Exploratory analysis of vineyard leaf water potential against sUAS multispectral and temperature information

Rui Gao; Maria Mar Alsina; Alfonso F. Torres-Rua; Lawrence Hipps; William P. Kustas; William A. White; Martha Anderson; Joseph Alfieri; Nick Dokoozlian; Hector Nieto; Feng Gao; Lynn G. McKee; John H. Prueger; Luis Sanchez; Andrew J. Mcelrone; Nicolas Bambach Ortiz; Calvin Coopmans; Ian Gowing

doi:10.1117/12.2622995

3 June 2022 Exploratory analysis of vineyard leaf water potential against sUAS multispectral and temperature information

Rui Gao, Maria Mar Alsina, Alfonso F. Torres-Rua, Lawrence Hipps, William P. Kustas, William A. White, Martha Anderson, Joseph Alfieri, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn G. McKee, John H. Prueger, Luis Sanchez, Andrew J. Mcelrone, Nicolas Bambach Ortiz, Calvin Coopmans, Ian Gowing

Author Affiliations +

Proceedings Volume 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII; 121140K (2022) https://doi.org/10.1117/12.2622995
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Leaf water potential (Ψ_l) in vineyards and orchards is a well-known indicator of plant water status and stress and it is commonly used by growers to make immediate crop and water management decisions. However, Ψ_l measurement via the direct method presents challenges as it is labor and time intensive and represents leaf-level conditions for only a small sampling of the vineyard or orchard block. Models are existed for vegetation water status prediction by using optical and thermal images. Considering this, a small unmanned aerial system (sUAS) can potentially collect those data and help to build a predictive model at a high resolution. In this study, we identify relationships and trends of vineyard Ψ_l and sUAS imagery at different times of the day and throughout the growing season in California. This study examines aerial and ground measurements collected by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) program over a period of eight years across California to build multivariable models for real time water status prediction. This preliminary analysis looks at spatial and temporal trends using a stepwise regression between leaf-level Ψ_l measurements and sUAS optical, thermal, and elevation data to identify potential predictors of Ψ_l, thus enabling mapping of Ψl at the scale of individual grapevines across the block. Such predictive models could be used to map the spatial variability in Ψ_l across multiple blocks during the growing season and at critical phenological stages in real time and improve the targeting of irrigation applications for vineyards and other perennial crops.

Conference Presentation

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Rui Gao, Maria Mar Alsina, Alfonso F. Torres-Rua, Lawrence Hipps, William P. Kustas, William A. White, Martha Anderson, Joseph Alfieri, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn G. McKee, John H. Prueger, Luis Sanchez, Andrew J. Mcelrone, Nicolas Bambach Ortiz, Calvin Coopmans, and Ian Gowing "Exploratory analysis of vineyard leaf water potential against sUAS multispectral and temperature information", Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 121140K (3 June 2022); https://doi.org/10.1117/12.2622995

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