Soil heat flux modeling using artificial neural networks and multispectral airborne remote sensing imagery

The estimation of spatially distributed crop water use or evapotranspiration (ET) can be achieved using the energy balance for land surface algorithm and multispectral imagery obtained from remote sensing sensors mounted on air- or space-borne platforms. In the energy balance model, net radiation (R_n) is well estimated using remote sensing; however, the estimation of soil heat flux (G) has had mixed results. Therefore, there is the need to improve the model to estimate soil heat flux and thus improve the efficiency of the energy balance method based on remote sensing inputs. In this study, modeling of airborne remote sensing-based soil heat flux was performed using Artificial Neural Networks (ANN). Soil heat flux was modeled using selected measured data from soybean and corn crop covers in Central Iowa, U.S.A. where measured values were obtained with soil heat flux plate sensors. Results in the modeling of G indicated that the combination R_n with air temperature (T_air) and crop height (h_c) better reproduced measured values when three independent variables were considered. The combination of R_n with leaf area index (LAI) from remote sensing, and R_n with surface aerodynamic resistance (r_ah) yielded relative larger overall correlation coefficient values when two independent variables were included using ANN. In addition, air temperature (T_air) may be a key variable in the modeling of G as suggested by the ANN application (r of 0.83). Therefore, it is suggested that R_n, LAI, r_ah and h_c and potentially T_air be considered in future modeling studies of G.