Surface temperature and emissivity at various scales: Definition, measurement and related problems

Abstract

In order to determine surface heat flux at large scale from space measurement, it is necessary to introduce a bulk temperature for the whole pixel area for heterogeneous and non isothermal surfaces. This temperature should be measurable from space and should be related to the corresponding fluxes. A possible definition of such a surface temperature is discussed and its relationship with the radiation and sensible heat fluxes is given in the first part of this paper. This temperature, referred to as the radiometric temperature 〈Tsr〉, depends not only on the distributions of surface temperature and emissivity within a pixel but also on the channel used to measure it. This dependence is modeled and it is shown that if the surface emissivity and surface temperature variations are small within a pixel, 〈Tsr 〉 is equivalent to the average of surface temperatures within this pixel. In some cases, it is possible to get the average and the variance of the distribution of the surface temperatures within a pixel from the measurement of 〈Tsr 〉 in at least two channels. In order to measure this radiometric temperature from space, it is necessary to separate surface temperature and surface emissivity from the observed radiance. We review several approaches in the second part of this paper and present an improved TISI (Temperature‐Independent Spectral Indices) for deriving emis‐sivities from AVHRR data. Once the emissivities are measured, the radiometric temperature may be derived using local split‐window algorithms. We propose a new split window algorithm which takes into account both the spectral emissivities of the surface and the corrections for large variations of the atmospheric water vapor content. Since it is very difficult to obtain in‐situ surface temperature relevant for AVHRR pixel, we just give in the last part of this paper an estimation of the errors of models by comparing, over different sites and during different seasons, the surface temperature retrieved from this local split window algorithm and from several published split window algorithms for AVHRR/2 data using in all the cases the values of emissivjties derived from the method discussed in the second part. The results of this comparison show that algorithms derived by Prata and Platt (1991), Sobrino et al. (1991, 1993), Ulivieri et al. (1994) and the proposed local split window give comparable results, while a former algorithm of Becker and Li (1990b) gives a systematic surface temperature over‐estimation of about 1.5 K with respect to the most recent methods, and other algorithms (Price, 1984; Ulivieri et al. 1985; and NESDIS) can be significantly different.