Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

Abstract

► We defined a latent heat flux ( λE ) upscaling framework for remote sensing ( RS ). ► We evaluated the accuracy of four upscaling methods, two observed and two modelled. ► Upscaling bias from observed flux was complementary to that from modelled flux. ► A procedure to minimise bias from all four upscaling approaches was developed. ► Modelled top-of-atmosphere solar irradiance was found suitable to upscale λE for RS . For instantaneous latent heat flux ( λE ) estimates from thermal remote sensing data to be useful in the hydrologic sciences, they require integration over longer time frames (e.g., months to years). This is not trivial because thermal remote sensing data acquired under cloud-free daytime conditions require upscaling to a monthly energy amount that is both relevant over cloudy periods and considers daytime and nighttime. Previous work has compared upscaling approaches, but as yet there is no authoritative comparison that does so under conditions relevant for thermal remote sensing. In this paper we describe, under the conditions relevant for thermal remote sensing, a generic framework for comparing any upscaling approach that assumes self-preservation. Then we use eddy-flux data from two sites in contrasting climates to systematically evaluate the accuracy of different upscaling proposals within the framework. We assumed that the instantaneous estimate of the latent heat flux measured by the eddy-flux technique would have been measured by a satellite sensor. We then scaled this estimate to a monthly period using four approaches and compared the result with the observed monthly integral. This design enabled us to isolate the accuracy of each upscaling method. The four methods upscaled λE by: (i) observed solar irradiance ( S ); (ii) modelled solar irradiance from a sine function ( S SIN ); (iii) modelled top-of-atmosphere solar irradiance ( S TOA ); and (iv) observed available energy ( A E ). We showed that upscaling λE using observed data ( S , A E ) resulted in underestimation of monthly evaporative energy, while the use of modelled data ( S SIN , S TOA ) led to overestimation, primarily due to the relationship between error and both the season (day-of-year) and cloud fraction. Of the two observed fluxes, upscaling with S resulted in lower overall errors than when using A E ( S bias: −1.11 M J m −2 d −1 or −16%; A E bias: −2.15 M J m −2 d −1 or −34%). Of the two modelled fluxes, upscaling with S TOA had lower errors than the widely used S SIN method ( S SIN bias: 1.03 M J m −2 d −1 or 14%; S TOA bias: 0.91 M J m −2 d −1 or 13%). We subsequently developed a simple procedure to minimise bias from all four upscaling approaches, and concluded that modelled data ( S TOA ) can be used to upscale λE to longer timescales for thermal remote sensing applications. This study developed the theory to minimise upscaling bias at two sites with contrasting climates, further work is needed to extend the approach to all global terrestrial climates.