The Evolution of U.S. Moderate Resolution Optical Land Remote Sensing from AVHRR to VIIRS

  • Christopher O. Justice
  • Eric Vermote
  • Jeff Privette
  • Alain Sei
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 11)


Moderate resolution satellite data have become an integral part of land remote sensing, which provide observations to support global and climate change research and applications of societal benefit. Starting with the NOAA Advanced Very High Resolution Radiometer (AVHRR), daily data analysis in the time-domain provided important new insights in vegetation studies at the global scale. Combining the temporal characteristics of the AVHRR and extending the spectral characteristics of the Landsat-5 Thematic Mapper, the NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) instruments launched a new era in land remote sensing. The Terra and Aqua MODIS instruments now provide science quality, moderate resolution data with spatial resolutions from 250 m to 1 km. The next generation U.S. moderate resolution sensor, the Visible/Infrared Imager/Radiometer Suite (VIIRS), is scheduled to fly on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), expected to launch in October 2011, and subsequently on successive NPOESS platforms. The VIIRS instrument will transition much of the capability of the experimental MODIS instruments into the operational domain, a critical step for the land remote sensing community. This chapter provides the background to the current state of moderate resolution land remote sensing, and a description of the VIIRS instrument and the associated Environmental Data Records for the land surface. The VIIRS sensor and product similarities and differences with MODIS are described. The challenges in meeting the future needs for moderate resolution land remote sensing are discussed.


Normalize Difference Vegetation Index Land Surface Temperature Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer Enhance Vegetation Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Barnes WL, Pagano TS, Salomonson VV (1998) Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS AM1. IEEE Trans Geosci Remote Sens 36:1088–1100ADSCrossRefGoogle Scholar
  2. CCSP (2003) Strategic plan for the U.S. Climate Change Science Program. U.S. Climate Change Science Program Office, Washington DC, p 202Google Scholar
  3. Cracknell AP (2001) The exciting and totally unanticipated success of the AVHRR in applications for which it was never intended. Adv Space Res 28:233–240ADSCrossRefGoogle Scholar
  4. Duggin MJ, Piwinski D (1984) Recorded radiance indices for vegetation monitoring using NOAA AVHRR data; atmospheric and other effects in multi-temporal data sets. Appl Opt 23:2620ADSCrossRefGoogle Scholar
  5. El Saleous NZ, Vermote EF, Justice CO, Townshend JRG, Tucker CJ, Goward SN (2000) Improvements in the global biospheric record from the Advanced Very High Resolution Radiometer (AVHRR). Int J Remote Sens 21(6):1251–1277CrossRefGoogle Scholar
  6. GCOS (2004) Implementation plan for the Global Observing System for Climate in support of UNFCCC. GCOS Report 92, WMO TD 1219Google Scholar
  7. Giglio L, Kendall JD (2001) Application of the Dozier retrieval to wildfire characterization: a sensitivity analysis. Remote Sens Environ 77:34–49CrossRefGoogle Scholar
  8. Giglio L, Descloitres J, Justice CO, Kaufman Y (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282CrossRefGoogle Scholar
  9. Giglio L, Csiszar I, and Justice CO (2006) Global distribution and seasonality of active fires as observed with the Terra and Aqua MODIS sensors. J Geophys Res 111:G02016, doi:10.1029/2005JG000142CrossRefGoogle Scholar
  10. Guenther B, Xiong X, Salomonson VV, Barnes WL, Young J (2002) On-orbit performance of the Earth Observing System Moderate Resolution Imaging Spectroradiometer; first year of data. Remote Sens Environ 83:16–30CrossRefGoogle Scholar
