Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements - I. A universal approach to calculate thermal inertia

In this paper we pledge that physically based equations should be combined with remote sensing techniques to enable a more theoretically rigorous estimation of area-average soil heat flux, G. A standard physical equation (i.e. the analytical or exact method) for the estimation of G, in combination with a simple, but theoretically derived, equation for soil thermal inertia (F), provides the basis for a more transparent and readily interpretable method for the estimation of G; without the requirement for in situ instrumentation. Moreover, such an approach ensures a more universally applicable method than those derived from purely empirical studies (employing vegetation indices and albedo, for example). Hence, a new equation for the estimation of Gamma(for homogeneous soils) is discussed in this paper which only requires knowledge of soil type, which is readily obtainable from extant soil databases and surveys, in combination with a coarse estimate of moisture status. This approach can be used to obtain area-averaged estimates of Gamma(and thus G, as explained in paper II) which is important for large-scale energy balance studies that employ aircraft or satellite data. Furthermore, this method also relaxes the instrumental demand for studies at the plot and field scale (no requirement for in situ soil temperature sensors, soil heat flux plates and/or thermal conductivity sensors). In addition, this equation can be incorporated in soil-vegetation-atmosphere-transfer models that use the force restore method to update surface temperatures (such as the well-known ISBA model), to replace the thermal inertia coefficient.

The Role of Collateral in Entrepreneurial Finance

Previous research has suggested collateral has the role of sorting entrepreneurs either by observed risk or by private information. In order to test these roles, this paper develops a model which incorporates a signalling process (sorting by observed risk) into the design of an incentivecompatible menu of loan contracts which works as a self-selection mechanism (sorting by private information). It then tests this Sorting by Signalling and Self-Selection Model, using the 1998 US Survey of Small Business Finances. It reports for the first time that: high type entrepreneurs are more likely to pledge collateral and pay a lower interest rate; and entrepreneurs who transfer good signals enjoy better contracts than those transferring bad signals. These findings suggest that the Sorting by Signalling and Self-Selection Model sheds more light on entrepreneurial debt finance than either the sorting-by-observed-risk or the sorting-by-private information paradigms on their own.