adv Geophys 25:305–353CrossRefGoogle Scholar
  13. Diner DJ, Martonchik JV, Borel C, Gerstl S, Gordon HR, Knyazikhin Y, Myneni R, Pinty B, Verstraete MM (2008) Multi-angle imaging spectroradiometer (MISR) level 2 surface retrieval algorithm theoretical basis (version E). Jet Propulsion Laboratory, Pasadena Google Scholar
  14. Fang H, Liang S, Kim H-Y, Townshend JR, Schaaf CL, Strahler AH, Dickinson RE (2007) Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products. J Geophys Research-Atmos 112:D20206. doi: 20210.21029/22006JD008377 CrossRefGoogle Scholar
  15. Fettweis X, Hanna E, Lang C, Belleflamme A, Erpicum M, Gallée H (2013) Brief communication “Important role of the mid-tropospheric atmospheric circulation in the recent surface melt increase over the Greenland ice sheet”. Cryosphere 7:241–248CrossRefGoogle Scholar
  16. Gao F, Schaaf C, Strahler A, Roesch A, Lucht W, Dickinson R (2005) MODIS bidirectional reflectance distribution function and albedo climate modeling grid products and the variability of albedo for major global vegetation types. J Geophys Res 110:D01104CrossRefGoogle Scholar
  17. Geiger B, Roujean J, Carrer D, Meurey C (2005) Product user manual (PUM) land surface albedo. LSA SAF internal documentsGoogle Scholar
  18. Geiger B Samain O (2004) Albedo determination, algorithm theoretical basis document of the CYCLOPES project. In: Météo-France/CNRM, p 20Google Scholar
  19. Govaerts Y, Lattanzio A, Taberner M, Pinty B (2008) Generating global surface albedo products from multiple geostationary satellites. Remote Sens Environ 112:2804–2816CrossRefGoogle Scholar
  20. He T, Liang S, Yu Y, Wang D, Gao F, Liu Q (2013) Greenland surface albedo changes in July 1981–2012 from satellite observations, Environmental Research Letters, (in press)Google Scholar
  21. Hu BX, Lucht W, Strahler AH, Schaaf CB, Smith M (2000) Surface albedos and angle-corrected NDVI from AVHRR observations of South America. Remote Sens Environ 71:119–132CrossRefGoogle Scholar
  22. Kendall MG (1976) Rank correlation methods. 4th edn., Griffin, LondonGoogle Scholar
  23. Leroy M, Deuzé J, Bréon F, Hautecoeur O, Herman M, Buriez J, Tanré D, Bouffies S, Chazette P, Roujean J (1997) Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. J Geophys Res 102:17023–17037CrossRefGoogle Scholar
  24. Li XW, Gao F, Wang JD, Strahler A (2001) A priori knowledge accumulation and its application to linear BRDF model inversion. J Geophys Research-Atmos 106:11925–11935CrossRefGoogle Scholar
  25. Liang S (2001) Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sens Environ 76:213–238CrossRefGoogle Scholar
  26. Liang S (2003) A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Trans Geosci Remote Sens 41:136–145CrossRefGoogle Scholar
  27. Liang S (2004) Quantitative remote sensing of land surface. Wiley, New JerseyGoogle Scholar
  28. Liang S (ed) (2008) Advances in land remote sensing: system, modeling, inversion and application. Springer, BerlinGoogle Scholar
  29. Liang S, Li X, Wang J (eds) (2012) Advanced remote sensing: terrestrial information extraction and applications. Academic Press, OxfordGoogle Scholar
  30. Liang S, Strahler A, Walthall C (1999) Retrieval of land surface albedo from satellite observations: a simulation study. J Appl Meteorol 38:712–725CrossRefGoogle Scholar
  31. Liang S, Stroeve J, Box J (2005) Mapping daily snow/ice shortwave broadband albedo from moderate resolution imaging spectroradiometer (MODIS): the improved direct retrieval algorithm and validation with Greenland in situ measurement. J Geophys Res 110:D10109CrossRefGoogle Scholar
  32. Liang S, Wang K, Zhang X, Wild M (2010) Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE J Spec Top Appl Earth Obs Remote Sens 3:225–240CrossRefGoogle Scholar
  33. Liang S, He T, Zhang X, Cheng J, Wang D (2013a) Remote sensing of earth surface radiation budget, in remote sensing of land surface turbulent fluxes and soil surface moisture content: state of the art. In: Petropoulos GP (ed), CRC Press, Boca raton, pp 125–165Google Scholar
