Exploiting satellite observations for global surface albedo trends monitoring

  • Nektarios ChrysoulakisEmail author
  • Zina Mitraka
  • Noel Gorelick
Original Paper


Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.


Authors contributions

Individual contribution of each co-author to the reported research: N. C. contributed to data analysis and wrote the paper; Z. M. developed the GEE codes for the MODIS BRDF products processing, performed the statistical analysis and reviewed the paper; N. G. ran the developed codes for the whole MODIS archive globally, contributed to data analysis and reviewed the paper.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

Supplementary material

704_2018_2663_MOESM1_ESM.pdf (84 kb)
ESM 1 (PDF 83 kb)


  1. Benas N, Chrysoulakis N (2015) Estimation of the land surface albedo changes in the broader Mediterranean area, based on 12 years satellite observations. Remote Sens 7:16150–16163CrossRefGoogle Scholar
  2. Betts RA (2000) Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408:187–190CrossRefGoogle Scholar
  3. Cescatti A, Marcolla B, Santhana Vannan SK, Pan JY, Román 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
  4. Chen X, Liang S, Cao Y, He T, Wang D (2015) Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001–2014. Sci Rep 5:16820CrossRefGoogle Scholar
  5. Climate Council of Australia (2015) Thirsty country: climate change and drought in Australia. Published by the Climate Council of Australia Limited. ISBN: 978-0-9942453-8-0Google Scholar
  6. Colditz RR, Ressl RA, Bonilla-Moheno M (2015) Trends in 15-year MODIS NDVI time series for Mexico. Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 8th International Workshop on the, Annecy, pp. 1–4Google Scholar
  7. Disney M, Lewis P, Thackrah G, Quaife T, Barnsley M (2004) Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales. Int J Remote Sens 25:5297–5317CrossRefGoogle Scholar
  8. Dole R, Hoerling M, Perlwitz J, Eischeid J, Pegion P, Zhang T, Quan XW, Xu T, Murray D (2011) Was there a basis for anticipating the 2010 Russian heat wave? Geophys Res Lett 38:L06702CrossRefGoogle Scholar
  9. Dong B, Sutton R (2015) Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall. Nat Clim Chang 5:757–760CrossRefGoogle Scholar
  10. Dorigo W, de Jeu R, Chung D, Parinussa R, Liu Y, Wagner W, Fernandez-Prieto D (2012) Evaluating global trends (1988–2010) in harmonized multi-satellite surface soil moisture. Geophys Res Lett 39:L18405CrossRefGoogle Scholar
  11. Godinho S, Gil A, Guiomar N, Costa MJ, Neves N (2016) Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach. Appl Geogr 74:84–94CrossRefGoogle Scholar
  12. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geosptial analysis for everyone. Remote Sens Environ 202:18–27CrossRefGoogle Scholar
  13. Hall A (2004) The role of surface albedo feedback in climate. J Clim 17:1550–1568CrossRefGoogle Scholar
  14. Henderson-Sellers A, Wilson MF (1983) Surface albedo data for climatic modeling. Rev Geophys 21:1743–1778CrossRefGoogle Scholar
  15. Hoag H (2015) How cities can beat the heat. Nature 524:402–404CrossRefGoogle Scholar
  16. Hollander M, Wolfe DA, Chicken E (2015) Nonparametric statistical methods. John Wiley & Sons, Inc., HobokenCrossRefGoogle Scholar
  17. Jin Y, Schaaf CB, Woodcock CE, Gao F, Li X, Strahler AH et al (2003) Consistency of MODIS surface BRDF/Albedo retrievals: 2. Validation. J Geophys Res 108:4159CrossRefGoogle Scholar
  18. Knobelspiesse KD, Cairns B, Schmid B, Román OM, Schaaf BC (2008) Surface BRDF estimation from an aircraft compared to MODIS and ground estimates at the Southern Great Plains site. J Geophys Res 113:D20105CrossRefGoogle Scholar
