Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards.

Evapotranspiration () is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System () with sensor technology similar to satellite platforms allows for the estimation of high-resolution at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate from products, the sensitivity of models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature () model, which uses remotely sensed soil/substrate and canopy temperature from imagery, was used to estimate and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (). Original spectral and thermal imagery data from were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance () measurements. Results indicated that the model is only slightly affected in the estimation of the net radiation ( ) and the soil heat flux () at different spatial resolutions, while the sensible and latent heat fluxes ( and , respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of and underestimation of values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature () and the normalized difference vegetation index () at coarse model resolution. Another predominant reason for reduction in was the decrease in the aerodynamic resistance ( ), which is a function of the friction velocity F) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the imagery. The results also indicated that the mean at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.