  34. Liang S, Zhao X, Yuan W, Liu S, Cheng X, Xiao Z, Zhang X, Liu Q, Cheng J, Tang H, Qu YH, Bo Y, Qu Y, Ren H, Yu K, Townshend J (2013b) A long-term global land surface satellite (GLASS) data-set for environmental studies. Int J Digit Earth. doi: 10.1080/17538947.17532013.17805262 Google Scholar
  35. Liu NF, Liu Q, Wang LZ, Liang SL, Wen JG, Qu Y, Liu SH (2013a) A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data. Hydrol Earth Syst Sci 17:2121–2129CrossRefGoogle Scholar
  36. Liu Q, Wang L, Qu Y, Liu N, Liu S, Tang H, Liang S (2013b) Preliminary evaluation of the long-term GLASS albedo product. Int J Digit Earth. doi: 10.1080/17538947.17532013.17804601 Google Scholar
  37. Liu Q, Wen JG, Qu Y, He T, Zhang XT (2012) Broadband albedo. In: Liang S, Li X, Wang J (eds.) Advanced remote sensing: terrestrial information extraction and applications Academic Press, Oxford, pp 173–230Google Scholar
  38. Long CN, Gaustad KL (2004) The shortwave (SW) clear-sky detection and fitting algorithm: algorithm operational details and explanations. In: Atmospheric radiation measurement program technical report, 26 ppGoogle Scholar
  39. Lucht W, Schaaf C, Strahler A (2002) An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Trans Geosci Remote Sens 38:977–998CrossRefGoogle Scholar
  40. Lucht W, Schaaf CB, Strahler AH (2000) An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Trans Geosci Remote Sens 38:977–998CrossRefGoogle Scholar
  41. Maignan F, Bréon F, Lacaze R (2004) Bidirectional reflectance of earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the hot spot. Remote Sens Environ 90:210–220CrossRefGoogle Scholar
  42. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259CrossRefGoogle Scholar
  43. Mason P (2005). Implementation plan for the global observing systems for climate in support of the UNFCCC. In: 21st international conference on interactive information processing systems for meteorology, oceanography, and hydrology. San DiegoGoogle Scholar
  44. Moody EG, King MD, Platnick S, Schaaf CB, Feng G (2005) Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products. IEEE Trans Geosci Remote Sens 43:144–158CrossRefGoogle Scholar
  45. Mote TL (2007) Greenland surface melt trends 1973–2007: evidence of a large increase in 2007. Geophys Res Lett, 34:L22507Google Scholar
  46. Muller J-P, Preusker R, Fischer J, Zuhlke M, Brockmann C, Regner P (2007) ALBEDOMAP: MERIS land surface albedo retrieval using data fusion with MODIS BRDF and its validation using contemporaneous EO and in situ data products. In: Geoscience and remote sensing symposium, 2007. IGARSS 2007. IEEE International, IEEE, pp 2404–2407Google Scholar
  47. Nghiem SV, Hall DK, Mote TL, Tedesco M, Albert MR, Keegan K, Shuman CA, DiGirolamo NE, Neumann G (2012) The extreme melt across the Greenland ice sheet in 2012. Geophys Res Lett 39:L20502CrossRefGoogle Scholar
  48. Pedelty J, Devadiga S, Masuoka E, Brown M, Pinzon J, Tucker C, Roy D, Ju JC, Vermote E, Prince S, Nagol J, Justice C, Schaaf C, Liu JC, Privette J, Pinheiro A (2007) Generating a long-term land data record from the AVHRR and MODIS instruments. In: Ieee international geoscience and remote sensing symposium, Ieee, New York, pp 1021–1024Google Scholar
  49. Pinty B, Roveda F, Verstraete M, Gobron N, Govaerts Y, Martonchik J, Diner D, Kahn R (2000) Surface albedo retrieval from meteosat 1 theory. J Geophys Res 105:18099–18112CrossRefGoogle Scholar
  50. Qin W, Herman J, Ahmad Z (2001) A fast, accurate algorithm to account for non-Lambertian surface effects on TOA radiance. J Geophys Res 106:22671–22684CrossRefGoogle Scholar
  51. Qu Y, Liu Q, Liang SL, Wang LZ, Liu NF, Liu SH (2013) Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Trans Geosci Remote Sens. doi: 10.1109/TGRS.2013.2245670 Google Scholar
  52. Rahman H, Pinty B, Verstraete M (1993) Coupled surface-atmosphere reflectance (CSAR) model 2. semiempirical surface model usable with NOAA advanced very high resolution radiometer data. J Geophys Res 98:20791–20801CrossRefGoogle Scholar
