Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #369784

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Influence of model grid size on the estimation of surface fluxes using the two source energy balance model and sUAS imagery in vineyards

Author
item NASSAR, A. - Utah State University
item TORRES, A. - Utah State University
item Kustas, William - Bill
item NIETO, H. - University Of Alcala
item MCKEE, M. - Utah State University
item HIPPS, L.E. - Utah State University
item STEVENS, D. - Utah State University
item Alfieri, Joseph
item Prueger, John
item ALSINA, M. - E & J Gallo Winery
item McKee, Lynn
item COOPMANS, C. - Utah State University
item SANCHEZ, L. - E & J Gallo Winery
item DOKOOZLIAN, N. - E & J Gallo Winery

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/10/2020
Publication Date: 1/21/2020
Citation: Nassar, A., Torres, A., Kustas, W.P., Nieto, H., McKee, M., Hipps, L., Stevens, D., Alfieri, J.G., Prueger, J.H., Alsina, M., McKee, L.G., Coopmans, C., Sanchez, L., Dokoozlian, N. 2020. Influence of model grid size on the estimation of surface fluxes using the two source energy balance model and sUAS imagery in vineyards. Remote Sensing. https://doi.org/10.3390/rs12030342.
DOI: https://doi.org/10.3390/rs12030342

Interpretive Summary: Evapotranspiration (ET) is a key factor in determining irrigation demand of croplands. Conventional ground-based methods for estimating ET are limited to sampling small areas within a field while ET typically varies spatially due to soil and vegetative conditions. Improved sensor systems and methods in remote sensing using small unmanned aerial systems (sUAS) have made this earth observation a valuable source of spatial information on ET at the canopy level. These sUAS systems not only offer very high-resolution data, but are “flexible on timing”, meaning remotely sensed information can be obtained when needed or on demand. From a computational and operational perspective, however, there would be an advantage in running ET models at much coarser resolutions than the very high native pixel size acquired from sUAS imagery. This study directly quantified the effect of sensor resolution on key ET model inputs for a vineyard and found that resolutions more than 2 times the vine row spacing caused significant differences with modeled ET at finer resolutions as well as with tower-based ET measurements. These results suggest running ET models with sUAS imagery aggregated over multiple row/interrow spacings for vineyards is not advisable and can lead to significant errors in ET estimation.

Technical Abstract: Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US and particularly California, which grows much of the high-value perennial crops in the US. The advent of sUAS with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from small Unmanned Aerial System (sUAS) products, the sensitivity of ET models to different contextual spatial domains or model grid size/resolution in complex canopies or dual crop environments, such as orchards and vineyards, is still unknown. The variability of row spacing, canopy structure and distance between fields makes this information necessary because it is difficult to process individual fields, as opposed to the entire image at a fixed resolution that is potentially larger than the plant-row separation. From a computational perspective, there would be an advantage running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance model version using remotely sensed soil/substrate and canopy temperature (TSEB2T) from very high resolution sUAS imagery as input were used to estimate ET 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 AggieAir(TM) sUAS Program over a commer¬cial vineyard located near Lodi, California as part of the USDA- Agricultural Research ServiceGrape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. Original spectral and thermal imagery data from sUAS 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 indicate that the TSEB2T model is only slightly affected in the estimation of Rn and G at different spatial resolutions, while heat fluxes (H and LE) are significantly affected by coarse grid sizes, mostly by overestimating the sensible heat flux (H) causing the underestimation of LE values, particularly at Landsat scale (30 m). 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 sUAS imagery. The results also indicate that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.