Validation of the SEBS-derived sensible heat for FY3A/VIRR and TERRA/MODIS over an alpine grass region using LAS measurements |
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| Institution: | 1. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA), and the National Satellite Meteorological Center, China Meteorological Administration, Beijing, PR China;2. Applied Meteorology College, Nanjing University of Information Science & Technology, Nanjing, PR China;1. Dipartimento di Matematica e Informatica, Università di Palermo, Via Archirafi 34, 90123, Palermo, Italy;2. IMECC, Unicamp, Sergio Buarque de Holanda 651, 13083-859, Campinas, SP, Brazil;1. Laboratoire Image, Ville, Environnement, LIVE, UMR 7362 CNRS, Université de Strasbourg, France;2. Institut de Physique du Globe de Strasbourg, IPGS, UMR 7516 CNRS, Université de Strasbourg, France;3. Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie, ICUBE, UMR 7357, Université de Strasbourg, France;1. Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, Clamecystr. 12, D-63571 Gelnhausen, Germany;2. Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, D-60325 Frankfurt am Main, Germany;1. Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, India;2. School of Infrastructure, Indian Institute of Technology, Bhubaneswar 751013, India;3. Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur 721302, India;4. Government of West Bengal, Mathabhanga, Coochbehar, 736146, India;1. USDA-ARS, U.S. Arid Land Agricultural Research Center, 21881 N Cardon Ln, Maricopa, AZ, 85138, USA;2. USDA-ARS, Conservation and Production Research Laboratory, 2300 Experiment Station Rd, Bushland, TX, 79012, USA;3. USDA-ARS, Center for Agricultural Resources Research, 2150 Centre Ave, Fort Collins, CO, 80526, USA;4. USDA-ARS, Cropping Systems Research Laboratory, 3810 4th St, Lubbock, TX 79415, USA |
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| Abstract: | In this study, sensible heat (H) calculation using remote sensing data over an alpine grass landscape is conducted from May to September 2010, and the calculation is validated using LAS (large aperture scintillometers) measurements. Data from two remote sensing sensors (FY3A-VIRR and TERRA-MODIS) are analysed. Remote sensing data, combined with the ground meteorological observations (pressure, temperature, wind speed, humidity) are fed into the SEBS (Surface Energy Balance System) model. Then the VIRR-derived sensible heat (VIRR_SEBS_H) and MODIS-derived sensible heat (MODIS_SEBS_H) are compared with the LAS-estimated H, which are obtained at the respective satellite overpass time. Furthermore, the similarities and differences between the VIRR_SEBS_H and MODIS_SEBS_H values are investigated. The results indicate that VIRR data quality is as good as MODIS data for the purpose of H estimation. The root mean square errors (rmse) of the VIRR_SEBS_H and MODIS_SEBS_H values are 45.1098 W/m2 (n = 64) and 58.4654 W/m2 (n = 71), respectively. The monthly means of the MODIS_SEBS_H are marginally higher than those of VIRR_SEBS_H because the satellite overpass time of the TERRA satellite lags by 25 min to that of the FT3A satellite. Relative evaporation (EFr), which is more time-independent, shows a higher agreement between MODIS and VIRR. Many common features are shared by the VIRR_SEBS_H and the MODIS_SEBS_H, which can be attributed to the SEBS model performance. In May–June, H is over-estimated with more fluctuations and larger rmse, whereas in July–September, H is under-estimated with fewer fluctuations and smaller rmse. Sensitivity analysis shows that potential temperature gradient (delta_T) plays a dominant role in determining the magnitude and fluctuation of H. The largest rmse and over-estimation in H occur in June, which could most likely be attributed to high delta_T, high wind speed, and the complicated thermodynamic state during the transitional period when bare land transforms to dense vegetation cover. |
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| Keywords: | SEBS Sensible heat VIRR MODIS LAS |
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