ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Author:

Gao Rui1ORCID,Torres-Rua Alfonso F.1ORCID,Nieto Hector2ORCID,Zahn Einara3,Hipps Lawrence4,Kustas William P.5ORCID,Alsina Maria Mar6ORCID,Bambach Nicolas7ORCID,Castro Sebastian J.7,Prueger John H.8,Alfieri Joseph5,McKee Lynn G.5,White William A.5ORCID,Gao Feng5,McElrone Andrew J.79,Anderson Martha5,Knipper Kyle10ORCID,Coopmans Calvin11,Gowing Ian1,Agam Nurit12ORCID,Sanchez Luis6,Dokoozlian Nick6

Affiliation:

1. Civil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA

2. Institute of Agricultural Sciences—CSIC, 28006 Madrid, Spain

3. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA

4. Plants, Soils, and Climate Department, Utah State University, Old Main Hill, Logan, UT 84322, USA

5. U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA

6. E & J Gallo Winegrowing Research, Modesto, CA 95354, USA

7. Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA

8. U.S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USA

9. USDA-Agricultural Research Service, Davis, CA 95616, USA

10. Sustainable Agriculture Water Systems Research Unit, USDA ARS, Davis, CA 95616, USA

11. Electrical and Computer Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA

12. Blaustein Institutes for Desert Research, Sede-Boqer Campus, Ben-Gurion University of the Negev, Be'er Sheva 84990, Israel

Abstract

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the 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 such as 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 the 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 (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. 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 from 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 by 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.

Funder

USDA-Agricultural Research Service, E&J Gallo Winery

NASA Applied Sciences Water Resources

Utah Water Research Laboratory Student Fellowship

Utah Water Research Laboratory

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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