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Remote Sensing of Evapotranspiration and Water Stress of Woody Perennial Crops in Water-Limited Regions

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5982

Special Issue Editors

Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84321, USA
Interests: water resources management; water resources engineering; watershed management; flood modelling; evapotranspiration; water resources

E-Mail Website
Guest Editor
Hydrology and Remote Sensing Lab, USDA ARS, Beltsville, MD 20705, USA
Interests: land-atmosphere exchange processes; remote sensing; hydrology

Special Issue Information

Dear Colleagues,

Woody perennial crops, such as grape vines and various orchard crops, represent an economically important fraction of agricultural production in the US and around the world. Irrigation scheduling is often seen as the single most important tool for the management of both yield and quality in the production of these crops, but the combined effects of global climate change and growing demands for water have begun to threaten the long-term viability of continued production levels in some regions. Timely knowledge of evapotranspiration (ET) rates and plant water stress status can be very useful in irrigation scheduling decisions, but the use of remote sensing (RS) technologies for the accurate estimation of these factors for most woody perennial crops is difficult for various scientific and technical reasons. We lack a sufficient understanding of such things as the effects of inconsistencies in the spatial, temporal, and spectral resolution of different types of sensors that prove difficult to resolve; how to accurately separate crop canopy from interrow ET, especially for ET models based on coarse-resolution satellite imagery; how to accurately account for differences in cultivars, irrigation methods, and plant management technologies in modeling ET and ET components; the relationship of crop canopy ET to stress conditions; and the effects of the formation and evolution of atmospheric boundary layers—especially at the edges of large irrigated crop production areas—on diurnal ET rates and biophysical processes.  This Special Issue will address these and other scientific challenges for the use of RS technologies in ET estimation for agricultural water management.

Dr. Mac McKee
Dr. Joseph G. Alfieri
Guest Editors

Manuscript Submission Information

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Keywords

  • evapotranspiration
  • transpiration
  • woody perennial crops
  • remote sensing
  • energy balance models
  • machine learning models
  • boundary layers
  • land surface–atmospheric exchanges
  • spatial scale effects
  • data fusion
  • upscaling
  • downscaling
  • plant water stress
  • biomass influence
  • advection processes
  • irrigation management

Published Papers (2 papers)

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Research

20 pages, 4023 KiB  
Article
ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards
by 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 and Nick Dokoozlianadd Show full author list remove Hide full author list
Remote Sens. 2023, 15(3), 756; https://doi.org/10.3390/rs15030756 - 28 Jan 2023
Cited by 9 | Viewed by 2828
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 [...] Read more.
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. Full article
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26 pages, 10150 KiB  
Article
Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California
by Kyle Knipper, Martha Anderson, Nicolas Bambach, William Kustas, Feng Gao, Einara Zahn, Christopher Hain, Andrew McElrone, Oscar Rosario Belfiore, Sebastian Castro, Maria Mar Alsina and Sebastian Saa
Remote Sens. 2023, 15(1), 68; https://doi.org/10.3390/rs15010068 - 23 Dec 2022
Cited by 8 | Viewed by 2621
Abstract
Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. [...] Read more.
Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems. Full article
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