Advertisement

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

  • Frédéric Jacob
  • Thomas Schmugge
  • Albert Olioso
  • Andrew French
  • Dominique Courault
  • Kenta Ogawa
  • Francois Petitcolin
  • Ghani Chehbouni
  • Ana Pinheiro
  • Jeffrey Privette

Thermal Infra Red (TIR) Remote sensing allows spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill-posed problem, with several parameters to be constrained from few information.

Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales.

Keywords

Brightness Temperature Radiative Transfer Equation Forest Meteorol View Zenith Angle Vegetation Temperature 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Romanov P, Tarpley D, (1993) Automated monitoring of snow cover over South America using GOES imager data. Int. J. Remote Sens. 24(5):1119–1125Google Scholar
  2. 2.
    Kay J, Gillespie A, Hansen G, Pettit E, (2003) Spatial relationships between snow contaminant content, grain size, and surface temperature from multispectral images of Mt. Rainier, Washington, DC (USA). Remote Sens. Environ. 86(2):216–231Google Scholar
  3. 3.
    Aires F, Chédin A, Scott N, Rossow W., (2002) A regularized neural net approach for retrieval of atmospheric and surface temperatures with the IASI instrument. J. Appl. Meteorol. 41:144–159Google Scholar
  4. 4.
    Aires F, Rossow W, Chédin A, Scott N, (2002) Remote sensing from the infrared atmospheric sounding interferometer instrument 2. Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles. J. Geophys. Res. 107(D22):4620Google Scholar
  5. 5.
    Prigent C, Aires F, Rossow W, (2003) Land surface skin temperatures from a combined analysis of microwave and infrared satellite observations for an all weather evaluation of the differences between air and skin temperatures. J. Geophys. Res. 108(D10):4310Google Scholar
  6. 6.
    Hong G, Heygster G, Kunzi K, (2005) Intercomparison of deep convective cloud fractions from passive infrared and microwave radiance measurements. IEEE Geosci. Remote Sens. Lett. 2(1):18–24Google Scholar
  7. 7.
    Sunlner M, Michael K, Bradshaw C Hindell M, (2003) Remote sensing of Southern Ocean sea surface temperature: implications for marine biophysical models. Remote Sens. Environ. 84 (2):161–173Google Scholar
  8. 8.
    Gould R, Arnone R, (2004) Temporal and spatial variability of satellite sea surface temperature and ocean colour in the Japan/East Sea. Int. J. Remote Sens. 25(7-8):1377–1382Google Scholar
  9. 9.
    Simpson J, Yueh L, Schmidt A, Harris A, (2005) Analysis of along track scanning radiometer-2 (ATSR-2) data for clouds, glint and sea surface temperature using neural networks. Remote Sens. Environ. 98(2-3):152–181Google Scholar
  10. 10.
    Quattrochi D, Luvall J, (2003) Thermal remote sensing in land surface processes. Taylor & Francis, LondonGoogle Scholar
  11. 11.
    Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y, (2002) The MODIS fire products. Remote Sens. Environ. 83:244-26Google Scholar
  12. 12.
    Petitcolin F, Vermote E, (2002) Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote Sens. Environ. 83:112–134Google Scholar
  13. 13.
    Kasischke ES, Hewson JH, Stocks B, van der Werf G, Randerson J, (2003) The use of ATSR active fire counts for estimating relative patterns of biomass burning a study from the boreal forest region. Geophys. Res. Lett. 30(18):1969, doi:10.1029/2003GL017859Google Scholar
  14. 14.
    Manso-Delgado L, Aguirre-Gomez R, Alvarez R, (2003) Multitemporal analysis of land surface temperature using NOAA-AVHRR: preliminary relationships between climatic anomalies and forest fires. Int. J. Remote Sens. 20:4417–4423Google Scholar
  15. 15.
    Lo C, Quattrochi D, Luvall J, (1997) Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. Int. J. Remote Sens. 18:287–304Google Scholar
  16. 16.
    Lagouarde JP, Moreau P, Irvine M, Bonnefond JM, Voogt JA, Solliec F, (2004) Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France). Remote Sens. Environ. 93:443–462Google Scholar
  17. 17.
    Mestayer PG, Durand P, Augustin P, Basting S, Bonnefond JM, Benech B, Campistron B, Coppalle A, Delbarre H, Dousset B, Drobinski P, Druilhet A, Frejafon E, Grimmond CSB, Groleau D, Irvine M, Kergomard C, Kermadi S, Lagouarde JP, Lemonsu A, Lohou F, Long N, Masson V, Moppert C, Noilhan J, Offerle B, Oke TR, Pigeon G, Puygrenier V, Roberts S, Rosant JM, Saïd F, Salmond J, Tal-baut M, Voogt J, (2005) The urban boundary-layer field campaign in Marseille (UBL/CLU-escompte): set-up and first results. Boundary-Layer Meteorol. 114(2):315–365Google Scholar
  18. 18.
    Olioso A, Taconet O, Ben Mehrez M, (1996) Estimation of heat and mass fluxes from IR brightness temperature. IEEE Trans. Geosci. Remote Sens. 34:1184–1190Google Scholar
  19. 19.
    Norman J, Kustas W, Prueger J, Diak GR, (2000) Surface flux estimation using radiometric temperature: a dual-temperature-difference method to minimize measurement errors. Water Res. Res. 36:2263–2274Google Scholar
  20. 20.
    Chehbouni A, Nouvellon Y, Lhomme J, Watts C, Boulet G, Kerr Y, Moran M, Goodrich D (2001) Estimation of surface sensible heat flux using dual angle observations of radiative surface temperature. Agric. Forest Meteorol. 108:55–65Google Scholar
  21. 21.
    Wassenaar T, Olioso A, Hasager C, Jacob F, Chehbouni A, (2002) Estimation of evapotranspiration over heterogeneous pixels. In: Sobrino J (ed.) Proceedings of the First International Symposium on Recent Advances in Quantitative Remote Sensing, September 2002, Valencia, Spain, pp 458–465Google Scholar
  22. 22.
    Su Z, (2002) The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Sci. Syst. 6:85–99Google Scholar
  23. 23.
    Jacob F, Olioso A, Gu X, Su Z, Seguin B, (2002) Mapping surface fluxes using visible, near infrared, thermal infrared remote sensing data with a spatialized surface energy balance model. Agronomie: Agric. Environ. 22:669–680Google Scholar
  24. 24.
    French A, Jacob F, Anderson M, Kustas W, Timmermans W, Gieske A, Su B, Su H, McCabe M, Li F, Prueger J, Brunsell N, (2005) Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens. Environ. 99:55–65Google Scholar
  25. 25.
    Courault D, Lacarrère P, Clastre P, Lecharpentier P, Jacob F, Marloie O, Prévot L, Olioso A (2003) Estimation of surface fluxes in a small agricultural area using the three-dimensional atmospheric model Meso-NH and remote sensing data. Can. J. Remote Sens. 29:741–754Google Scholar
  26. 26.
    Olioso A, Carlson T, Brisson N, (1996) Simulation of diurnal transpiration and photosynthesis of a water stresses soybean crop. Agric. Forest Meteorol. 81:41–59Google Scholar
  27. 27.
    Moran M, Vidal A, Troufleau D, Qi J, Clarke T Jr., Pinter PJ, Mitchell T, Inoue Y, Nealeg MU, (1997) Combining Multifrequency Microwave and Optical Data for Crop Management. Remote Sens. Environ. 61:96–109Google Scholar
  28. 28.
    Moran M, Inoue Y, Barnes E, (1997) Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management. Remote Sens. Environ. 61:319–346Google Scholar
  29. 29.
    Cayrol P, Kergoat L, Moulin S, Dedieu G, Chehbouni A., (2000) Calibrating a coupled SVAT - vegetation growth model with remotely sensed reflectance and surface temperature. J. Appl. Meteorol. 39:2452–2472Google Scholar
  30. 30.
    Bastiaanssen W, Molden D, Makin I, (2000) Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manag. 46:137–155Google Scholar
  31. 31.
    Droogers P, Bastiaanssen W, (2002) Irrigation performance using hydrological and remote sensing modeling. J. Irrig. Drain. Eng. 128:11–18Google Scholar
  32. 32.
    Hale R, Duchon C, (2003) Use of AVHRR-derived surface temperatures in evaluating a land-atmosphere model. Int. J. Remote Sens. 24:4527–4541Google Scholar
  33. 33.
