Abstract
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 (Rn) 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 Rn with air temperature (Tair) and crop height (hc) better reproduced measured values when three independent variables were considered. The combination of Rn with leaf area index (LAI) from remote sensing, and Rn with surface aerodynamic resistance (rah) yielded relative larger overall correlation coefficient values when two independent variables were included using ANN. In addition, air temperature (Tair) 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 Rn, LAI, rah and hc and potentially Tair be considered in future modeling studies of G.
Highlights
There are several available models to spatially estimate the surface energy balance components from remote sensing [1]
The Testing component is the only one that uses unseen data and, for most cases, the performance of the model was not very good (r and R2
Artificial Neural Networks (ANNs) represent a novel technique that can be successfully implemented to estimate Soil Heat Flux (G) as preliminary demonstrated in this study and evidenced by the performance of the constructed models
Summary
There are several available models to spatially estimate the surface energy balance components from remote sensing [1]. The soil heat flux (G) models have only been applied, with different degrees of success, in conditions similar to those in which they were developed (e.g., a given vegetation type, soil background, and environmental conditions) [2,3]. There is a need to further understand the variables that explain G for a range of surface crops and for different environmental and climatic conditions. Net radiation (Rn) estimates from remote sensing are fairly accurate according to Neale et al [4], but soil and sensible heat flux (H) estimates need more research [5,6]. The models were a function of vegetation indices and a budget of short and long wave radiation. Results of that study indicated that the tested G models under predicted measured G with mean bias errors and root mean square errors (MBE ± RMSE) varying from
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