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Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications

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2008-09
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Sobrino, J. A.
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Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications
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Author
  • Sobrino, J. A.;
  • Jiménez Muñoz, J. C.;
  • Sòria, G.;
  • Gómez, M.;
  • Barella Ortiz, A.;
  • Romaguera, M.;
  • Zaragoza, M.;
  • Julien, Y.;
  • Cuenca, J.;
  • Atitar, M.;
  • Hidalgo, V.;
  • Franch, B.;
  • Mattar, C.;
  • Ruescas, A.;
  • Morales Salinas, Luis;
  • Gillespie, A.;
  • Balick, L.;
  • Su, Z.;
  • Nerry, F.;
  • Peres, L.;
  • Libonati, R.;
Abstract
A description of thermal radiometric field measurements carried out in the framework of the European project SENtinel-2 and Fluorescence Experiment (SEN2FLEX) is presented. The field campaign was developed in the region of Barrax (Spain) during June and July 2005. The purpose of the thermal measurements was to retrieve biogeophysical parameters such as land surface emissivity (LSE) and temperature (LST) to validate airborne-based methodologies and to characterize different surfaces. Thermal measurements were carried out using two multiband field radiometers and several broadband field radiometers, pointing at different targets. High-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor were used to retrieve LST and LSE, applying the Temperature and Emissivity Separation (TES) algorithm as well as single-channel (SC) and two-channel (TC) methods. To this purpose, 10 AHS thermal infrared (TIR) bands (8–13 mm) were considered. LST and LSE estimations derived from AHS data were used to obtain heat fluxes and evapotranspiration (ET) as an application of thermal remote sensing in the context of agriculture and water management. To this end, an energy balance equation was solved using the evaporative fraction concept involved in the Simplified Surface Energy Balance Index (S-SEBI) model. The test of the different algorithms and methods against ground-based measurements showed root mean square errors (RMSE) lower than 1.8K for temperature and lower than 1.1 mm/day for daily ET.
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URI: https://repositorio.uchile.cl/handle/2250/120161
DOI: DOI: 10.1080/01431160802036516
ISSN: 0143-1161 print
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International Journal of Remote Sensing Vol. 29, Nos. 17–18, September 2008, 4961–4991
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