  11. Gutman GG, Ignatov A (1995) Global land monitoring from AVHRR: potential and limitations. Int J Remote Sens 16:2301–2309CrossRefGoogle Scholar
  12. Holben BN (1986) Characteristics of maximum value composite images from temporal AVHRR data. Int J Remote Sens 7:1417–1434CrossRefGoogle Scholar
  13. Huete, AR, Miura T, Kim Y, Didan K, Privette J (2006) Assessments of multisensor vegetation index dependencies with hyperspectral and tower flux data. In: Gao W, Ustin SL (eds) Remote sensing and modeling of ecosystems for sustainability III, Proceedings of SPIE, 6298, 1–14 0277–786X/06/$15 doi: 10.1117/12.681382Google Scholar
  14. Integrated Program Office (IPO) (2000) Visible/Infrared Image Radiometer Suite (VIIRS) Sensor Requirements Document (SRD) for National Polar-orbiting Operational Environmental Satellite System (NPOESS) Spacecraft and Sensors. Version 2, Rev. D, Silver Spring, Maryland, 20910Google Scholar
  15. Jacobowitz H (ed) (1997) Climate measurement requirements for the National Polar Orbiting Environmental Satellite System (NPOESS). Workshop Report, February 27–29, (1996) University of Maryland, College Park, NOAA, 71Google Scholar
  16. Justice CO, Eck T, Holben BN, Tanre D (1991) The effect of water vapor on the NDVI derived for the Sahelian region from NOAA-AVHRR data. Int J Remote Sens 12(6):1165–1188CrossRefGoogle Scholar
  17. Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Wolfe R, Knyazikhin Y, Running SW, Nemani RR, Wan Z, Huete AR, van Leeuwen W, Giglio RE, Muller J-P, Lewis P, Barnsley MJ (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36(4):1228–1249ADSCrossRefGoogle Scholar
  18. Justice CO, Townshend JRG, Holben BN, Tucker CJ (1985) Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens 6(8):1272–1318CrossRefGoogle Scholar
  19. Justice CO, Eck T, Holben BN, Tanre D (1991) The effect of water vapor on the NDVI derived for the Sahelian region from NOAA-AVHRR data. Int J Remote Sens 12(6):1165–1188CrossRefGoogle Scholar
  20. Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, El Saleous N, Roy DP, Morisette JT (2002a) An overview of MODIS Land data processing and product status. Remote Sens Environ 83(1–2):3–15CrossRefGoogle Scholar
  21. Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002b) The MODIS fire products. Remote Sens Environ 83(1–2):244–262CrossRefGoogle Scholar
  22. Justice CO, Smith R, Gill M, Csiszar I (2003) Satellite-based fire monitoring: current capabilities and future directions. Int J Wildland Fire 12:247–258CrossRefGoogle Scholar
  23. Kaufman YJ, Tanré D, Remer L, Vermote EF, Chu A, Holben BN (1997a) Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J Geophys Res 102(D14):17051–17067ADSCrossRefGoogle Scholar
  24. Kaufman YJ, Wald AE, Remer LA, Gao B, Li R, Flynn L (1997b) The MODIS 2.1-µm channel – correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans Geosci Remote Sens 35(5):1286–1298ADSCrossRefGoogle Scholar
  25. Kaufman RK, Zhou L, Knyazikhin Y, Shabanov V, Myneni RB, Tucker CJ (2000) Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans Geosci Remote Sens 38:2484–2597CrossRefGoogle Scholar
  26. Kotchenova SY, Vermote EF, Matarrese R, Klemm F (2006) Validation of a new vector version of the 6S radiative transfer code for atmospheric correction of MODIS data: Part I – Path Radiance. Appl Opt 45(26):6762–6774ADSCrossRefGoogle Scholar
  27. Kiran Chand TR, Badarinath KVS, Krishna Prasad V, Murthy MSR, Elvidge CD, Tuttle BT (2006) Monitoring forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System nighttime satellite data. Remote Sens Environ 103(2):165–178CrossRefGoogle Scholar
  28. Lee TE, Miller SD, Turk FJ, Schueler C, Julian R, Deyo S, Dills P, Wang S (2006) The NPOESS VIIRS day/night visible sensor. Bull Amer Meteorol Soc 87(2):191–199CrossRefGoogle Scholar
  29. Masuoka E, Wolfe RE, Teague M, Saleous N, Devadiga S, Morisette J, Sinno S, Justice CO, Roy DP MODIS Land products generation, quality assurance and validation (Chapter 22 in this volume)Google Scholar
