Modification of harmonic analysis model for diurnal surface soil heat flux estimate from multiple remote sensing data

Abstract

The regional surface soil heat flux (G0) is very important for the large-scale land surface process modeling, and particularly for the regional evapotranspiration estimation. Harmonic analysis method (HM) could obtain regional G0 using remote sensing data. However, it has been demonstrated that HM cannot work well during nighttime due to the nonharmonic nature of G0. Encouraged by the half-order method (HO), which can resolve nonharmonic series, this study combined the two methods of HM and HO in order to obtain an accurate estimation of G0 for full day. The combined method was assessed with in-situ measurements and was applied to the Heihe River Basin (HRB) of northwest China using multiremote sensing observations at 30-min intervals. The below-canopy soil surface temperature (Ts) is one of the key variables in the model, which can be estimated from canopy temperature (Tc). The relationship of Tc and Ts was discussed. Results show that the nighttime G0 improves with coefficient of determination (R2) from 0.17 to 0.66 by HO method over maize and from 0.09 to 0.81 over grassland, and root mean square error decreases from 24.5 to 16.6 W m − 2, 16.1 to 8.68 W m − 2 over maize and grassland, respectively. Regional G0 for bare soil is larger than the vegetation surface, and values in the middle reach area of HRB are generally higher than the upper reach area because of larger fractional vegetation cover in the upper reach. For maize, grassland, and vegetable surfaces (all are dense vegetation in July in this study), the combined method performed better when using Tc (almost coincides with remote sensing measurement land surface temperature) than estimated Ts by a simple empirical formula provided by Murray and Verhoef. The combined method gets more accurate diurnal G0 than individual HM. However, a more theoretical relationship of Tc and Ts is required when more accurate G0 is needed in other studies.