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H. Tanaka, T. Hiyama, K. Yamamoto, H. Fujinami, T. Shinoda, A. Higuchi, S. Endo, S. Ikeda, W. Li and K. Nakamura, “Surface Flux and Atmospheric Boundary Layer Observations from the LAPS Project over the Middle Stream of the Huaihe River Basin in China,” Hydrological Processes, Vol. 21, No. 15, 2007, pp. 1997-2008. http://dx.doi.org/10.1002/hyp.6706

has been cited by the following article:

• TITLE: Linking Surface Temperature Based Approaches for Estimating Soil Heat Flux with Error Propagation

  AUTHORS: Panpan Lu, Yuanbo Liu, Tetsuya Hiyama

  KEYWORDS: Soil Heat Flux; Land Surface Temperature; Remote Sensing

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.4 No.1, December 20, 2013

  ABSTRACT: Soil heat flux is an inseparable component of the surface energy balance. Accurate estimation of regional soil heat flux is valuable to studies of meteorology and hydrology. Conventional measurement of using soil heat flux plates at the site scale is impractical to estimate large-scale flux. Other approaches generally require soil temperature tobe measured in at least two soil layers, which is also difficult to implement at the regional scale. In the last decade, single-layer based approaches were developed to fulfill the regional requirement. This study used a simple but more general approach for estimating soil heat flux solely with surface temperature. The generalized approach can be conditionally linked to two existing single-layer based approaches but has fewer restrictions or assumptions. Error analysis revealed that measurement error in surface temperature would have limited effects on soil heat flux estimated from the new approach. Model simulations showed that soil heat flux estimated from the approach agreed with those simulated from the heat transfer equation. Furthermore, case examinations at two sites with contrasting climate regimes demonstrated that the generalized approach had better performance than the existing single-layer approaches. It achieved the highest correlation of determination and the lowest mean, standard deviation, and root mean squared error of the differences between the estimates and the field measures at either site. The generalized approach can estimate soil heat flux at a depth but it requires only surface temperature data as input, which is an advantage to remote sensing applications.