ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards

Authors: GAO, R. - UTAH STATE UNIVERSITY; TORRES-RUA, A. - UTAH STATE UNIVERSITY; NIETO, H. - INSTITUTE OF AGRICULTURAL SCIENCES; ZAHN, E. - PRINCETON UNIVERSITY; HIPPS, L. - UTAH STATE UNIVERSITY; Kustas, William - Bill; ALSINA, M. - E & J GALLO WINERY; ORTIZ, N. - UNIVERSITY OF CALIFORNIA, DAVIS; CASTRO, S. - UNIVERSITY OF CALIFORNIA, DAVIS; PRUEGER, J. - OAK RIDGE INSTITUTE FOR SCIENCE AND EDUCATION (ORISE); Alfieri, Joseph; McKee, Lynn; White, William - Alex; Gao, Feng; McElrone, Andrew; Anderson, Martha; Knipper, Kyle; COOPMANS, C. - UTAH STATE UNIVERSITY; GOWING, I. - UTAH STATE UNIVERSITY; AGAM, N. - BEN GURION UNIVERSITY OF NEGEV; SANCHEZ, L. - E & J GALLO WINERY; DOKOOZLIAN, N. - E & J GALLO WINERY

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/24/2023
Publication Date: 1/28/2023
Citation: Gao, R., Torres-Rua, A., Nieto, H., Zahn, E., Hipps, L., Kustas, W.P., Alsina, M., Ortiz, N., Castro, S., Prueger, J., Alfieri, J.G., McKee, L.G., White, W.A., Gao, F.N., McElrone, A.J., Anderson, M.C., Knipper, K.R., Coopmans, C., Gowing, I., Agam, N., Sanchez, L., Dokoozlian, N. 2023. ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards. Remote Sensing. 15(3). Article 756. https://doi.org/10.3390/rs15030756.
DOI: https://doi.org/10.3390/rs15030756

Interpretive Summary: The partitioning of evapotranspiration (ET) into soil evaporation and plant transpiration in agriculture is important for determining crop stress, yield, quality, irrigation efficiency, and growth. This is particularly important for wine grapes and other fruit and nut crops grown in water limited regions such as the California Central Valley which produces nearly three-quarters of the fruit and nuts for the United States. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale, but are not able to explicitly distinguish the contribution of soil evaporation and plant transpiration to the total ET. However, very fine resolution imagery from a small unmanned aerial system (sUAS) can distinguish plant transpiration from interrow soil evaporation. Such sUAS imagery was used with the two-source energy balance (TSEB) model and a new method for soil and plant temperature partitioning that incorporates a quantile technique separation (QTS) algorithm over vineyards in California. Comparisons with micrometeorological ET partitioning approaches indicate the TSEB-QTS modeling approach with sUAS imagery can provide reliable vine transpiration and interrow evaporation that can used to refine satellite-based approaches for large area mapping of plant transpiration for vine and potentially other fruit and nut cropping systems.

Technical Abstract: Evapotranspiration (ET) is a crucial part of commercial grapevine production in California and partitioning of this quantity allows separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms like Landsat is often insufficient for precision agricultural management at the plant scale. Small unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration) considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method coupled with the TSEB model (called TSEB-2TQ) improved sensible heat flux estimation. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation - MREA; Flux Variance Similarity - FVS; and Conditional Eddy Covariance - CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different with the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled from different TSEB models, the TSEB-2TQ shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2TQ coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.