Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV

Abstract

Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψₛₜₑₘ, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψₛₜₑₘ was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψₛₜₑₘ values with an R² of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.

Plant phenotyping relevance

UAV搭載NIR/SWIRハイパースペクトル画像と機械学習により、ブドウの茎水ポテンシャルという植物生理形質を推定し、回帰・分類性能を検証している。表現型取得法と技術評価が研究の中心である。

Code and data availability

The paper's hyperspectral UAV imagery, Ψstem field measurements, and analysis code are not publicly deposited; the authors state data are available only upon request from the corresponding author.

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