    Jacobs A, Ronda R, Holtslag A, (2003) Water vapour and carbon dioxide fluxes over bog vegetation. Agric. Forest Meteorol. 116:103–112Google Scholar
  34. 34.
    Schuurmans J, Troch P, Veldhuizen A, Bastiaanssen W, Bierkens M, (2003) Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv. Water Res. 26:151–159Google Scholar
  35. 35.
    Nijbroek R, HoogenBoom G, Jones J, (2003) Optimizing irrigation management for a spatially variable soybean field. Agric. Syst. 76:359–377Google Scholar
  36. 36.
    Panda R, Behera S, Kashyap P, (2003) Effective management of irrigation water for wheat under stress conditions. Agric. Water Manag. 63:37–56Google Scholar
  37. 37.
    Bastiaanssen W, Chandrapala L, (2003) Water balance variability across Sri Lanka for assessing agricultural and environmental water use. Agric. Water Manag. 58:171–192Google Scholar
  38. 38. Demarty J, Ottlé C, Braud I, Olioso A, Frangi JP, Gupta H, Bastidas L, (2005) Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach. Water Resour. Res. 41 (2005) W01011, doi:10.1029/2004WR003695Google Scholar
  39. 39.
    Olioso A, Inoue Y, Ortega-Farias S, Demarty J, Wigneron JP, Braud I, Jacob F, Lecharpentier P, Ottlé C, Calvet JC, Brisson N, (2005) Future directions for advanced evapotranspiration modeling: Assimilation of remote sensing data into crop simulation models and SVAT models. Irrig. Drain. Syst. 19(3-4):355–376Google Scholar
  40. 40.
    Capehart W, Carlson T, (1997) Decoupling of surface and near-surface soil water content: A remote sensing perspective. Water Resour. Res. 33:1383–1395Google Scholar
  41. 41.
    Gillies R, Carlson T, Cui J, Kustas W, Humes K, (1997) Verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Vegetation Index NDVI and surface radiant temperature. Int. J. Remote Sens. 18:3145–3166Google Scholar
  42. 42.
    Inoue Y, Olioso A, Choi W, (2004) Dynamic change of CO2 flux over bare soil field and its relationship with remotely sensed surface temperature. Int. J. Remote Sens. 25(10):1881–1892Google Scholar
  43. 43.
    Coudert B, Ottlé C, Boudevillain B, Demarty J, Guillevic P, (2005) Contribution of thermal infrared remote sensing data in multiobjective calibration of a dual source SVAT model. J. Hydrometeorol. 7:404–420Google Scholar
  44. 44.
    Sobrino J, Raissouni N, (2000) Toward remote sensing methods for land cover dynamic monitoring. Application to Morroco. Int. J. Remote Sens. 21:353–366Google Scholar
  45. 45. Wilber A, Kratz D, Gupta S, (1999) Surface emissivity maps for use in satellite retrievals of longwave radiation. NASA/TP-1999-209362, NASAGoogle Scholar
  46. 46.
    Zhou L, Dickinson R, Ogawa K, Tian Y, Jin M, Schmugge T, (2003) Relations between albe-dos and emissivities from MODIS and ASTER data over North African Desert. Geophys. Res. Lett. 30(20):2026, doi:10.1029/2003GL018069Google Scholar
  47. 47.
    Zhou L, Dickinson R, Tian Y, Jin M, Ogawa K, Yu H, Schmugge T, (2003) A sensitivity study of climate and energy balance simulations with use of satellite derived emissivity data over Northern Africa and the Arabian Peninsula. J. Geophys. Res. 108(D24):4795, doi:10.1029/2003JD004083Google Scholar
  48. 48.
    Jin M, Liang S, (2006) Improving Land surface emissivity parameter of land surface model in GCM. J. Climate 19:2867–2881Google Scholar
  49. 49.
    Wan Z, Wang P, Li X, (2004) Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens. 25(1):61–72Google Scholar
  50. 50.
    Norman J, Becker F, (1995) Terminology in thermal infrared remote sensing of natural surfaces. Remote Sens. Rev. 12:159–173Google Scholar
  51. 51.
    Dash P, G öttsche FM, Olesen FS, Fischer H, (2002) Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. Int. J. Remote Sens. 23(13):2563–2594Google Scholar
  52. 52.
    Ogawa K, Schmugge T, Jacob F, French A, (2003) Estimation of land surface window (8-12 µm) emissivity from multi-spectral thermal infrared remote sensing - a case study in a part of Sahara Desert. Geophys. Res. Lett. 30:1067–1071Google Scholar
  53. 53. Ogawa K, Schmugge T, (2004) Mapping Surface Broadband Emissivity of the Sahara Desert Using ASTER and MODIS Data. Earth Interactions 8 Paper No. 7Google Scholar
  54. 54. Wang K, Wan Z, Wang P, Sparrow M, Liu J, Zhou X, Haginoya S, (2005) Estimation of Surface Long Wave Radiation and Broadband Emissivity Using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Products. J. Geophys. Res. -Atmos. 110(D11, D11109) Paper No. 10.1029/2004JD005566Google Scholar
  55. 55.
    Becker F, Li Z, (1995) Surface temperature and emissivity at various scale: definition, mea-surement and related problem. Remote Sens. Reviews 12:225–253Google Scholar
  56. 56.
    Kustas W, Norman J, Anderson M, French A, (2003) Estimating subpixel surface temper-atures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sens. Environ. 85:429–440Google Scholar
  57. 57.
    Nishida K, Nemani RR, Glassy JM, Running SW, (2003) Development of an Evapotranspiration Index From Aqua/MODIS for Monitoring Surface Moisture Status. IEEE Trans. Geosci. Remote Sens. 41(2):493–501Google Scholar
  58. 58.
    Luquet D, Bégué A, Vidal A, Clouvel P, Dauzat J, Olioso A, Gu X, Yu T, (2003) Using multidirectional thermography to characterize water status of cotton. Remote Sens. Environ. 84:411–421Google Scholar
  59. 59.
    Roerink GJ, Su Z, Menenti M, (2000) S-SEBI: a simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth (B) 25(2):147–157Google Scholar
  60. 60.
    Gomez M, Sobrino J, Olioso A, Jacob F, (2005) Retrieval of evapotranspiration over the Alpilles test site using PolDER and thermal camera data. Remote Sens. Environ. 96:399–408Google Scholar
  61. 61.
    Kustas W, Humes K, Norman J, Moran M, (1996) Single and dual source modeling of surface energy fluxes with radiometric surface temperature. J. Appl. Meteorol. 35:110–121Google Scholar
  62. 62.
    Chehbouni A, Lo Seen D, Njoku E, Monteny B, (1996) Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sens. Environ. 58:177–186Google Scholar
  63. 63.
    Chehbouni A, Lo Seen D, Njoku E, Lhomme JP, Monteny B, Kerr Y, (1997) Estimation of sensible heat flux over sparsely vegetated surfaces. J. Hydrol. 188-189:855–868Google Scholar
  64. 64.
    Habets F, Noilhan J, Golaz C, Goutorbe JP, Lacarrère P, Leblois E, Ledoux E, Martin E, Ottlé C, Vidal-Madjar D, (1999) – The ISBA surface scheme in a macroscale hydro-logical model applied to the Hapex-Mobilhy area. Part I: model and database. J. hydrol. 217:75–96Google Scholar
  65. 65.
    Su Z, Menenti M, Pelgrum H, van den Hurk B, Bastiaanssen W, (1999) Remote sensing of land surface fluxes for updating numerical weather predictions. In: Nieuwenhuis, Vaughan, Molenaar (eds), Operational Remote Sensing for Sustainable Development, Balkema, RotterdamGoogle Scholar
  66. 66.
    Li J, Su Z, van den Hurk B, Menenti M, Moene A, de Bruin H, Baselga Yrisarry J, Ibanez M, Cuesta A, (2003) Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements. Phys. Chem. Earth 28:75–88Google Scholar
  67. 67.
    Demarty J, Ottlé C, Braud I, Olioso A, Frangi JP, Bastidas L, Gupta H, (2004) Using a multiobjective approach to retrieve information on surface properties used in a SVAT model. J. Hydrol. 287:214–236Google Scholar
  68. 68.
    François C, (2002) The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status. Remote Sens. Environ. 80:122–133Google Scholar
  69. 69.
