Evaluation of the use of two-stage calibrated PlanetScope images and environmental variables for the development of the grapevine water status prediction model

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Abstract Grapevine water status (GWS) assessment between flowering and veraison plays an important role in viticulture management in terms of producing high-quality grapes. Although satellites and uncrewed aerial vehicles (UAV) have successfully monitored GWS, these platforms are practically limited because data transfer is delayed due to post processing and UAV operation is weather dependent. This study focuses on addressing two issues: the unreliability of GWS estimation using satellite images with low-moderate spatial resolution and the inaccessibility of real-time satellite data. It aims to predict the temporal variation of GWS based on a prediction model using spectral information (calibrated PlanetScope (PS) images), soil/topography data (apparent electrical conductivity, elevation, slope), weather parameters (rainfall and potential evapotranspiration), cultivation practices (irrigation, fertigation, plucking, and trimming), and seasonality (day of the year) as predictors. Stem water potential (Ψstem) was used as a proxy for GWS. Two-stage calibration, including an initial calibration of UAV images with measured Ψstem and a subsequent calibration of satellite images with calibrated UAV data, was applied to calibrate the PS images. Three machine learning models (random forest regression, support vector regression, and multilayer perceptron) were used in the calibration and modeling process. The results showed that a two-stage calibration can generate reliable reference data, with a root mean square error of 113 kPa and 59 kPa on the test sets during the first and second calibration stage, respectively. The prediction model described the temporal variation of block Ψstem when compared with the measured Ψstem. In the similarity analysis, the Pearson correlation coefficient was 0.89 and 0.87 between predicted and reference Ψstem maps across four dates for the two study vineyards. This study supports the concept of developing an approach to predict grapevine Ψstem, which would enable growers to acquire Ψstem variation in advance during the growing season, leading to improved irrigation scheduling and optimal grape quality.
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Technology in Agronomy, 2023, 3 (6), pp. ? - ? (14)