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Agricultural and Forest Meteorology, 154- 155 (2012) 55-66
Spatio-temporal surface soil heat flux estimates from satellite data; results for the AMMA experiment at the Fakara (Niger) supersite
A. Verhoef ( ) 1, C. Ottlé 2, B. Cappelaere 3, Tom Murray 1, S. Saux-Picart 4, M. Zribi 5, F. Maignan 2, Nicolas Boulain 3, 6, J. Demarty 3, David Ramier 3
(2012)

This paper describes a method that employs Earth Observation (EO) data to calculate spatiotemporal estimates of soil heat flux, G, using a physically-based method (the Analytical Method). The method involves a harmonic analysis of land surface temperature (LST) data. It also requires an estimate of near-surface soil thermal inertia; this property depends on soil textural composition and varies as a function of soil moisture content. The EO data needed to drive the model equations, and the ground-based data required to provide verification of the method, were obtained over the Fakara domain within the African Monsoon Multidisciplinary Analysis (AMMA) program. LST estimates (3 km × 3 km, one image 15 min−1) were derived from MSG-SEVIRI data. Soil moisture estimates were obtained from ENVISAT-ASAR data, while estimates of leaf area index, LAI, (to calculate the effect of the canopy on G, largely due to radiation extinction) were obtained from SPOT-HRV images. The variation of these variables over the Fakara domain, and implications for values of G derived from them, were discussed. Results showed that this method provides reliable large-scale spatiotemporal estimates of G. Variations in G could largely be explained by the variability in the model input variables. Furthermore, it was shown that this method is relatively insensitive to model parameters related to the vegetation or soil texture. However, the strong sensitivity of thermal inertia to soil moisture content at low values of relative saturation (<0.2) means that in arid or semi-arid climates accurate estimates of surface soil moisture content are of utmost importance, if reliable estimates of G are to be obtained. This method has the potential to improve large-scale evaporation estimates, to aid land surface model prediction and to advance research that aims to explain failure in energy balance closure of meteorological field studies.
1 :  University of Reading (UOR)
University of Reading
2 :  Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE - UMR 8212)
CNRS : UMR8212 – CEA : DSM/LSCE – Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
3 :  Hydrosciences Montpellier (HSM)
CNRS : UMR5569 – Institut de recherche pour le développement [IRD] – Université Montpellier II - Sciences et techniques
4 :  Plymouth Marine Laboratory (PML)
Plymouth Marine Laboratory
5 :  Centre d'études spatiales de la biosphère (CESBIO)
CNRS : UMR5126 – Institut de recherche pour le développement [IRD] – CNES – Observatoire Midi-Pyrénées – INSU – Université Paul Sabatier (UPS) - Toulouse III
6 :  Plant Functional Biology and Climate Change Cluster (C3)
Universitu of Technology Sydney
Sciences du Vivant/Ecologie, Environnement/Ecosystèmes
Land surface temperatures MSG Soil heat flux AMMA Sahel
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2012_VerhoefeAMMAsoilheatflux.pdf(2.1 MB)