    Braud I, Dantas Antonino A, Vauclin M, Thony J, Ruelle P, (1995) A Simple Soil Plant Atmosphere Transfer model (SiSPAT) development and field verification. J. Hydrol. 166:213–250Google Scholar
  70. 70.
    Boulet G, Chehbouni A, Braud I, Vauclin M, (1999) Mosaic versus dual source approaches for modeling the surface energy balance of a semi-arid land. Hydrol. Earth Sci. Syst. 3(2):247–258Google Scholar
  71. 71.
    Merlin O, Chehbouni A, (2004) Different approaches in estimating heat flux using dual angle observations of radiative surface temperature. Int. J. Remote Sens. 25:275–289Google Scholar
  72. 72.
    Luquet D, Vidal A, Dauzat J, Bégué A, Olioso A, Clouvel P, (2003) Using directional TIR measurements and 3D simulations to assess the limitations and opportunities of water stress indices. Remote Sens. Environ. 90:53–62Google Scholar
  73. 73.
    Yu T, Gu X, Tian T, Legrand M, Baret F, Hanocq JF, Bosseno R, Zhang Y, (2004) Modeling directional brightness temperature over a maize canopy in row structure. IEEE Trans. Geosci. Remote Sens. 42:2290–2304Google Scholar
  74. 74. Verhoef W, Xiao Q, Jia L, Su Z, (2006) Earth observation modeling based on layer scattering matrices. IEEE Trans. Geosci. Remote Sens. (submitted).Google Scholar
  75. 75. Smith J Jr, Ballard, J.R, (1999) Physics Based Modeling and Rendering of Vegetation in the Thermal Infrared. In: 1999 IEEE Workshop on Photometric Modeling for Computer Vision and Graphics 34Google Scholar
  76. 76.
    Guillevic P, Gastellu-Etchegorry JP, Demarty J, Prévot L, (2003) Thermal infrared radiative transfer within three-dimensional vegetation cover. J. Geophys. Res. -Atm. 108(D8):4248, doi:10.1029/2002JD02247Google Scholar
  77. 77.
    Gastellu-Etchegorry JP, Martin E, Gascon F, (2004) DART: a 3D model for simulating satellite images and studying surface radiation budget. Int. J. Remote Sens. 25(1):73–96Google Scholar
  78. 78. Belot A, Gastellu-Etchegorry JP, Perrier A, (2005) DART-EB, a 3-D model of mass and energy transfers in vegetated canopy. Remote Sens. Environ. (submitted)Google Scholar
  79. 79.
    Seguin B, Becker F, Phulpin T, Gu X, Guyot G, Kerr Y, King C, Lagouarde J, Ottlé C, Stoll M, Tabbagh T, Vidal A, (1999) IRSUTE: a minisatellite project for land surface heat flux estimation from field to regional scale. Remote Sens. Environ. 68:357–369Google Scholar
  80. 80.
    Hernandez-Baquero ED, (2000) Characterization of the Earth’s Surface and Atmosphere from Multispectral and Hyperspectral Thermal Imagery. Ph.D. thesis, Air Force Institute of Technology, Wright-Patterson, AFB, OH, 270pp.Google Scholar
  81. 81.
    French A, Norman J, Anderson M, (2003) A simple and fast atmospheric correction for space-borne remote sensing of surface temperature. Remote Sens. Environ. 87:326–333Google Scholar
  82. 82.
    Sobrino J, Romaguera M, (2004) Land surface temperature retrieval from MSG1-SEVIRI data. Remote Sens. Environ. 92:247–254Google Scholar
  83. 83.
    Petitcolin F, Nerry F, Stoll MP, (2002) Mapping temperature independent spectral indice of emissivity and directional emissivity in AVHRR channels 4 and 5. Int. J. Remote Sens. 23:3473–3491Google Scholar
  84. 84.
    Nakajima T, Nakajima T, Nakajima M, Fukushima H, Kuji M, Uchiyama A, Kishino M, (1998) Optimization of the Advanced Earth Observing Satellite (ADEOS) II Global Imager (GLI) channels by use of radiative transfer calculations. Appl. Opt. 37(15):3149–3163Google Scholar
  85. 85.
    Justice C, Vermote E, Townshend J, Defries R, Roy D, Hall D, Salomonson V, Privette J, Riggs G, Strahler A, Lucht W, Myneni R, Knyazikhin Y, Running S, Ne-mani R, Wan Z, Huete A, van Leeuwen W, Wolfe R, Giglio L, Muller JP, Lewis P, Barnsley M, (1998) The MODerate Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36:1228–1249Google Scholar
  86. 86.
    Llewellyn-Jones D, Edwards M, Mutlow C, Birks A, Barton I, Tait H, (2001) AATSR: global-change and surface-temperature measurements from Envisat. Eur Space Agency Bull 105:11–21Google Scholar
  87. 87.
    Goward S, Masek J, Williams D, Irons J, Thompson R, (2001) The Landsat 7 mission -Terrestrial research and applications for the 21st century. Remote Sens. Environ. 78:3–12Google Scholar
  88. 88.
    Yamaguchi Y, Kahle A, Tsu H, Kawakami T, Pniel M, (1998) Overview of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER). IEEE Trans. Geosci. Remote Sens. 36:1282–1289Google Scholar
  89. 89.
    Becker F, Seguin B, Phulpin T, Durpaire J, (1996) IRSUTE, a small satellite for water budget estimate with high resolution infrared imagery. Acta Astronautica 39:883–897Google Scholar
  90. 90. Lagouarde J, (1997) SEXTET: a high resolution and high repetitivity infra red instrument on a mini satellite platform. Proposal to CNES call for ideasGoogle Scholar
  91. 91. Menenti M, Rast M, Baret F, van den Hurk B, Knorr W, Mauser W, Miller J, Moreno J, Schaepman M, Verstraete M.M, (2004) Understanding vegetation response to climate variability from space: recent advances towards the SPECTRA Mission. In: Meynart R (ed) Proceedings of SPIE: Sensors, Systems, and Next-Generation Satellites VII. Vol 5234, pp 76-85Google Scholar
  92. 92.
    Weber PG, Brock BC, Garrett AJ, Smith BW, Borel CC, Clodius WB Bender SC, Kay RR, Decker ML, (1999) MTI Mission Overview. In: Proceedings of SPIE Conference on Imaging Spectrometry V. Vol 3753, pp 340–346Google Scholar
  93. 93. Palluconi F, Meeks G, (1985) Thermal Infrared Multi-spectral Scanner (TIMS): an investigator’s guide to TIMS data. JPL Publications N 85-32Google Scholar
  94. 94.
    Müller A, Gege P, Cocks T, (2001) The airborne imaging spectrometers used in daisex. In: DAISEX Final Results Workshop, ESTEC, Holland, 15-16 March 2001, ESA SP-499, pp3–6Google Scholar
  95. 95.
    King MD, Menzel WP, Grant PS, Myers JS, Arnold GT, Platnick SE, Gumley LE, Tsay SC, Moeller CC, Fitzgerald M, Brown KS, Osterwisch FG, (1996) Airborne scanning spectrometer for remote sensing of cloud, aerosol, water vapor and surface properties. J. Atm Oceanic Technol. 13:777–794Google Scholar
  96. 96.
    Hook S, Myers J, Thome K, Fitzgerald M, Kahle A, (2001) The MODIS/ ASTER airborne simulator (MASTER) - a new instrument for Earth science studies. Remote Sens. Environ. 76:93–102Google Scholar
  97. 97. Prévot L, Baret F, Chanzy A, Olioso A, Wigneron J, Autret H, Baudin F, Besse-moulin P, Bethenod O, Blamont D, Blavoux B, Bonnefond J, Boubkraoui S, Bouman B, Braud I, Bruguier N, Calvet J, Caselles V, Chauki H, Clevers J, Coll C, Company A, Courault D, Dedieu G, Degenne P, Delécolle R, Denis H, Desprats J, Ducros Y, Dyer D, Fies J, Fischer A, Francois C, Gaudu J, Gonzalez E, Gouget R, Gu X, Guérif M, Hanocq J, Hautecoeur J, Haverkamp R, Hobbs S, Jacob F, Jeansoulin R, Jongschaap R, Kerr Y, King C, Laborie P, Lagouarde J, Laques A, Larcena D, Laurent G, Laurent J, Leroy M, McAneney J, Macelloni G, Moulin S, Noilhan J, Ottlé C, Paloscia S, Pampaloni P, Podvin T, Quaracino F, Roujean J, Rozier C, Ruisi R, Susini C, Taconet O, Tallet N, Thony J, Travi Y, Van Leewen H, Vauclin M, Vidal-Madjar D, Vonder O, Weiss M, (1998) Assimilation of multi-sensor and multi-temporal remote sensing data to monitor vegetation and soil: the Alpilles ReSeDA project. In: Tsang L (ed) IGARSS ’98 International Geoscience and Remote Sensing Symposium, IEEE, Institute of Electrical and Electronics Engineers, Piscataway (USA), Sensing and managing the environment. Vol 5, pp 2399-2401Google Scholar
  98. 98.
