Leaf water potential (Ψl) 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, Ψl 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 Ψl 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 Ψl measurements and sUAS optical, thermal, and elevation data to identify potential predictors of Ψl, 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 Ψl 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.
Evapotranspiration (ET) is a crucial part of hydrological cycling, and its (ET) partitioning allows separate assessment of soil and plant water, energy, and carbon fluxes. ET partitioning plays an important role in agriculture since it is related to yield quality, irrigation efficiency, and plant growth. Satellite remote-sense-based methods provide an opportunity for ET partitioning at a subfield scale. However, one challenge is the resolution of the remote sensing data and relating these results to vine plants and irrigation subfield sections (valve units). With the small unmanned aerial system - sUAS such as AggieAir from Utah State University, ET and ET partition estimation via the two-source energy balance (TSEB) model at vineyards can be achieved. In this study, assessment of ET and ET partitioning, soil water evaporation and plant transpiration, using the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) information and aerial high-resolution imagery from AggieAir team will be performed. In specific, the performance of two implementations of the TSEB model: TSEB-PT (Priestley-Taylor approach) and TSEB-2T (dual temperature approach) on ET partitioning at a subfield scale will be evaluated, using ground and recently developed machine-learning-based Grapevine LAI imagery. The performance of the TSEB implementations will be documented for three major areas in California called Barelli, Sierra Loma, and Ripperdan, which encompasses major climatological regions in the state.
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