Semi-empirical models and scaling: a least square method for remote sensing experiments
Models used in remote sensing are based on the assumed relationship between measured radiances and the physical parameters of the soil surface. A number of them are semi-empirical, in the sense that they contain constants adjusted to take account of in situ experiments, generally carried out on a given type of medium. Once validated in situ, these models are applied to large scale areas with global radiances. In this paper, we demonstrate that constants taken to be appropriate for in situ experiments are no longer appropriate for global radiances. For remote sensing applications, we demonstrate that these constants must be determined by a new least square cost function. Finally, we evaluate, for various simulated cases using the LAI and the APAR models, the magnitude of gain of the new method for space applications, when compared to classical examples.
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