    Olioso A, Prévot L, Baret F, Chanzy A, Braud I, Autret H, Baudin F, Bessemoulin P, Bethenod O, Blamont D, Blavoux B, Bonnefond J, Boubkraoui S, Bouman B, Bruguier N, Calvet J, Caselles V, Chauki H, Clevers J, Coll C, Company A, Courault D, Dedieu G, Degenne P, Delécolle R, Denis H, Desprats J, Ducros Y, Dyer D, Fies J, Fischer A, Francois C, Gaudu J, Gonzalez E, Gouget R, Gu X, Guérif M, Hanocq J, Hautecoeur J, Haverkamp R, Hobbs S, Jacob F, Jeansoulin R, Jongschaap R, Kerr Y, King C, Laborie P, Lagouarde J, Laques A, Larcena D, Laurent G, Laurent J, Leroy M, McAneney J, Macelloni G, Moulin S, Noilhan J, Ottlé C, Paloscia S, Pampaloni P, Podvin T, Quaracino F, Roujean J, Rozier C, Ruisi R, Susini C, Taconet O, Tallet N, Thony J, Travi Y, Van Leewen H, Vauclin M, Vidal-Madjar D, Vonder O, Weiss M, Wigneron J, (1998) Spatial aspects in the Alpilles-ReSeDA project. In: Marceau D (ed.) Scaling and Modeling in Forestry: Application in Remote Sensing and GIS, Ed. D Marceau, Université de Montréal, Québec pp 92–102Google Scholar
  99. 99.
    Baret F, (2000) ReSeDA, assimilation of multisensor and multitemporal remote sensing data to monitor soil and vegetation functioning. Final Report EC project ENV4CT960326, INRA, France, 59p.Google Scholar
  100. 100.
    National Research Council, (2004) Committee on NASA-NOAA Transition from Research to Operations: Satellite Observations of the Earth’s Environment: Accelerating the Transition of Research to Operations. National Academies Press, Washington, DCGoogle Scholar
  101. 101.
    Yu Y, Privette J, Pinheiro A, (2005) Analysis of the NPOESS VIIRS Land Surface Temperature Algorithm Using MODIS Data. IEEE Trans. Geosci. Remote Sens. 43:2340–2350Google Scholar
  102. 102.
    Vaughan R, Calvin W, Taranik J, (2003) SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping. Remote Sens. Environ. 85:48–63Google Scholar
  103. 103.
    Liu Q, Gu X, Li X, Jacob F, Hanocq JF, (2000) Study on airborne experiment for directional patterns of thermal infrared radiation. Science in China(E) 30:89–97Google Scholar
  104. 104.
    Payan V, Royer A, (2004) Analysis of the Temperature Emissivity Separation (TES) algorithm applicability and sensitivity. Int. J. Remote Sens. 25:15–37Google Scholar
  105. 105.
    Rubio E, Caselles V, Badenas C, (1997) Emissivity measurements of several soils and vegetation types in the 8-14 µm wave band: analysis of two fields method. Remote Sens. Environ. 59:490–521Google Scholar
  106. 106.
    Kant Y, Badarinath K, (2002) Ground-based method for measuring thermal infrared effective emissivity and perspectives on the measurement of land surface temperature from satellite data. Int. J. Remote Sens. 23(11):2179–2191Google Scholar
  107. 107.
    Rubio E, Caselles V, Coll C, Valor E, Sospedra F, (2003) Thermal infrared emissivities of natural surfaces: improvement on the experimental set-up and new measurements. Int. J. Remote Sens. 24(24):5379–5390Google Scholar
  108. 108.
    Li ZL, Zhang R, Sun X, Su H, Tang X, Zhu Z, Sobrino JA, (2004) Experimental system for the study of the directional thermal emission of natural surfaces. Int. J. Remote Sens. 25(1):195–204Google Scholar
  109. 109. Coret L, Briottet X, Kerr Y, Chehbouni G, (2005) Experimental study of directional aggregation over heterogeneous surfaces in the thermal infrared. Remote Sens. Environ. (submitted).Google Scholar
  110. 110.
    Garrat J, Hicks B, (1973) Momentum, heat and water vapor transfer to and from natural and artificial surfaces. Quart. J. Roy. Meteorol. Soc. 99:680–687Google Scholar
  111. 111.
    Ham J, Heilman J, (1991) Aerodynamic land surface resistances affecting energy transport in a sparse crop. Agric. Forest Meteorol. 53:267–284Google Scholar
  112. 112.
    Garrat J, (1978) Transfer characteristics for a heterogeneous surface of large aerodynamic roughness. Quart. J. Roy. Meteorol. Soc. 104:491–502Google Scholar
  113. 113.
    Troufleau D, Lhomme J, Monteny B, Vidal A, (1997) Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation. I. An Experimental analysis of the kB parameter. J. Hydrol. 188-189:815–838Google Scholar
  114. 114.
    Choudhury B, (1989) Estimating evaporation and carbon assimilation using infrared temperature data: vistas in modeling. In: Theory and Applications of Optical Remote Sensing. Wiley Interscience, Paris, France, pp 629–690Google Scholar
  115. 115.
    Norman J, Divakarla M, Goel N, (2000) Algorithms for Extracting Information from Remote Thermal-IR Observations of the Earth’s Surface. Remote Sens. Environ. 51:157–168Google Scholar
  116. 116.
    Martonchik JV, Bruegge CJ, Strahler AH, (2000) A Review of Reflectance Nomenclature for Remote Sensing. Remote Sens. Rev. 19:9–20Google Scholar
  117. 117.
    Li X, Strahler A, Friedl M, (1999) A conceptual model for effective directional emissivity from non-isothermal surfaces. IEEE Trans. Geosci. Remote Sens. 37:2508–2517Google Scholar
  118. 118.
    François C, Ottlé C, Prévot L, (1997) Analytical parameterization of canopy directional emis-sivity and directional radiance in the thermal infrared. Application on the retrieval of soil and foliage temperatures using two directional measurements. Int. J. Remote Sens. 18:2587–2621Google Scholar
  119. 119.
    Chehbouni A, Nouvellon Y, Kerr Y, Moran M, Watts C, Prévot L, Goodrich D, Rambal S, (2001) Directional effect on radiative surface temperature measurements over a semi-arid grassland site. Remote Sens. Environ. 76:360–372Google Scholar
  120. 120.
    Olioso A, Soria G, Sobrino J, Duchemin B, (2006) Evidences of low land surface thermal infrared emissivity in presence of dry vegetation. IEEE Geosci. Remote Sens. Lett. 4(1):112-116Google Scholar
  121. 121.
    Sutherland R, Bartholic J, (1977) Significance of vegetation in interpreting thermal radiation from a terrestrial surface. J. Appl. Meteorol. 16:759–763Google Scholar
  122. 122.
    Colton A, (1996) Effective thermal parameters for a heterogeneous lans surface. Remote Sens. Environ. 57:143–160Google Scholar
  123. 123.
    Barducci A, Pippi Y, (1996) Temperature and emissivity retrieval from remotely sensed images using the “Grey Body Emissivity” method. IEEE Trans. Geosci. Remote Sens. 34:681-695Google Scholar
  124. 124.
    Valor E, Caselles V, (1996) Mapping land surface emissivity from NDVI: application to European, African and South American areas. Remote Sens. Environ. 57:167–184Google Scholar
  125. 125. Sobrino J, S òria G, Prata AJ , (2004) Surface temperature retrieval from Along Track Scanning Radiometer 2 data: Algorithms and validation. J. Geophys. Res. 109D11101, doi:10.1029/2003JD004212Google Scholar
  126. 126.
    S òria G, Sobrino J, (2006) Envisat/AATSR derived land surface temperature over a heterogeneous region. Remote Sens. Environ. 111(4):409–422Google Scholar
  127. 127.
