Seasonal estimation of evaporation and stomatal conductance over a soybean field using surface IR temperatures
Seasonal estimation of evaporation and stomatal conductance over a soybean field using surface IR temperatures
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DOI:
10.1016/0168-1923(94)05082-h
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发表时间:
1995-03
影响因子:
6.2
通讯作者:
O. Taconet;A. Olioso;M. B. Mehrez;N. Brisson
中科院分区:
文献类型:
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作者:
O. Taconet;A. Olioso;M. B. Mehrez;N. Brisson
Over dense canopies, IR remote sensing provides information on the canopy water status. Both canopy evapotranspiration and canopy stomatal conductance can be inferred, through inversion of models describing the transfers between soil, vegetation and atmosphere using midday radiometric temperatures. Soil parameters are initialized using calibrated functions with soil moisture measurements. Vegetation density and height are measured. The single parameter which remains to be determined is the canopy stomatal conductance. To simplify the remote-sensed inversion, we use a simple parameterization of the leaf conductance. The leaf conductance is only a function of the incident solar flux and of a coefficient related to the level of midday leaf stomatal conductance. The latter coefficient is derived through model inversion. This method is evaluated using data from a field experiment over soybean, carried out in 1990 at the INRA Research Centre at Avignon, associating brightness temperature measurements with measurements of surface fluxes and soil and vegetation parameters throughout the crop cycle. The evaluation of evapotranspiration and leaf stomatal conductance changes is very promising. They show good correlations with experimental seasonal evolution and only slight underestimations (−0.5 mm day−1and −0.002 m s−1). The inferred midday leaf stomatal conductance can be related to the temporal variations of the environmental factors controlling the stomata aperture, as the leaf water potential and the in-canopy vapour pressure deficit. This is done using the response functions of conductance calibrated by Olioso (Thèse de Doctorat, Université de Montpellier, 1992). It opens the potentiality of incorporating this remote-sensed information of plant water status into crop growth modelling, particularly for monitoring the water stress module.