  19. Koutsias N, Pleniou M, Mallinis G, Nioti F, Sifakis NI (2013) A rule-based semi-automatic method to map burned areas: exploring the USGS historical Landsat archives to reconstruct recent fire history. Int J Remote Sens 34:7049–7068CrossRefGoogle Scholar
  20. Lawrence D, Vandecar K (2014) Effects of tropical deforestation on climate and agriculture. Nat Clim Chang 5:27–36CrossRefGoogle Scholar
  21. Levy RC, Remer LA, Kleidman RG, Mattoo S, Ichoku C, Kahn R, Eck TF (2010) Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos Chem Phys 10:10399–10420CrossRefGoogle Scholar
  22. Liang S, Fang H, Chen M, Walthall C, Daughtry C, Morisette J et al (2002) Validating MODIS land surface reflectance and albedo products: methods and preliminary results. Remote Sens Environ 83:149–162CrossRefGoogle Scholar
  23. Liang S, Zhao X, Liu S, Yuan W, Cheng X, Xiao Z, Zhang X, Liu Q, Cheng J, Tang H, Qu Y, Bo Y, Qu Y, Ren H, Yu K, Townshend J (2013) A long-term global land surface satellite (glass) data-set for environmental studies. Int J Digital Earth 6:5–33CrossRefGoogle Scholar
  24. Liu J, Schaaf C, Strahler A, Jiao Z, Shuai Y, Zhang Q et al (2009) Validation of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo retrieval algorithm: dependence of albedo on solar zenith angle. J Geophys Res 114:D01106CrossRefGoogle Scholar
  25. 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
  26. Lyapustin A, Wang Y, Laszlo I, Kahn R, Korkin S, Remer L, Levy R, Reid JS (2011) Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. J Geophys Res 116:D03211Google Scholar
  27. Lyons EA, Jin Y, Randerson JT (2008) Changes in surface albedo after fire in boreal forest ecosystems of interior Alaska assessed using MODIS satellite observations. J Geophys Res 113:G02012Google Scholar
  28. Maidment RI, Allan RP, Black E (2015) Recent observed and simulated changes in precipitation over Africa. Geophys Res Lett 42:8155–8164CrossRefGoogle Scholar
  29. Moody EG, King MD, Schaaf CB, Platnick S (2008) MODIS-derived spatially complete surface albedo products: spatial and temporal pixel distribution and zonal averages. J Appl Meteorol Climatol 47:2879–2894CrossRefGoogle Scholar
  30. Moustafa SE, Rennermalm AK, Román MO, Wang Z, Schaaf CB, Smith LC, Koenig LS, Erb A (2017) Evaluation of satellite remote sensing albedo retrievals over the ablation area of the southwestern Greenland ice sheet. Remote Sens Environ 198:115–125CrossRefGoogle Scholar
  31. N’Datchoh ET, Konaré A, Diedhiou A, Diawara A, Quansah E, Assamoi P (2015) Effects of climate variability on savannah fire regimes in West Africa. Earth Syst Dynam 6:161–174CrossRefGoogle Scholar
  32. Pepin NC et al (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5:424–430CrossRefGoogle Scholar
  33. Román MO, Schaaf CB, Woodcock CE, Strahler AH, Yang X, Braswell RH, Curtis PS, Davis KJ, Dragoni D, Goulden ML (2009) The MODIS (Collection V005) BRDF/albedo product: assessment of spatial representativeness over forested landscapes. Remote Sens Environ 113:2476–2498CrossRefGoogle Scholar
  34. Román MO, Schaaf CB, Lewis P, Gao F, Anderson GP, Privette JL, Strahler AH, Woodcock CE, Barnsley M (2010) Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. Remote Sens Environ 114:738–760CrossRefGoogle Scholar
  35. Salomon JG, Schaaf CB, Strahler AH, Gao F, Jin Y (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 44:1555–1565CrossRefGoogle Scholar
  36. Sampaio G, Nobre C, Costa MH, Satyamurty P, Soares-Filho BS, Cardoso M (2007) Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion. Geophys Res Lett 34:L17709CrossRefGoogle Scholar