    Chen L, Li ZL, Liu Q, Chen S, Tang Y, Zhong B, (2004) Definition of component effective emissivity for heterogeneous and non-isothermal surfaces and its approximate calculation. Int. J Remote Sens. 25(1):231–244Google Scholar
  128. 128.
    Su L, Li X, Friedl M, Strahler A, Gu X, (2002) A kernel-driven model of effective directional emissivity for non-isothermal surfaces. Prog. Natural Sci. 12(8):603–607Google Scholar
  129. 129.
    Snyder W, Wan Z, (1998) BRDF models to predict spectral reflectance and emissivity in the infrared. IEEE Trans. Geosci. Remote Sens. 36:214–225Google Scholar
  130. 130.
    Roujean J, Leroy M, Deschamps P, (1992) A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. J. Geophys. Res. 97:20455–20468Google Scholar
  131. 131.
    Wanner W, Li X, Strahler A, (1995) On the derivation of kernels for kernel-driven models of bidirectional reflectance. J. Geophys. Res. 100:21077–21089Google Scholar
  132. 132.
    Engelsen O, Pinty B, Verstraete M, Martonchik J, (1996) Parametric bidirectional reflectance factor models : evaluation, improvements and applications. Report EUR16426EN, European Commission, Joint Researches Center, Space Application Institute, ISPRA, ItalyGoogle Scholar
  133. 133.
    Berk A, Bernstein L, Anderson G, Acharya P, Robertson D, Chetwynd J, Adler-Golden S, (1998) MODTRAN cloud and multiple scattering upgrades with application to AVIRIS. Remote Sens. Environ. 65:367–375Google Scholar
  134. 134. Anderson G, Berk A, Acharya P, Matthew M, Bernstein L, Chetwynd J, Dothe H, Adler-Golden S, Ratkowski A, Felde G, Gardner J, Hoke M, Richtsmeier S, Pukall B, Mello J, Jeong L, (2000) MODTRAN 4.0: radiative transfer modeling for remote sensing. In: Algorithms for multispectral, hyperspectral and ultraspectral imagery VI: Proceedings of SPIE , pp 176-183Google Scholar
  135. 135.
    Verhoef, W., (1984) Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 16:125–141Google Scholar
  136. 136.
    Olioso A, (1995) Simulating the relationship between thermal infrared emissivity and the Normalized Difference Vegetation Index. Int. J. Remote Sens. 16:3211–3216Google Scholar
  137. 137.
    Olioso A, Chauki H, Courault D, Wigneron J, (1999) Estimation of evapotranspiration and photo-synthesis by assimilation of remote sensing data into SVAT models. Remote Sens. Environ. 68:341–356Google Scholar
  138. 138.
    Kimes D, (1980) Effects of vegetation canopy structure on remotely sensed canopy temperatures. Remote Sens. Environ. 10:165–174Google Scholar
  139. 139.
    Prévot L, (1985) Modélisation deséchanges radiatifs au sein des couverts végétaux - Application à la télédétection - Validation sur un couvert de Maïs. Ph.D. thesis, p 185, Université Paris VI, Paris, FranceGoogle Scholar
  140. 140.
    Jackson R, Reginato R, Pinter P, (1979) Plant canopy information extraction from compositesite reflectance of row crop. Appl. Opt. 18:3775–3782Google Scholar
  141. 141.
    Kimes D, Kirchner J, (1983) Directional radiometric measurements of row - crop temperatures. Int. J. Remote Sens. 4:299–311Google Scholar
  142. 142.
    Sobrino JA, Caselles V, (1990) Thermal infrared radiance model for interpreting the directional radiometric temperature of a vegetative surface. Remote Sens. Environ. 33:193–199Google Scholar
  143. 143.
    Caselles V, Sobrino JA, Coll C, (1992) A physical model for interpreting the land surface temperature obtained by remote sensors over incomplete canopies. Remote Sens. Environ. 39:203–211Google Scholar
  144. 144.
    Coret L, Briottet X, Kerr Y, Chehbouni G, (2004) Simulation study of view angle effects on thermal infrared measurements over heterogeneous surfaces. IEEE Trans. Geosci. and Remote Sens. 42(3):664–672Google Scholar
  145. 145.
    Du Y, Liu Q, Chen L, Liu Q, Yu T, (2007) Modeling directional brightness temperature of the winter wheat canopy at the ear stage. IEEE Trans. Geosci. Remote Sens. 45(11):3721–37390Google Scholar
  146. 146.
    Norman J, Campbell G, (1983) Application of a plant-environment model to problems in irrigation. In: Hillel D (ed.) Advances in irrigation, Academic Press, New York, pp 155–188Google Scholar
  147. 147.
    Norman J, Arkebauer T, (1991) Predicting canopy light-use efficiency from leaf characteristics. In: Ritchie J, Hanks R (eds), Modeling plant and soil systems, ASA, Madison, WI, pp 125–143Google Scholar
  148. 148.
    Dauzat J, Rapidel B, Berger A, (2001) Simulation of leaf transpiration and sap flow in virtual plants: description of the model and application to a coffee plantation in Costa Rica. Agric. Forest Meteorol. 109:143–160Google Scholar
  149. 149.
    Jia L, (2004) Modeling heat exchanges at the land atmosphere interface using multiangular thermal infrared measurements. Ph.D. thesis, Wageningen University, Wageningen, The NetherlandsGoogle Scholar
  150. 150.
    Su H, Zhang R, Tang XZ, Sun X, (2000) Determination of the effective emissivity for the regular and irregular cavities using Monte-carlo method. Int. J. Remote Sens. 21(11):2313–2319Google Scholar
  151. 151.
    Su L, Li X, Liang S, Strahler A, (2003) Simulation of scaling effects of thermal emission from non-isothermal pixels with the typical three dimension structure. Int. J. Remote Sens. 24(19):3743–3753Google Scholar
  152. 152.
    Kimes D, Knyazikhin Y, Privette J, Abuelgasim A, Gao F, (2000) Inversion methods for physically-based models. Remote Sens. Rev. 18:381–439Google Scholar
  153. 153.
    Chen J, Li X, Nilson T, Strahler A, (2000) Recent advances in geometrical optical modeling and its applications. Remote Sens. Rev. 18:227–262Google Scholar
  154. 154.
    Qin W, Liang S, (2000) Plane-parallel canopy radiation transfer modeling: recent advances and future directions. Remote Sens. Rev. 18:281–305Google Scholar
  155. 155.
    Nolin A, Liang S, (2000) Progress in bidirectional reflectance modeling and applications for surface particulate media: snow and soils. Remote Sens. Rev. 18:307–342Google Scholar
  156. 156.
    Lucht W, Roujean J, (2000) Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations. Remote Sens. Rev. 18:343–379Google Scholar
  157. 157.
    Schmugge T, French A, Ritchie J, Rango A, Pelgrum H, (2002) Temperature and emissivity separation from multispectral thermal infrared observations. Remote Sens. Environ. 79:189–198Google Scholar
  158. 158.
    Jacob F, Petitcolin F, Schmugge T, Vermote E, Ogawa K, French A, (2004) Comparison of land surface emissivity and radiometric temperature from MODIS and ASTER sensors. Remote Sens. Environ. 83:1–18Google Scholar
  159. 159.
    Hall F, Huemmrich K, Goetz S, Sellers P, Nickeson P, (1992) Satellite remote sensing of surface energy balance : success, failures and unresolved issues in FIFE. J. Geophys. Res. 97:19061–19089Google Scholar
  160. 160.
    Bolle H, et al., (1993) EFEDA: European Field in Desertification threatened Area. Annales Geophysicae 11:173–189Google Scholar
  161. 161.
    Goutorbe J, et al., (1994) HAPEX-SAHEL: a large scale study of land-atmosphere interactions in the semiarid tropics. Annales Geophysicae 12: 53–64Google Scholar
  162. 162.
    Havstad K, Kustas W, Rango A, Ritchie J, Schmugge T, (2000) Jornada Experimental Range: a unique arid land location for experiments to validate satellite system. Remote Sens. Environ. 74:13–25Google Scholar
  163. 163.
    Baldocchi D, Falge E, Gu LH, Olson R, Hollinger D, Running S, Anthoni P, Bern-hofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee XH, Malhi Y, Meyers T, Munger W, Oechel W, Paw UKT, Pilegaard K Schmid HP, Valentini R, Verma S, (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. 82-11:2415-2434Google Scholar
  164. 164.