  30. Morisette JT, Privette JL, Justice CO (2002) A framework for the validation of MODIS land products. Remote Sens Environ 83(1–2):77–96CrossRefGoogle Scholar
  31. Muirhead K, Cracknell AP (1985) Straw burning over Great Britain detected by AVHRR. Int J Remote Sens 6(5):827–833CrossRefGoogle Scholar
  32. Murphy RE (2006) The NPOESS preparatory project. In: Qu JJ et al (eds) Earth science satellite remote sensing Vol. 1: Science and Instruments, Chapter 10, pp 183–198. Tsinghua University Press, and Springer, ISBN: 978-3-540-35837-4Google Scholar
  33. Murphy RE, Ardanuy P, De Luccia FJ, Clement JE, Schueler CF (2006) The visible infrared imaging radiometer suite. In Qu JJ et al (eds) Earth science satellite remote sensing vol. 1: Science and instruments, Chapter 3, pp 33–49, Tsinghua University Press, and Springer, ISBN: 978-3-540-35837-4Google Scholar
  34. Murphy RE, Barnes WL, Lyapustin AI, Privette J, Welsch C, De Luccia F, Swenson H, Schueler CF, Ardanuy PE, Kealey PSM (2001) Using VIIRS to provide data continuity with MODIS. Proc IEEE Int Geosci Remote Sens Symp (IGARSS ‘01) 3:1212–1214Google Scholar
  35. National Environmental Satellite, Data, and Information Service (NESDIS) (2005) Comprehensive Large Array-data Stewardship System (CLASS): Master Project Management Plan, CLASS-1028-CLS-PLN-MPMP. Available at
  36. National Research Council (NRC) (2000) Issues in the integration of research and operational satellite systems for climate research. Part I: Science and Design. National Academies Press, Washington DC, p 124Google Scholar
  37. National Research Council (NRC) (2007) Earth Science and Applications from Space: National imperatives for the next decade and beyond. National Academies Press, Washington DC, p 418Google Scholar
  38. Nickeson JE, Morisette JT, Privette JL, Justice CO, Wickland DE (2007) Coordinating earth observation system land validation. EOS Trans 88:81–82Google Scholar
  39. Norwine J, Greegor DH (1983) Vegetation classification based on Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. Remote Sens Environ 134:69–87CrossRefGoogle Scholar
  40. Pinheiro ACT, Privette JL, Bates JJ, Pedelty J (2005) Satellite retrieval of land surface temperature: challenges and opportunities. 20th Conference on Climate Variability and Change, American Meteorological SocietyGoogle Scholar
  41. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufman Publishers Inc., San Mateo, CAGoogle Scholar
  42. Roger JC, Vermote EF (1998) A method to retrieve the reflectivity signature at 3.75 μm from AVHRR data. Remote Sens Environ 64:103–114Google Scholar
  43. Roy DP, Borak JS, Devadiga S, Wolfe RE, Descloitres J (2002) The MODIS land product quality assessment approach. Remote Sens Environ 83:62–76Google Scholar
  44. Running SW, Justice CO, Salomonson VV, Hall D, Barker J, Kaufman YJ, Strahler AR, Muller J-P, Vanderbilt V, Wan ZM, Teillet P, Carneggie D (1994) Terrestrial remote sensing science and algorithms planned for the MODIS-EOS. Int J Remote Sens 15(17):3587–3620CrossRefGoogle Scholar
  45. Salomonson VV, Barnes WL, Maymon PW, Montgomery HE, Ostrow H (1989) MODIS: advanced facility instrument for studies of the earth as a system. IEEE Trans Geosci Remote Sens 27:145–153ADSCrossRefGoogle Scholar
  46. Salomonson VV, Barnes, W, Masuoka EJ (2006) Introduction to MODIS and an overview of associated activities. In: Qu JJ et al (eds) Earth science satellite remote sensing 1:Science and instruments, Chapter 2, pp 12–31, Tsinghua University Press, and Springer, ISBN: 978-3-540-35837-4Google Scholar
  47. Sei A (2006) Extension of Chandrasekhar’s formula to a homogeneous non-Lambertian surface and comparison with the 6S formulation. Appl Opt 45:1010–1022ADSCrossRefGoogle Scholar
  48. Schaaf CB, Gao F, Strahler AH, Lucht W, Li X, Tsang T, Strugnell N, Zhang X, Jin Y, Muller J-P, Lewis PE, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, dEntremont RP, Hu B, Liang S, Privette J, Roy DP (2002) First operational BRDF, Albedo and Nadir reflectance products from MODIS. Remote Sens Environ 83:135–148CrossRefGoogle Scholar
