Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery - block-out and date-out cross-validation

Aug 5, 2026

Climate change is reducing grapevine productivity through limited water availability, sudden water excess, and more frequent heatwaves, making spatial and temporal knowledge of vine water status critical for adaptive viticulture. This study combines Landsat 8 imagery with weather data and a Gradient Boosting Machine to predict vine water status in large vineyard blocks. Prediction skill is evaluated for mapping and forecasting using block-out and date-out cross-validation across two Merlot growing seasons in Central California. Results show strong spatial accuracy and more challenging temporal generalization, with improved performance when adding ground data at one spatial site.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Climate change impacts on viticulture and water stress

Need for reliable monitoring of vine water status

Materials and Methods

Study site and vineyard design

Ground measurements of water status and gas exchange

Landsat 8 imagery and weather data

Machine learning model (Gradient Boosting Machine)

Validation strategy: block-out and date-out cross-validation

Results

Spatial prediction performance (mapping)

Temporal forecasting performance (date-out)

Effect of adding ground data at a single location

Discussion and Implications

Precision viticulture monitoring and forecasting at field scale

Importance of validation for using ML models on agricultural data

Agricultural Water Management 306 (2024) 109163 Contents lists available at ScienceDirect Agricultural Water Management journal [homepage:](homepage: www.elsevier.com/locate/agwat)[ www.elsevier.com/locate/agwat](homepage: www.elsevier.com/locate/agwat) | Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation
Eve Laroche-Pinela, Vincenzo Cianciolaa,b, Khushwinder Singha, Gaetano A. Vivaldi b, Luca Brillante a,*
a Department of Viticulture & Enology, California State University Fresno, Fresno, CA, USA b Department of Soil, Plant and Food Sciences – University of Bari “Aldo Moro”, Italy | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Handling Editor - J.E. Fern´andez | | Grapevine production worldwide is adversely impacted by climate change, including limited water availability, low-quality or sudden excess of water, and more frequent, severe, and prolonged heatwaves. As a result, grapevine growers require reliable spatial and temporal information on vine water status to adapt practices. This research evaluates the use of Landsat 8 satellite imagery in conjunction with weather data, and a machine learning algorithm (Gradient Boosting Machine) to predict vine water status in large vineyard blocks. The accuracy of predictions was assessed across both space (mapping) and time (forecast) using block-out and date-out cross-validation techniques. The study was conducted over two consecutive growing seasons on a Vitis vinifera, L.
cv. Merlot vineyard in Central California. The ground data included measurements of midday stem water potentials, Ψ stem and leaf gas exchange (net assimilation, AN and stomatal conductance, gs). Data acquisition was performed in twenty-four experimental units on the same day of the satellite overpasses. The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. The temporal forecast, assed via date-out cross-validation, proves to be more challenging, although the addition of ground data at one single spatial location improves the date-out performances and allows the NRMSE to reach 6.8 % for Ψ stem with R2 of 0.90, 53.4 % for gs with R2 of 0.74, and 25.5 % for AN with R2 of 0.78. The findings from this study have important implications for precision viticulture. They provide an assessment of Landsat 8 imagery, coupled with machine learning, as a means for growers to monitor and forecast vine water status at the field scale. The study highlights the importance of the validation method to ensure the proper use and assessment of machine learning models on agriculture data. | | Keywords:
Precision viticulture
Remote sensing
Modeling spatial-temporal patterns Precision agriculture
Digital agriculture | | | 1. Introduction The impacts of climate change, especially in production areas characterized by Mediterranean climates like California and Southern Europe, are causing severe damage to agricultural production, particularly viticulture (Santillan et al., 2020, van Leeuwen et al., 2024). Changes in temperature and precipitation patterns can alter the timing of bud break, flowering, veraison, and harvest, affecting grape ripening and wine quality (Mosedale et al., 2016). Warmer temperatures can lead to heat stress in grapevines, affecting photosynthesis, fruit development, and grape composition (van Leeuwen et al., 2019). This can result in reduced yields and changes in wine characteristics. Changes in precipitation patterns, including altered timing and intensity of rainfall, can lead to periods of drought and water stress in vineyards. Thi

FAQs

What inputs are used to predict grapevine water status?

The approach uses Landsat 8 satellite imagery combined with weather data, together with a machine learning model (Gradient Boosting Machine). Ground measurements include midday stem water potentials and gas exchange and assimilation-related variables.

How is spatial performance evaluated versus temporal performance?

Spatial performance is assessed with block-out cross-validation (mapping across space), while temporal forecasting is assessed with date-out cross-validation (generalization across time).

What are the main findings about mapping accuracy and forecasting difficulty?

The model predicts vine water status spatially with low errors and high accuracy (R2>0.8 for all three measurements). Temporal forecasting is more challenging, but performance improves when ground data are added at a single spatial location.

Aug 5, 2026

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