A fully remote sensing-based implementation of the two-source energy balance model: an application over Mediterranean crops

Authors: CAMMALLERI, C - University Of Milan; Anderson, Martha; BAMBACH, N - University Of California, Davis; McElrone, Andrew; Knipper, Kyle; Roby, Matthew; CIRAOLO, G - University Of Palermo Italy; DECARO, D - University Of Palermo Italy; IPPOLITO, M - University Of Palermo Italy; CORBARI, C - University Of Milan; CEPPI, A - University Of Milan; MANCINI, M - University Of Milan; Kustas, William

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/28/2024
Publication Date: 12/3/2024
Citation: Cammalleri, C., Anderson, M.C., Bambach, N., Mcelrone, A.J., Knipper, K.R., Roby, M.C., Ciraolo, G., Decaro, D., Ippolito, M., Corbari, C., Ceppi, A., Mancini, M., Kustas, W.P. 2024. A fully remote sensing-based implementation of the two-source energy balance model: an application over Mediterranean crops. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2024.109207.
DOI: https://doi.org/10.1016/j.agwat.2024.109207

Interpretive Summary: Geospatial estimates of evapotranspiration (ET, the flux of water vapor between the land and the atmosphere) derived from remote sensing are increasing in demand in agriculture and water resource management. Particularly when generated at field scale, ET maps characterize crop water use, providing useful irrigation decision support and serving as an indicator of crop health over agricultural landscape. Many ET remote sensing models use land-surface temperature as a key input, tying evaporative cooling to the temperature of the evaporating surface. However, such models also require information about air temperature relative to the surface to accurately define that cooling effect, and these data are difficult to obtain for field scales of application. This paper describes a creative simplified approach to deducing the air temperature model inputs from the modeling scene itself, thereby creating a mode of application with limited need for ancillary information. The method is tested over several flux measurement sites in Italy and the U.S. in irrigated cropping systems and was found to perform well, even given the reduced information set needed. This suggests utility for practical operational irrigation decision support in areas lacking extensive weather station network infrastructure.

Technical Abstract: The use of wet and dry boundary conditions, commonly referred to as hot and cold pixels, is widely used in thermal-based single-source surface energy balance models for defining the relationship between satellite land-surface temperature (LST) and surface-atmosphere temperature gradient used to compute sensible heat flux. This contextual scaling approach reduces model sensitivity to biases in the LST retrievals. The possibility to integrate this approach within the two-source energy balance (TSEB) scheme is here investigated, with the goal of removing the need to derive air temperature and wind speed from auxiliary sources. An automatic procedure to retrieve the two boundary temperatures is proposed, returning temperature values that are unbiased compared to ideal estimations (from in-situ observations), and characterized by deviations on the order of 1.5 and 4.5 °C for cold and hot conditions, respectively. The results show how the lower accuracy in the hot pixel temperature does not seem to significantly affect the overall capability of the model to reproduce observed fluxes, with error in instantaneous sensible and latent heat fluxes in the order of 60 W m-2 (slightly above 1 mm d-1 on daily evapotranspiration) over a set of 16 sites in US and Italy characterized by typical Mediterranean crops. The proposed TSEB implementation is fully remote sensing based, in the sense that satellite-consistent retrievals of air temperature and wind speed are directly obtained from information available within the satellite scene itself, and it represents a suitable alternative in case of lack of reliable sources for meteorological data.