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 Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3-5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north-south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

“…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].…”

Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

“…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.…”

A 10-Year Dataset of Basic Meteorology and Soil Properties in Central Sudan

“…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.…”

“…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].…”