On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables

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.