Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards
Evapotranspiration (<i>ET</i>) 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...
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| Format: | Article |
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/342 |
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| author | Ayman Nassar Alfonso Torres-Rua William Kustas Hector Nieto Mac McKee Lawrence Hipps David Stevens Joseph Alfieri John Prueger Maria Mar Alsina Lynn McKee Calvin Coopmans Luis Sanchez Nick Dokoozlian |
| author_facet | Ayman Nassar Alfonso Torres-Rua William Kustas Hector Nieto Mac McKee Lawrence Hipps David Stevens Joseph Alfieri John Prueger Maria Mar Alsina Lynn McKee Calvin Coopmans Luis Sanchez Nick Dokoozlian |
| author_sort | Ayman Nassar |
| collection | DOAJ |
| description | Evapotranspiration (<i>ET</i>) 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 (<i>sUAS</i>) with sensor technology similar to satellite platforms allows for the estimation of high-resolution <i>ET</i> at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate <i>ET</i> from <i>sUAS</i> products, the sensitivity of <i>ET</i> 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 <i>sUAS</i> imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (<i>TSEB2T</i>) model, which uses remotely sensed soil/substrate and canopy temperature from <i>sUAS</i> imagery, was used to estimate <i>ET</i> 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 <i>AggieAir<sup>TM</sup> sUAS</i> 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 (<i>GRAPEX</i>). Original spectral and thermal imagery data from <i>sUAS</i> 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 (<i>EC</i>) measurements. Results indicated that the <i>TSEB2T</i> model is only slightly affected in the estimation of the net radiation (<i>R<sub>n</sub></i>) and the soil heat flux (<i>G</i>) at different spatial resolutions, while the sensible and latent heat fluxes (<i>H</i> and <i>LE</i>, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of <i>H</i> and underestimation of <i>LE</i> values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (<i>LST</i>) and the normalized difference vegetation index (<i>NDVI</i>) at coarse model resolution. Another predominant reason for <i>LE</i> reduction in <i>TSEB2T</i> was the decrease in the aerodynamic resistance (<i>R<sub>a</sub></i>), which is a function of the friction velocity (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>u</mi> <mo>*</mo> </msub> </mrow> </semantics> </math> </inline-formula>) 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 <i>sUAS</i> imagery. The results also indicated that the mean <i>LE</i> at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in <i>LE</i> values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of <i>LE</i> are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications. |
| first_indexed | 2024-04-11T16:34:22Z |
| format | Article |
| id | doaj.art-04c97046e20d4dde8f6a38cfca8a33a4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:34:22Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-04c97046e20d4dde8f6a38cfca8a33a42022-12-22T04:13:52ZengMDPI AGRemote Sensing2072-42922020-01-0112334210.3390/rs12030342rs12030342Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in VineyardsAyman Nassar0Alfonso Torres-Rua1William Kustas2Hector Nieto3Mac McKee4Lawrence Hipps5David Stevens6Joseph Alfieri7John Prueger8Maria Mar Alsina9Lynn McKee10Calvin Coopmans11Luis Sanchez12Nick Dokoozlian13Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USADepartment of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USAU. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAComplutum Tecnologías de la Información Geográfica (COMPLUTIG), 28801 Madrid, SpainDepartment of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USAPlants, Soils and Climate Department, Logan, UT 84322, USADepartment of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USAU. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAU. S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USAE & J Gallo Winery Viticulture Research, Modesto, CA 95354, USAU. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USADepartment of Electrical Engineering, Utah State University, Logan, UT 84322, USAE & J Gallo Winery Viticulture Research, Modesto, CA 95354, USAE & J Gallo Winery Viticulture Research, Modesto, CA 95354, USAEvapotranspiration (<i>ET</i>) 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 (<i>sUAS</i>) with sensor technology similar to satellite platforms allows for the estimation of high-resolution <i>ET</i> at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate <i>ET</i> from <i>sUAS</i> products, the sensitivity of <i>ET</i> 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 <i>sUAS</i> imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (<i>TSEB2T</i>) model, which uses remotely sensed soil/substrate and canopy temperature from <i>sUAS</i> imagery, was used to estimate <i>ET</i> 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 <i>AggieAir<sup>TM</sup> sUAS</i> 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 (<i>GRAPEX</i>). Original spectral and thermal imagery data from <i>sUAS</i> 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 (<i>EC</i>) measurements. Results indicated that the <i>TSEB2T</i> model is only slightly affected in the estimation of the net radiation (<i>R<sub>n</sub></i>) and the soil heat flux (<i>G</i>) at different spatial resolutions, while the sensible and latent heat fluxes (<i>H</i> and <i>LE</i>, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of <i>H</i> and underestimation of <i>LE</i> values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (<i>LST</i>) and the normalized difference vegetation index (<i>NDVI</i>) at coarse model resolution. Another predominant reason for <i>LE</i> reduction in <i>TSEB2T</i> was the decrease in the aerodynamic resistance (<i>R<sub>a</sub></i>), which is a function of the friction velocity (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>u</mi> <mo>*</mo> </msub> </mrow> </semantics> </math> </inline-formula>) 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 <i>sUAS</i> imagery. The results also indicated that the mean <i>LE</i> at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in <i>LE</i> values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of <i>LE</i> are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.https://www.mdpi.com/2072-4292/12/3/342evapotranspiration (<i>et</i>)<i>grapex</i><i>suas</i>remote sensingtwo source energy balance model (<i>tseb</i>)contextual spatial domain/resolutiondata aggregationeddy covariance (<i>ec</i>) |
| spellingShingle | Ayman Nassar Alfonso Torres-Rua William Kustas Hector Nieto Mac McKee Lawrence Hipps David Stevens Joseph Alfieri John Prueger Maria Mar Alsina Lynn McKee Calvin Coopmans Luis Sanchez Nick Dokoozlian 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 evapotranspiration (<i>et</i>) <i>grapex</i> <i>suas</i> remote sensing two source energy balance model (<i>tseb</i>) contextual spatial domain/resolution data aggregation eddy covariance (<i>ec</i>) |
| title | Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards |
| title_full | Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards |
| title_fullStr | Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards |
| title_full_unstemmed | Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards |
| title_short | Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards |
| title_sort | influence of model grid size on the estimation of surface fluxes using the two source energy balance model and suas imagery in vineyards |
| topic | evapotranspiration (<i>et</i>) <i>grapex</i> <i>suas</i> remote sensing two source energy balance model (<i>tseb</i>) contextual spatial domain/resolution data aggregation eddy covariance (<i>ec</i>) |
| url | https://www.mdpi.com/2072-4292/12/3/342 |
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