### Abstract

- Top of page
- Abstract
- 1. Introduction and Background
- 2. Materials and Methods
- 3. Result and Discussion
- 4. Conclusions
- References
- Supporting Information

[1] The 1-D and single layer combination-based energy balance Penman-Monteith (PM) model has limitations in practical application due to the lack of canopy resistance (*r*_{c}) data for different vegetation surfaces. *r*_{c} could be estimated by inversion of the PM model if the actual evapotranspiration (*E*_{Ta}) rate is known, but this approach has its own set of issues. Instead, an empirical method of estimating *r*_{c} is suggested in this study. We investigated the relationships between primary micrometeorological parameters and *r*_{c} and developed seven models to estimate *r*_{c} for a nonstressed maize canopy on an hourly time step using a generalized-linear modeling approach. The most complex *r*_{c} model uses net radiation (*R*_{n}), air temperature (*T*_{a}), vapor pressure deficit (VPD), relative humidity (RH), wind speed at 3 m (*u*_{3}), aerodynamic resistance (*r*_{a}), leaf area index (LAI), and solar zenith angle (Θ). The simplest model requires *R*_{n}, *T*_{a}, and RH. We present the practical implementation of all models via experimental validation using scaled up *r*_{c} data obtained from the dynamic diffusion porometer-measured leaf stomatal resistance through an extensive field campaign in 2006. For further validation, we estimated *E*_{Ta} by solving the PM model using the modeled *r*_{c} from all seven models and compared the PM *E*_{Ta} estimates with the Bowen ratio energy balance system (BREBS)-measured *E*_{Ta} for an independent data set in 2005. The relationships between hourly *r*_{c} versus *T*_{a}, RH, VPD, *R*_{n}, incoming shortwave radiation (*R*_{s}), *u*_{3}, wind direction, LAI, Θ, and *r*_{a} were presented and discussed. We demonstrated the negative impact of exclusion of LAI when modeling *r*_{c}, whereas exclusion of *r*_{a} and Θ did not impact the performance of the *r*_{c} models. Compared to the calibration results, the validation root mean square difference between observed and modeled *r*_{c} increased by 5 s m^{−1} for all *r*_{c} models developed, ranging from 9.9 s m^{−1} for the most complex model to 22.8 s m^{−1} for the simplest model, as compared with the observed *r*_{c}. The validation *r*^{2} values were close to 0.70 for all models, and the modeling efficiency ranged from 0.61 for the most complex model to −1.09 for the simplest model. There was a strong agreement between the BREBS-measured and the PM-estimated *E*_{Ta} using modeled *r*_{c}. These findings can aid in the selection of a suitable model based on the availability and quality of the input data to predict *r*_{c} for one-step application of the PM model to estimate *E*_{Ta} for a nonstressed maize canopy.