    Berger M, Rast M, Wursteisen P, Attema E, Moreno J, Mueller A, Beisl U, Richter R, Schaepman M, Strub G, Stoll MP, Nerry F, Leroy M, (2001) The DAISEX campaigns in support of a future land-surface-processes mission ESA Bulletin. Remote Sens. Environ. 105:101–111Google Scholar
  165. 165.
    Goodrich DC, Chehbouni A, Goff BF, Macnish R, Maddock T, Moran MS, Shuttleworth WJ, Williams DG, Watts CJ, Hipps LJ, Cooper DI, Schieldge J, Kerr YH, Arias H, Kirkland M, Carlos R, Cayrol P, Kepner W, Jones B, Avissar R, Begue A, Bonnefond JM, Boulet G, Branan B, Brunel JP, Chen LC, Clarke T, Davis MR, Debruin H, Dedieu G, Elguero E, Eichinger WE, Everitt J, Garatuza-Payan J, Gempko VL, Gupta H, Harlow C, Hartogensis O, Helfert M, Holifield C, Hymer D, Kahle A, Keefer T, Krishnamoorthy S, Lhomme JP, Lagouarde JP, Lo Seen D, Luquet D, Marsett R, Monteny B, Ni W, Nouvellon Y, Pinker R, Peters C, Pool D, Qi J, Rambal S, Rodriquez J, Santiago F, Sano E, Schaeffer SM, Schulte M, Scott R, Shao X, Snyder KA, Sorooshian S, Unkrich CL, Whitaker M, Yucel I, (2000) Preface paper to the Semi-Arid Land-Surface- Atmosphere (SALSA) Program special issue. Agric. Forest Meteorol. 105:320Google Scholar
  166. 166.
    Njoku E, Lakshmi V, O’Neill P, (2004) Soil moisture field experiment special issue. Remote Sens. Environ. 92:425–426Google Scholar
  167. 167.
    Schmugge T, Hook S, Coll C, (1998) Recovering surface temperature and emissivity from thermal infrared multispectral data. Remote Sens. Environ. 65:121–131Google Scholar
  168. 168.
    Coll C, Caselles V, Rubio E, Sospedra F, Valor E, (2000) Temperature and emissivity separation from calibrated data of the Digital Airborne Imaging Spectrometer. Remote Sens. Environ. 76:250–259Google Scholar
  169. 169.
    Buongiorno M, Realmuto V, Doumaz F, (2002) Recovery of spectral emissivity from thermal infrared multispectral scanner imagery acquired over a mountainous terrain: a case study from mount Etna, Sicily. Remote Sens. Environ. 79:123–133Google Scholar
  170. 170.
    Coll C, Caselles V, Rubio E, Valor E, Sospedra F, Baret F, Prévot L, Jacob F, (2002) Temperature and emissivity extracted from airborne multi-channel data in the ReSeDA experiment. Agronomie: Agric. Environ. 22:567–574Google Scholar
  171. 171.
    Jacob F, Gu X, Hanocq JF, Baret F, (2003) Atmospheric corrections of single broadband channel and multidirectional airborne thermal infrared data. Application to the ReSeDA Experiment. Int. J. Remote Sens. 24:3269–3290Google Scholar
  172. 172.
    Coll C, Caselles V, Valor E, Rubio E, (2003) Validation of temperature-emissivity separation and split-window methods from TIMS data and ground measurements. Remote Sens. Environ. 85:232–242Google Scholar
  173. 173.
    Coll C, Valor E, Caselles V, Nicl òs R, (2003) Adjusted Normalized Emissivity Method for surface temperature and emissivity retrieval from optical and thermal infrared remote sensing data. J. Geophys. Res. 108(D23):4739, doi:10.1029/2003JD003688Google Scholar
  174. 174.
    Sobrino J, Jimenez-Munoz J, El-Kharraz J, Gomez M, Romaguera M, Sòria G, (2004) Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site. Int. J. Remote Sens. 25(1):215–230Google Scholar
  175. 175.
    Schmugge T, Jacob F, French A, Ogawa K, (2002) Quantitative estimates of emissivity using ASTER data. In: Sobrino J (ed.) Proceedings of the First International Symposium on Recent Advances in Quantitative Remote Sensing, September 2002, Valencia, Spain, pp 585–589Google Scholar
  176. 176.
    Schmugge T, Ogawa K, Jacob F, French A, Hsu Y, Ritchie J, Rango A, (2003) Validation of emissivity estimates from ASTER data. In: Proceedings of 2003 International Geoscience and Remote Sensing Symposium, Toulouse, France, 21-25 July 2003. Vol III , pp 1873-1875Google Scholar
  177. 177.
    Sobrino J, Jimenez-Munoz J, Paolini L, (2004) Land surface temperature retrieval from Land-sat TM 5. Remote Sens. Environ. 90:434–440Google Scholar
  178. 178.
    Li F, Jackson T, Kustas W, Schmugge T, French A, Cosh M, Bindlish R, (2004) Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sens. Environ. 92:521–534Google Scholar
  179. 179.
    Jimenez-Munoz JC, Sobrino JA, Gillespie A, Sabol D, Gustafson TG, (2006) Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sens. Environ. 103:474–487Google Scholar
  180. 180.
    Jimenez-Munoz JC, Sobrino JA, (2003) A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res. 108(D22):4688, doi:10.1029/2003JD003480Google Scholar
  181. 181.
    Wan Z, Zhang Y, Zhang Q, Li ZL, (2002) Validation of the land surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sens. Environ. 83:163–180Google Scholar
  182. 182.
    Wan Z, Zhang Y, Zhang Q, Li ZL, (2004) Quality assessment and validation of the MODIS global land surface temperature. Int. J. Remote Sens. 25:261–274Google Scholar
  183. 183.
    Coll C, Caselles V, Galve JM, Valor E, Niclòs R, Sánchez J, Rivas R, (2005) Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sens. Environ. 97:288–300Google Scholar
  184. 184.
    Weiss M, Baret F, Garrigues S, Lacaze R, (2007) LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part2: validation and comparison with MODIS collection 4 products. Remote Sens. Environ. 110(3):317–331Google Scholar
  185. 185.
    Tonooka H, (2001) An atmospheric correction algorithm for thermal infrared multispectral data over land - a water vapor scaling method. IEEE Trans. Geosci. Remote Sens. 39:682–692Google Scholar
  186. 186.
    ash P, Göttsche FM, Olesen FS, (2002) Potential of MSG for surface temperature and emissivity estimation: considerations for real-time applications. Int. J. Remote Sens. 23(20):4511–4518Google Scholar
  187. 187.
    Peres L, DaCamara C, (2004) Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data. Remote Sens. Environ. 91:377–389Google Scholar
  188. 188.
    Dash P, Göttsche FM, Olesen FS, Fischer H, (2005) Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method. Remote Sens. Environ. 96:1–17Google Scholar
  189. 189.
    Jia L, Li Z, Menenti M, Su Z, Verhoef W, Wan Z, (2003) A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data. Int. J. Remote Sens. 24:4739–4760Google Scholar
  190. 190.
    Kratz DP, Chou MD, Yan MMH, Ho CH, (1998) Minor Trace Gas Radiative Forcing Calculations Using the k Distribution Method with One-Parameter Scaling. J. Geophys. Res.103(D24):31647–31656Google Scholar
  191. 191. Saunders R, Brunel P, English S, Bauer P, OKeeffe U, Francis P, Rayer P, (2005) Rttov-8, science and validation report. Technical report, EUMESAT (2005) report NWPSAF-MO-TV-007, v1.2, 02/03/2005Google Scholar
  192. 192.
    Gottsche FM, Olesen F, (2002) Evolution of neural networks for radiative transfer calculations in the terrestrial infrared. Remote Sens. Environ. 80:157–164Google Scholar
  193. 193.
    Derber J, Parrish D, Lord S, (1991) The new global operational analysis system at the National Meteorological Center (NMC). Weather Forecast. 6:538–547Google Scholar
  194. 194.
    Derber J, Wu W, (1998) The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Monthly Weather Rev. 126:2287–2299Google Scholar
  195. 195.
    Schroedter M, Olesen FS, Fischer H, (2003) Determination of land surface temperature distributions from single channel IR measurements: an effective spatial interpolation method for the use of TOVS, ECMWF, and radiosonde profiles in the atmospheric correction scheme. Int. J. Remote Sens. 24:1189–1196Google Scholar
  196. 196.