  37. Schaaf C et al (2002) First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sens Environ 83:135–148CrossRefGoogle Scholar
  38. Schaepman-Strub G, Schaepman ME, Painter TH, Dangel S, Martonchik JV (2006) Reflectance quantities in optical remote sensing-definitions and case studies. Remote Sens Environ 103:27–42CrossRefGoogle Scholar
  39. Scherler D, Bookhagen B, Strecker MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nat Geosci 4:156–159CrossRefGoogle Scholar
  40. Shuai Y, Schaaf CB, Strahler AH, Liu J, Jiao Z (2008) Quality assessment of BRDF/albedo retrievals in MODIS operational system. Geophys Res Lett 35:L05407CrossRefGoogle Scholar
  41. Ummenhofer CC, Sen Gupta A, England MH, Reason CJC (2009a) Contributions of Indian Ocean sea surface temperatures to enhanced East African rainfall. J Clim 22:993–1013CrossRefGoogle Scholar
  42. Ummenhofer CC, England MH, Mclntosh PC, Meyeers GA, Pook MJ, Risbey JS, Sen Gupta A, Taschetto AS (2009b) What causes southeast Australia’s worst droughts? Geophys Res Lett 36:1–5CrossRefGoogle Scholar
  43. Van Dijk AIJM, Beck HE, Crosbie RS, De Jeu RAM, Liu YY, Podger GM, Timbal B, Viney NR (2013) The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour Res 49:1040–1057CrossRefGoogle Scholar
  44. Wang KC, Liu JM, Zhou XJ, Sparrow M, Ma M, Sun Z et al (2004) Validation of the MODIS global land surface albedo product using groundmeasurements in a semidesert region on the Tibetan Plateau. J Geophys Res 109:D05107Google Scholar
  45. Wang Z, Barlage M, Zeng XB, Dickinson RE, Schaaf CB (2005) The solar zenith angle dependence of desert albedo. Geophys Res Lett 32:L05403CrossRefGoogle Scholar
  46. Wang K, Liang S, Schaaf CB, Strahler AH (2010) Evaluation of Moderate Resolution Imaging Spectroradiometer land surface visible and shortwave albedo products at FLUXNET sites. J Geophys Res 115:D17107CrossRefGoogle Scholar
  47. Wang Z, Schaaf CB, Chopping MJ, Strahler AH, Wang J, Román MO, Rocha AV, Woodcock CE, Shuai Y (2012) Evaluation of Moderate-resolution Imaging Spectroradiometer (MODIS) snowalbedo product (MCD43A) over tundra. Remote Sens Environ 117:264–280CrossRefGoogle Scholar
  48. Wang B, Liu J, Kim HJ, Webster PJ, Yim SY, Xiang B (2013) Northern Hemisphere summer monsoon intensified by mega-ElNiño/southern oscillation and Atlantic multidecadal oscillation. Proc Natl Acad Sci U S A 110:5347–5352CrossRefGoogle Scholar
  49. Wang Z, Schaaf CB, Strahler AH, Chopping MJ, Román MO Shuai Y, Woodcock CE, Hollinger DY, Fitzjarrald DR (2014) Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods. Remote Sens Environ 140:60–77CrossRefGoogle Scholar
  50. Wu X, Wen J, Xiao Q, Liu Q, Peng J, Dou B, Li X, You D, Tang Y, Liu Q (2016) Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: a case of MODIS albedo products preliminary validation over northern China. Remote Sens Environ 184:25–39CrossRefGoogle Scholar
  51. Xu B, Cao J, Hansen J, Yao T, Joswia DR, Wang N, Wu G, Wang M, Zhao H, Yang W, Liu L, He J (2009) Black soot and the survival of Tibetan glaciers. Proc Natl Acad Sci U S A 106:22114–22118CrossRefGoogle Scholar
  52. 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 Observ Remote Sens 3:296–305CrossRefGoogle Scholar
  53. Zhao M, Running W (2010) Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329:940–943CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Foundation for Research and Technology HellasInstitute of Applied and Computational Mathematics, Remote Sensing LabHeraklionGreece
  2. 2.Google Inc.ZürichSwitzerland

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