  53. Roujean JL, Leroy M, Deschamps PY (1992) A bidirectional reflectance model of the earth’s surface for the correction of remote sensing data. J Geophys Res 97:20455–20468CrossRefGoogle Scholar
  54. Rutan D, Charlock T, Rose F, Kato S, Zentz S, Coleman L (2006) Global surface albedo from CERES/TERRA surface and atmospheric radiation budget (SARB) data product. In: Proceedings of 12th conference on atmospheric radiation (AMS). MadisonGoogle Scholar
  55. Saunders RW (1990) The determination of broad band surface albedo from AVHRR visible and near-infrared radiances. Int J Remote Sens 11:49–67CrossRefGoogle Scholar
  56. Schaaf C, Gao F, Strahler A, Lucht W, Li X, Tsung T, Strugll N, Zhang X, Jin Y, Muller P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, d’Entremont R, Hu B, Liang S, Privette J, Roy D (2002) First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens Environ 83:135–148CrossRefGoogle Scholar
  57. Schaaf C, Martonchik J, Pinty B, Govaerts Y, Gao F, Lattanzio A, Liu J, Strahler A, Taberner M (2008) Retrieval of surface albedo from satellite sensors. In: Liang S (ed) Advances in land remote sensing: system, modelling, inversion and application, Springer, Heidelberg, pp 219–243Google Scholar
  58. Strahler A, Muller J, Lucht W, Schaaf C, Tsang T, Gao F, Li X, Lewis P, Barnsley M (1999) MODIS BRDF/albedo product: algorithm theoretical basis document version 5.0. MODIS documentationGoogle Scholar
  59. Stroeve J (2001) Assessment of Greenland albedo variability from the advanced very high resolution radiometer polar pathfinder data set. J Geophys Research-Atmos 106:33989–34006CrossRefGoogle Scholar
  60. Stroeve J, Box J, Gao F, Liang S, Nolin A, Schaaf C (2005) Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sens Environ 94:46–60CrossRefGoogle Scholar
  61. Strugnell NC, Lucht W, Schaaf C (2001) A global albedo data set derived from AVHRR data for use in climate simulations. Geophys Res Lett 28:191–194CrossRefGoogle Scholar
  62. Trishchenko AP, Luo Y, Khlopenkov KV, Wang S (2008) A method to derive the multispectral surface albedo consistent with MODIS from historical AVHRR and VGT satellite data. J Appl Meteorol Climatol 47:1199–1221CrossRefGoogle Scholar
  63. van Leeuwen W, Roujean J (2002) Land surface albedo from the synergistic use of polar (EPS) and geo-stationary (MSG) observing systems: an assessment of physical uncertainties. Remote Sens Environ 81:273–289CrossRefGoogle Scholar
  64. Vermote E, Tanré D, Deuzé J, Herman M, Morcrette J (1997) Second simulation of the satellite signal in the solar spectrum(6S), 6S User guide version 3Google Scholar
  65. Vermote EF, El Saleous NZ, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens Environ 83:97–111Google Scholar
  66. Wanner W, Li X, Strahler A (1995) On the derivation of kernels for kernel-driven models of bidirectional reflectance. J Geophys Res 100:21077–21090CrossRefGoogle Scholar
  67. Weiss M, Baret F, Leroy M, Begue A, Hautecoeur O, Santer R (1999) Hemispherical reflectance and albedo estimates from the accumulation of across-track sun-synchronous satellite data. J Geophys Research-Atmos 104:22221–22232CrossRefGoogle Scholar
  68. Zhang X, Liang S, Wang K, Li L, Gui S (2010) Analysis of global land surface shortwave broadband albedo from multiple data sources. IEEE J Sel Top Appl Earth Obs Remote Sens 3:296–305CrossRefGoogle Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Shunlin Liang
    • 2
    • 1
    Email author
  • Xiaotong Zhang
    • 2
  • Zhiqiang Xiao
    • 3
  • Jie Cheng
    • 2
  • Qiang Liu
    • 2
  • Xiang Zhao
    • 2
  1. 1.Department of Geographical SciencesUniversity of MarylandCollege ParkUSA
  2. 2.State Key Laboratory of Remote Sensing Science and College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingPeople’s Republic of China
  3. 3.State Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal UniversityBeijingPeople’s Republic of China

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