ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards
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, y...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/3/756 |
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| author | Rui Gao Alfonso F. Torres-Rua Hector Nieto Einara Zahn Lawrence Hipps William P. Kustas Maria Mar Alsina Nicolas Bambach Sebastian J. Castro John H. Prueger Joseph Alfieri Lynn G. McKee William A. White Feng Gao Andrew J. McElrone Martha Anderson Kyle Knipper Calvin Coopmans Ian Gowing Nurit Agam Luis Sanchez Nick Dokoozlian |
| author_facet | Rui Gao Alfonso F. Torres-Rua Hector Nieto Einara Zahn Lawrence Hipps William P. Kustas Maria Mar Alsina Nicolas Bambach Sebastian J. Castro John H. Prueger Joseph Alfieri Lynn G. McKee William A. White Feng Gao Andrew J. McElrone Martha Anderson Kyle Knipper Calvin Coopmans Ian Gowing Nurit Agam Luis Sanchez Nick Dokoozlian |
| author_sort | Rui Gao |
| collection | DOAJ |
| description | 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-2T<sub>Q</sub>), 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-2T<sub>Q</sub> shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T<sub>Q</sub> 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. |
| first_indexed | 2024-03-11T09:27:05Z |
| format | Article |
| id | doaj.art-43587d4803d34b3d94d503be2a7978df |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:27:05Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-43587d4803d34b3d94d503be2a7978df2023-11-16T17:53:47ZengMDPI AGRemote Sensing2072-42922023-01-0115375610.3390/rs15030756ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley VineyardsRui Gao0Alfonso F. Torres-Rua1Hector Nieto2Einara Zahn3Lawrence Hipps4William P. Kustas5Maria Mar Alsina6Nicolas Bambach7Sebastian J. Castro8John H. Prueger9Joseph Alfieri10Lynn G. McKee11William A. White12Feng Gao13Andrew J. McElrone14Martha Anderson15Kyle Knipper16Calvin Coopmans17Ian Gowing18Nurit Agam19Luis Sanchez20Nick Dokoozlian21Civil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USACivil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USAInstitute of Agricultural Sciences—CSIC, 28006 Madrid, SpainDepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USAPlants, Soils, and Climate Department, Utah State University, Old Main Hill, Logan, UT 84322, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAE & J Gallo Winegrowing Research, Modesto, CA 95354, USADepartment of Land, Air, and Water Resources, University of California, Davis, CA 95616, USADepartment of Land, Air, and Water Resources, University of California, Davis, CA 95616, USAU.S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USADepartment of Land, Air, and Water Resources, University of California, Davis, CA 95616, USAU.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USASustainable Agriculture Water Systems Research Unit, USDA ARS, Davis, CA 95616, USAElectrical and Computer Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USACivil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USABlaustein Institutes for Desert Research, Sede-Boqer Campus, Ben-Gurion University of the Negev, Be'er Sheva 84990, IsraelE & J Gallo Winegrowing Research, Modesto, CA 95354, USAE & J Gallo Winegrowing Research, Modesto, CA 95354, USAEvapotranspiration (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-2T<sub>Q</sub>), 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-2T<sub>Q</sub> shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T<sub>Q</sub> 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.https://www.mdpi.com/2072-4292/15/3/756temperature separationET partitioningtranspirationtranspiration ratioTSEB-PTTSEB-2T |
| spellingShingle | Rui Gao Alfonso F. Torres-Rua Hector Nieto Einara Zahn Lawrence Hipps William P. Kustas Maria Mar Alsina Nicolas Bambach Sebastian J. Castro John H. Prueger Joseph Alfieri Lynn G. McKee William A. White Feng Gao Andrew J. McElrone Martha Anderson Kyle Knipper Calvin Coopmans Ian Gowing Nurit Agam Luis Sanchez Nick Dokoozlian ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards Remote Sensing temperature separation ET partitioning transpiration transpiration ratio TSEB-PT TSEB-2T |
| title | ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards |
| title_full | ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards |
| title_fullStr | ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards |
| title_full_unstemmed | ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards |
| title_short | ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards |
| title_sort | et partitioning assessment using the tseb model and suas information across california central valley vineyards |
| topic | temperature separation ET partitioning transpiration transpiration ratio TSEB-PT TSEB-2T |
| url | https://www.mdpi.com/2072-4292/15/3/756 |
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