    Hirano A, Welch R, Lang H, (2002) Mapping from ASTER stereo image data: DEM validation and accuracy assessment. ISPRS J. Photogramm. Remote Sens. 55:1–15Google Scholar
  197. 197.
    Gu D, Gillespie AR, Kahle AB, Palluconi FD, (2000) Autonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 38:2557–2569Google Scholar
  198. 198.
    Li ZL, Li J, Su Z, Wan Z, Zhang R, (2003) A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area. Int. J. Remote Sens. 24(24):5095-5117Google Scholar
  199. 199.
    Sobrino J, El Kharraz J, Li ZL, (2003) Surface temperature and water vapor retrieval from MODIS data. Int. J. Remote Sens. 24(24):5161–5182Google Scholar
  200. 200.
    De Felice T, LLoyd D, Meyer D, Baltzer T, Piraino P, (2003) Water vapour correction of the daily 1 km AVHRR global land dataset: part I - validation and use of the water vapour input field. Int. J. Remote Sens. 24(11):2365–2375Google Scholar
  201. 201.
    Rabier F, Fourrié N, Chafaï D, Prunet P, (2001) Channel selection methods for infrared atmospheric sounding interferometer radiances. Quart. J. Roy. Meteorol. Soc. 128:1–17Google Scholar
  202. 202.
    Pinheiro A, Privette J, Mahoney R, Tucker C, (2004) Directional effects in a daily AVHRR land surface temperature dataset over Africa. IEEE Trans. Geosci. Remote Sens. 42(9):1941–1954Google Scholar
  203. 203.
    van de Griend A, Owe M, (1993) On the relationship between thermal infrared emissivity and the Normalized Difference Vegetation Index for natural surfaces. Int. J. Remote Sens. 14 :1119–1131Google Scholar
  204. 204.
    Kerr Y, Lagouarde J, Nerry F, Ottlé C, (2004) Land surface temperature retrieval techniques and applications: case of the AVHRR. In: Thermal remote sensing in land surface processes. Boca Raton, FL: CRC, pp 33–109 + 14 colour platesGoogle Scholar
  205. 205.
    Snyder W, Wan Z, Zhang Y, Feng YZ, (1998) Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sens. 19:2753–2774Google Scholar
  206. 206.
    Payan V, Royer A, (2004) Spectral emissivity of northern land cover types derived with MODIS and ASTER sensors in MWIR and LWIR. Research Note, Can. J. Remote Sens. 30(2):150–156Google Scholar
  207. 207.
    Peres L, DaCamara C, (2005) Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI. IEEE Trans. Geosci. Remote Sens. 43(8):1834–1844Google Scholar
  208. 208.
    Gillespie A, (1985) Lithologic mapping of silicate rocks using TIMS. In: The TIMS data user workshop, June 18-19, 1985. JPL Publication N 86-38, Jet Propulsion Laboratory, Pasadena, CA (1985), pp 29–44Google Scholar
  209. 209.
    Mushkin A, Balick LK, Gillespie AR, (2005) Extending surface temperature and emissivity retrieval to the mid-infrared (3-5 µm) using the Multispectral Thermal Imager (MTI). Remote Sens. Environ. 98:141–151Google Scholar
  210. 210.
    Gillespie A, Rokugawa S, Matsunaga T, Cothern S, Hook S, Kahle A, (1998) A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens. 36:1113–1126Google Scholar
  211. 211.
    Liang S, (2001) An optimization algorithm for separating land surface temperature and emissivity from multispectral thermal infrared imagery. IEEE Trans. Geosci. Remote Sens. 39:264–274Google Scholar
  212. 212. Mushkin A, Balick LK, Gillespie AR, (2002) Temperature/emissivity separation of MTI data using the Terra/ASTER TES algorithm. In: Shen SS, Lewis PE (eds) (2002) Proceedings of SPIE - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII. Vol 4725, pp 328-337Google Scholar
  213. 213.
    Gu D, Gillespie AR, (2000) A new approach for temperature and emissivity separation. Int. J. Remote Sens. 21:2127–2132Google Scholar
  214. 214.
    Ogawa K, Schmugge T, Jacob F, French A, (2002) Estimation of broadband land surface emissivity from multispectral thermal infrared remote sensing. Agronomie: Agric. Environ. 22:695–696Google Scholar
  215. 215.
    Peres L, DaCamara C, (2004) Inverse problems theory and application: analysis of the two-temperature method for land-surface temperature and emissivity estimation. Geosci. Remote Sens. Lett. 1(3):206–210Google Scholar
  216. 216.
    Wan Z, Li ZL, (1997) A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens. 35:980–996Google Scholar
  217. 217.
    Watson K, (1992) Two-temperature method for measuring emissivity. Remote Sens. Environ. 42:117–121Google Scholar
  218. 218.
    Boyd D, Petitcolin F, (2004) Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 µm). Int. J. Remote Sens. 25(17):3343–3368Google Scholar
  219. 219.
    Huntingford C, Verhoef A, Stewart J, (2000) Dual versus single source models for estimating surface temperature of African Savannah. Hydrol. Earth Sci. Syst. 4(1):185–191CrossRefGoogle Scholar
  220. 220.
    Kustas W, Choudhury B, Moran M, Reginato R, Jackson R, Gay L, Weaver H, (1989) Determination of sensible heat flux over sparse canopy using thermal infrared data. Agric. Forest Meteorol. 44:197–216Google Scholar
  221. 221.
    Moran M, Kustas W, Vidal A, Stannard D, Blanford J, Nichols W, (1994) Use of ground-based remotely sensed data for surface energy balance evaluation of a semiarid rangeland. Water Res. Res. 30:1339–1349Google Scholar
  222. 222.
    Stewart J, Kustas W, Humes K, Nichols W, Moran M, de Bruin H, (1994) Sensible heat flux - radiometric surface temperature relationship for eight semiarid areas. J. Appl. Meteorol. 33:1110–1117Google Scholar
  223. 223.
    Sun J, Mahrt L, (1995) Determination of surface fluxes from the surface radiative temperature. J. Atmos. Sci. 52(8):1096–1106Google Scholar
  224. 224.
    Cahill A, Parlange M, (1997) On the Brutsaert temperature roughness length model for sensible heat flux estimation. Water Res. Res. 10:2315–2324Google Scholar
  225. 225.
    Lhomme J, Troufleau D, Monteny B, Chehbouni A, Bauduin S, (1997) Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation II. A model for the kB−1 parameter. J. Hydrol. 188-189:839–854Google Scholar
  226. 226.
    Verhoef A, De Bruin H, Van Den Hurk B, (1997) Some practical notes on the kB−1 . J. Appl. Meteorol. 36:560–572Google Scholar
  227. 227.
    Qualls R, Yates D, (2001) Directional radiometric temperature profiles within a grass canopy.Adv. Water Res. 24:145–155Google Scholar
  228. 228.
    Molder M, Lindroth A, (2001) Dependence of kB−1 factor on roughness Reynolds number for barley and pasture. Agric. Forest Meteorol. 106:147–152Google Scholar
  229. 229.
    Molder M, Kellner E, (2002) Excess resistance of bog surfaces in central Sweden. Agric. Forest Meteorol. 112:23–30Google Scholar
  230. 230. Prévot L, Brunet Y, Paw UK, Seguin B, (1993) Canopy modeling for estimating sensible heat flux from thermal infrared measurements. In: Workshop on Thermal Remote Sensing of the Energy Balance Over Vegetation in Conjunction with Other Sensor, La Londe les Maures, 20-23 September. Vol 93, pp 17-30Google Scholar
  231. 231.
    Norman J, Kustas W, Humes K, (1995) Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. Forest Meteorol. 77:263–293Google Scholar
  232. 232.
    Kustas W, Norman J, (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric. Forest Meteorol. 94:13–29Google Scholar
  233. 233.
    Kustas W, Norman J, (2000) A two source energy balance approach using directional radiometric temperature observations for sparse canopy covered surfaces. Agron. J. 92:847–854CrossRefGoogle Scholar
  234. 234.
    Olioso A, (1995) Estimating the difference between brightness and surface temperatures for a vegetal canopy. Agric. Forest Meteorol. 72:237–242Google Scholar
  235. 235.
    Lagouarde JP, Kerr Y, Brunet Y, (1995) An experiment study of angular effects on surface temperature for various plant canopies and bare soils. Agric. Forest Meteorol. 77:167–190Google Scholar
  236. 236.
