Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information

Rui Gao, Alfonso Torres-Rua, Ayman Nassar, Joseph Alfieri, Mahyar Aboutalebi, Lawrence Hipps, Nicolas Bambach Ortiz, Andrew J. McElrone, Calvin Coopmans, William Kustas, William White, Lynn McKee, Maria Del Mar Alsina, Nick Dokoozlian, Luis Sanchez, John H. Prueger, Hector Nieto, Nurit Agam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6- meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the sixvariable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB- 2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

Original languageEnglish
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI
EditorsJ. Alex Thomasson, Alfonso F. Torres-Rua
PublisherSPIE
ISBN (Electronic)9781510643314
DOIs
StatePublished - 1 Jan 2021
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI 2021 - Virtual, Online, United States
Duration: 12 Apr 202116 Apr 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11747
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/04/2116/04/21

Keywords

  • EC tower data
  • ET partitioning
  • LAI
  • TSEB models
  • XGBoost

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