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Modeling and Inversion in Thermal Infrared Remote Sensing over ...

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10 <strong>Model<strong>in</strong>g</strong> <strong>and</strong> <strong>Inversion</strong> <strong>in</strong> <strong>Thermal</strong> <strong>Infrared</strong> <strong>Remote</strong> Sens<strong>in</strong>g 293ird-00392669, version 1 - 9 Jun 2009[261] Norman J, Anderson M, Kustas W, French A, Mecikalski J, Torn R, Diak G,Schmugge T, Tanner B (2003) <strong>Remote</strong> sens<strong>in</strong>g of surface energy fluxes at10 −1 m pixel resolutions. Water Res. Res. 39:1221[262] Cardot H, Faivre R, Goulard M (2003) Functional approaches for predict<strong>in</strong>gl<strong>and</strong> use with the temporal evolution of coarse resolution remote sens<strong>in</strong>g data.J. Appl. Stat. 30(10):1185–1199[263] Anderson M, Norman J, Diak G, Kustas W, Mecikalski J (1997) A two-sourcetime <strong>in</strong>tegrated model for estimat<strong>in</strong>g surface fluxes us<strong>in</strong>g thermal <strong>in</strong>fraredremote sens<strong>in</strong>g. <strong>Remote</strong> Sens. Environ. 60:195–216[264] Guillevic P (1999) Modélisation des bilans radiatif et énergétique des couvertsvégétaux. Ph.D. thesis, Université Paul Sabatier – Toulouse III, 181pp.[265] Schmugge T, Ogawa K (2006) Validation of Emissivity Estimates fromASTER <strong>and</strong> MODIS Data. In: Geoscience <strong>and</strong> <strong>Remote</strong> Sens<strong>in</strong>g Symposium,2006. IGARSS ’06. Proceed<strong>in</strong>gs. 2006 IEEE International. Vol I, pp 260–262Uncorrected Proof

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