2012
DOI: 10.5194/hess-16-2567-2012
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Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

Abstract: Abstract.Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facili… Show more

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Cited by 47 publications
(29 citation statements)
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“…The estimated ETa as a residual of the energy balance contains biases from the net radiation, soil heat flux and sensible heat flux as well. Recently, some developments have been made based on the Soil-Vegetation-Atmosphere Transfer (SVAT) scheme, Penman-Monteith equation and remotely sensed data for the temporally and spatially continuous estimates of ETa, which is particularly useful for various applications since it is not limited to clear sky conditions [15][16][17][18][19]26].…”
Section: Introductionmentioning
confidence: 99%
“…The estimated ETa as a residual of the energy balance contains biases from the net radiation, soil heat flux and sensible heat flux as well. Recently, some developments have been made based on the Soil-Vegetation-Atmosphere Transfer (SVAT) scheme, Penman-Monteith equation and remotely sensed data for the temporally and spatially continuous estimates of ETa, which is particularly useful for various applications since it is not limited to clear sky conditions [15][16][17][18][19]26].…”
Section: Introductionmentioning
confidence: 99%
“…Two Terra LST products can be obtained per day at 10:30/22:30 and two Aqua LST products can be obtained per day at 01:30/13:30. Soil moisture, land surface temperature and LAI influence the estimation of latent and sensible heat fluxes (Ghilain et al, 2012;Jarlan et al, 2008;Schwinger et al, 2010;van den Hurk, 2003;Yang et al, 1999), and therefore this study also focused on the calibration of LAI with the help of the assimilation of land surface temperature. However, there are large discrepancies between the remotely retrieved LAI and measured values, and the MODIS LAI product underestimates in situ measured LAI by 44 % on average (http: //landval.gsfc.nasa.gov/), and therefore the LAI is also calibrated by data assimilation.…”
mentioning
confidence: 99%
“…e collected dataset has already contributed to several studies related to geoscienti�c issues in semiarid areas [7][8][9][10][11][12][13][14][15][16]. As the number of remote-sensing-based studies and modeling studies increases, the need for calibration and validation datasets increases as well.…”
Section: Discussionmentioning
confidence: 99%
“…Studies using subsets of data presented in this paper have been published previously [7][8][9], sometimes in combination with �ux data [10][11][12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%