Latent Heat and Sensible Heat Fluxes Simulation in Maize Using Artificial Neural Networks

Abstract:

Latent Heat (LE) and Sensible Heat (H) flux are two major components of the energy balance at the earth’s surface which play important roles in the water cycle and global warming. There are various methods for their estimation or measurement. Eddy covariance is a direct and accurate technique for their measurement. Some limitations lead to prevention of the extensive use of the eddy covariance technique. Therefore, simulation approaches can be utilized for their estimation. ANNs are the information processing systems, which can inspect the empirical data and investigate the relations (hidden rules) among them, and then make the network structure.

In this study, multi-layer perceptron neural network trained by the steepest descent Back-Propagation (BP) algorithm was tested to simulate LE and H flux above two maize sites (rain-fed & irrigated) near Mead, Nebraska.

Network training and testing was fulfilled using hourly data of including year, local time of day (DTime), leaf area index (LAI), soil water content (SWC) in 10 and 25 cm depths, soil temperature (Ts) in 10 cm depth, air temperature (Ta), vapor pressure deficit (VPD), wind speed (WS), irrigation and precipitation (P), net radiation (Rn), and the fraction of incoming Photosynthetically Active Radiation (PAR) absorbed by the canopy (fPAR), which were selected from days of year (DOY) 169 to 222 for 2001, 2003, 2005, 2007, and 2009.

The results showed high correlation between actual and estimated data; the R² values for LE flux in irrigated and rain-fed sites were 0.9576, and 0.9642; and for H flux 0.8001, and 0.8478, respectively. Furthermore, the RMSE values ranged from 0.0580 to 0.0721 W/m² for LE flux and from 0.0824 to 0.0863 W/m² for H flux. In addition, the sensitivity of the fluxes with respect to each input was analyzed over the growth stages. Thus, the most powerful effects among the inputs for LE flux were identified net radiation, leaf area index, vapor pressure deficit, wind speed, and for H flux net radiation, wind speed, air temperature, leaf area index and vapor pressure deficit. This study reveals that artificial neural networks are not only a powerful technique for estimation of LE and H fluxes, but also can identify the effectiveness of each input on the fluxes.