    Verbrugghe M, Cierniewski J, (1998) Influence and modeling of view angles and micro-relief on surface temperature measurements of bare agricultural soils. ISPRS J. Photogramm. Remote Sens. 53:166–173Google Scholar
  237. 237.
    Sobrino J, Cuenca J, (1999) Angular variation of thermal infrared emissivity for some natural surfaces from experimental measurements. Appl. Opt. 38(18):3931–3936Google Scholar
  238. 238.
    Weiss M, Baret F, Leroy M, Hautecoeur O, Bacour C, Prévot L, Bruguier N, (2002) Evaluation of Neural Network techniques to estimate canopy biophysical variables from remote sensing data. Agronomie: Agric. Environ. 22:547–553Google Scholar
  239. 239.
    Bastiaanssen W, Menenti M, Feddes R, Holtslag A, (1998) A remote sensing surface  energy balance algorithm for land (SEBAL). I: formulation. J. Hydrol. 212-213:198–212Google Scholar
  240. 240.
    Su Z, Yacob A, Wen J, Roerink G, He Y, Gao B, Boogaard H, van Diepen C, (2003) Assessing soil moisture with remote sensing data: theory, experimental validation, and application to drought monitoring over the North China Plain. Phys. Chem. Earth 28:89–101Google Scholar
  241. 241.
    Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D, Zimmer D, Sierra J, Bertuzzi P, Burger P, Bussi ère F, Cabidoche Y, Cellier P, Debaeke P, Gaudillère J, Hénault C, Maraux F, Seguin B, Sinoquet H, (2003) An overview of the crop model STICS. Eur. J. Agron. 18:309–332Google Scholar
  242. 242.
    Mougin E, Lo Seen D, Rambal S, Gaston A, Hiernaux P, (2004) A regional sahelian grass-land model to be coupled with multispectral satellite data. I: model description and validation. Remote Sens. Environ. 52:181–193Google Scholar
  243. 243.
    Pellenq J, Boulet G, (2004) A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie 24:197–204Google Scholar
  244. 244. Lauvernet C, Baret F, Le Dimet FX, (2003) Assimilating high temporal frequency SPOT data to describe canopy functioning: the ADAM project. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS ’03. Proceedings. 2003 IEEE International. Vol 5, pp 3184-3186Google Scholar
  245. 245.
    Delécolle R, Maas S, Guérif M, Baret F, (1992) Remote sensing and crop production model: present trends. ISPRS J. of Photogramm. Remote Sens. 47:145–161Google Scholar
  246. 246.
    Weiss M, Troufleau D, Baret F, Chauki H, Prévot L, Olioso A, Bruguier N, Brisson N, (2001) Coupling canopy functioning and radiative transfer models for remote sensing data assimilation. Agric. Forest Meteorol. 108:113–128Google Scholar
  247. 247.
    Moulin S, Kergoat L, Cayrol P, Dedieu G, Prévot L, (2002) Calibration of a coupled canopy functioning and SVAT model in the ReSeDA experiment. Towards the assimilation of SPOT/HRV observations into the model. Agronomie: Agric. Environ. 22:681–686Google Scholar
  248. 248.
    Prévot L, Chauki H, Troufleau D, Weiss M, Baret F, Brisson N, (2003) Assimilating optical and radar data into the STICS crop model for wheat. Agronomie: Agric. Environ. 23:297–303Google Scholar
  249. 249.
    Ottlé C, Vidal-Madjar D, (1994) Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region. J. Hydrol. 158:241-264Google Scholar
  250. 250.
    van den Hurk B, Bastiaanssen W, Pelgrum H, van Meijgaard E, (1997) A new methodology for assimilation of initial soil moisture fields in weather prediction models using METEOSAT and NOAA data. J. Appl. Meteorol. 36:1271–1283Google Scholar
  251. 251.
    Chehbouni A, Njoku E, Lhomme JP, Kerr Y, (1995) Approaches for averaging surface parameters and fluxes over heterogeneous terrain. J. Climate 8:1386–1393Google Scholar
  252. 252.
    Njoku E, Hook S, Chehbouni A, (1996) Effects of surface heterogeneity on thermal remote sensing of land parameters, Chapter 2. In: Scaling up in hydrology using remote sensing. John, West Sussex, pp 19–31Google Scholar
  253. 253. Su Z, Pelgrum H, Menenti M, (1999) Aggregation effects of surface heterogeneity in land surface processes. In: Su Z, Menenti M (eds), Hydrology and Earth Science System. Vol 3, pp 549-563Google Scholar
  254. 254.
    Bouguerzaz FA, Olioso A, Raffy M, (1999) Modeling radiative and energy balance on heterogeneous areas from measured radiances. Can. J. Remote Sens. 25(4):412–424Google Scholar
  255. 255.
    Kustas W, Norman J, (2000) Evaluating the effects of subpixel heterogeneity on pixel average fluxes. Remote Sens. Environ. 74:327–342Google Scholar
  256. 256.
    Chehbouni A, Watts C, Kerr Y, Dedieu G, Rodriguez JC, Santiago F, Cayrol P, Boulet G, Goodrich D, (2000) Methods to aggregate turbulent fluxes over heterogeneous surfaces: application to SALSA data set in Mexico. Agric. Forest Meteorol. 105:133–144Google Scholar
  257. 257.
    French A, Schmugge T, Kustas W, Brubaker K, Prueger J, (2003) Surface energy fluxes over El Reno, Oklahoma using high resolution remotely sensed data. Water Res. Res. 39:1164Google Scholar
  258. 258.
    Brunsell N, Gillies R, (2003) Scale issues in land atmosphere interactions: implications for remote sensing of the surface energy balance. Agric. Forest Meteorol. 117:203–221Google Scholar
  259. 259.
    Kustas W, Li F, Jackson T, Prueger J, MacPherson J, Wolde M, (2004) Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sens. Environ. 92:535–547Google Scholar
  260. 260.
    Lyons T, Halldin S, (2004) Surface heterogeneity and the spatial variation of fluxes. Agric. Forest Meteorol. 121:153–165Google Scholar
  261. 261.
    Norman J, Anderson M, Kustas W, French A, Mecikalski J, Torn R, Diak G, Schmugge T, Tanner B, (2003) Remote sensing of surface energy fluxes at 10−1 m pixel resolutions. Water Res. Res. 39:1221Google Scholar
  262. 262.
    Cardot H, Faivre R, Goulard M, (2003) Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data. J. Appl. Stat. 30(10):1185-1199Google Scholar
  263. 263.
    Anderson M, Norman J, Diak G, Kustas W, Mecikalski J, (1997) A two-source time integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 60:195–216Google Scholar
  264. 264. Guillevic P, (1999) Modélisation des bilans radiatif eténergétique des couverts végétaux. Ph.D. thesis, Université Paul Sabatier - Toulouse III, 181pp.Google Scholar
  265. 265. Schmugge T, Ogawa K, (2006) Validation of Emissivity Estimates from ASTER and MODIS Data. In: Geoscience and Remote Sensing Symposium, 2006. IGARSS ’06. Proceedings. 2006 IEEE International. Vol I, pp 260-262Google Scholar

Copyright information

© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • Frédéric Jacob
    • 1
  • Thomas Schmugge
    • 2
  • Albert Olioso
    • 3
  • Andrew French
    • 4
  • Dominique Courault
    • 3
  • Kenta Ogawa
    • 5
  • Francois Petitcolin
    • 6
  • Ghani Chehbouni
    • 7
  • Ana Pinheiro
    • 8
  • Jeffrey Privette
    • 9
  1. 1.Institute of Research for the DevelopmentUMR LISAH SupAgro/INRA/IRDFrance
  2. 2.College of Agriculture New Mexico State UniversityLas CrucesUSA
  3. 3.National Institute for Agronomical ResearchUMR CSE INRA/UAPVFrance
  4. 4.United States Department of AgricultureUS Arid Land Agricultural Research CenterMaricopaUSA
  5. 5.Department of Geo-system EngineeringUniversity of Tokyo and Hitachi Ltd.Japan
  6. 6.ACRI-STFrance
  7. 7.Institute of Research for the DevelopmentCenter for Spatial Studies of the BiosphereFrance
  8. 8.Biospheric Sciences BranchNASA's GSFCGreenbeltUSA
  9. 9.NOAA's National Climatic Data CenterAshevilleUSA

Personalised recommendations