  49. Small C, Pozzi F, Elvidge CD (2005) Spatial analysis of global urban extent from DMSP-OLS nighttime lights. Remote Sens Environ 96:277–291CrossRefGoogle Scholar
  50. Smith GM, Curran PJ (1996) The signal-to-noise ratio (SNR) required for the estimation of foliar biochemical concentrations. Int J Remote Sens 17:1031–1058CrossRefGoogle Scholar
  51. Townshend JRG, Justice CO (1990) The spatial variation of vegetation changes at very coarse scales. Int J Remote Sens 11(1):149–157CrossRefGoogle Scholar
  52. Townshend JRG, Justice CO, Skole D, Malingreau JP, Cihlar J, Teilliet P, Sadowski F, Ruttenberg S (1994) The 1-km resolution global data set: needs of the International Geosphere Biosphere Programme. Int J Remote Sens 15(17):3417–3442CrossRefGoogle Scholar
  53. Townshend JRG, Justice CO, Skole DL, Belward A, Janetos A, Gunawan I, Goldammer J, Lee B (2004) Meeting the goals of GOFC: an evaluation of progress and steps for the future. In Gutman G et al (eds) Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface. Kluwer Academic Publishers, Dordrecht, The NetherlandsGoogle Scholar
  54. Townshend JRG, Latham J, Justice CO, Janetos A, Conant R, Arino O, Balstad R, Belward A, Feuquay J, Liu J, Ojima D, Schmullius C, Singh A, Tschirley J International coordination of satellite land observations: integrated observations of the land (Chapter 36 in this volume)Google Scholar
  55. Townshend JRG, Justice CO (2002) Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sens Environ 83(1–2):351–359CrossRefGoogle Scholar
  56. Townshend JRG, Justice CO, Kalb V (1987) Characterization and classification of South American land cover types using satellite data. Int J Remote Sens 8(8):1189–1207CrossRefGoogle Scholar
  57. Townshend JRG, Justice CO, Li W, Gurney C, McManus J (1991) Global land classification by remote sensing: present capabilities and future prospects. Remote Sens Environ 35:243–256CrossRefGoogle Scholar
  58. Tucker CJ (1978) A comparison of satellite sensor bands for vegetation monitoring. Photogramm Eng Remote Sensing 44(11):1169–1180Google Scholar
  59. Tucker CJ, Vanpraet C, Boerwinkel E, Gaston A (1983) Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sens Environ 13:461–474CrossRefGoogle Scholar
  60. Tucker CJ, Brown ME, Pinzon JE, Slayback DA, Mahoney R, Saleous NE, Vermote EF (2005) An extended AVHRR 8-km NDVI dataset comparable with MODIS and SPOT Vegetation NDVI data. Int J Remote Sens 26:4485–4498CrossRefGoogle Scholar
  61. Vermote EF, Kaufman YJ (1995) Absolute calibration of AVHRR visible and near infrared channels using ocean and cloud views. Int J Remote Sens 16(13):2317–2340CrossRefGoogle Scholar
  62. Vermote EF, El Saleous N, Justice CO, Kaufman YK, Privette JL, Remer L, Roger JC, Tanre D (1997) Atmospheric correction of visible to middle infrared EOS MODIS data over land surfaces: background, operational algorithm and validation. J Geophys Res 102(D14):17131–17141ADSCrossRefGoogle Scholar
  63. Wolfe R, Nishihama M, Fleig AJ, Kuyper J, Roy DP, Storey JC, Patt F (2002) Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens Environ 83:31–49CrossRefGoogle Scholar
  64. Xiong X, Barnes W (2006) MODIS Calibration and Characterization. In: Qu JJ et al (eds) Earth science satellite remote sensing Vol. II: data, computational processing and tools, Chapter 5, pp 77–97, Tsinghua University Press, and Springer, ISBN: 978-3-540-35837-4Google Scholar
  65. Yu Y, Privette JL, Pinheiro AC (2005) Analysis of the NPOESS VIIRS land surface temperature algorithm using MODIS data. IEEE Trans Geophys Remote Sens 43(10):2340–2350. doi:10.1109/TGRS.2005.856114ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Christopher O. Justice
    • 1
  • Eric Vermote
  • Jeff Privette
  • Alain Sei
  1. 1.Department of GeographyUniversity of MarylandCollege ParkUSA

Personalised recommendations