terclim by ICS banner
IVES 9 IVES Conference Series 9 GiESCO 9 GiESCO 2023 9 Monitoring grapevine water status using Landsat 8 images: a two-year case study in a Merlot vineyard

Monitoring grapevine water status using Landsat 8 images: a two-year case study in a Merlot vineyard


Context and purpose of the study – Viticulture needs for spatial and temporal information are increasing to improve vineyard management, especially concerning water efficiency. Remote sensing, particularly from satellites, can be a powerful tool to assess vineyard characteristics such as vigor or water status in space-time. In this study, we use Landsat 8, an American Earth observation satellite with six bands from the visible (VIS) to the Short-Wave Infrared (SWIR) domains with 30m spatial resolution and two thermal bands with 100m spatial resolution.

Material and methods – In 2020 and 2021, we conducted an experiment in a Merlot vineyard in Bakersfield, CA with 24 experimental units spatially distributed according to the pixel location of the Landsat 8 image. Contemporary to the satellite overpass on the vineyard, we measured water status every two weeks from June to August with midday stem water potentials (Ψstem), stomatal conductance (gs), and net carbon assimilation rate (AN). Berry weight, pH, total soluble solids (°Brix), and titratable acidity (TA) were also measured every two weeks from July to August. We converted the satellite images to reflectance values, extracted data for each experimental unit, and computed several vegetation indices (VI). Minimum relative humidity and maximum temperature were obtained from a neighboring weather station within the California Weather Irrigation System. We used linear regression and a gradient-boosting machine learning model with block-out and date-out cross-validation to predict water status from different combinations with band reflectance values, vegetation indices (VI), and minimum relative humidity and maximum temperature. We evaluated the results using the correlation coefficient (r), the determination coefficient (R2), and the root mean square error (RMSE). We interpreted the model by calculating feature importance for each predictor to understand the role of each Landsat 8 band in the model.

Results – The Ψstem data correlated with two VI: the Moisture Index, MSI, and the Normalized Difference Moisture Index, NDMI, (r = -0.64 and r = 0.63). The machine learning model predicted water status (Ψstem, AN, and gs) with an R2 of ~0.8 using only the Landsat 8 bands. The R2 was higher than 0.8 when remote sensing data was combined with ground weather data as evaluated by block-out cross-validation, thus showing the ability of the model to predict in space. Results were less encouraging when evaluated through date-out validation, therefore assessing the ability of the model to predict in time. The feature importance extraction identified the near-infrared (NIR) and SWIR domains as the most important bands to predict vine water status. These results confirm the interest in using satellite imagery to monitor vineyards at a large scale and with a good temporal resolution.


Publication date: June 22, 2023

Issue: GiESCO 2023

Type: Article



1Department of Viticulture & Enology, California State University Fresno, Fresno, CA, USA

Contact the author*


precision viticulture, grape water status, satellite images, Landsat 8, machine-learning



Related articles…

Physiological and growth reaction of Shiraz/101-14 Mgt to row orientation and soil water status

Advanced knowledge on grapevine row orientation is required to improve establishment, management and outcomes of vineyards on terroirs with different environmental conditions (climate, soil, topography) and in view of a future change to more extreme climatic conditions. The purpose of this study was to determine the combined effect of row orientation, plant water status and ripeness level on the physiological and viticultural reaction of Shiraz/101-14 Mgt.

Effects of mechanical leafing and deficit irrigation on Cabernet Sauvignon grown in warm climate of California

San Joaquin Valley accounts for 40% of wine grape acreage and produces 70% of wine grape in California. Fruit quality is one of most important factors which impact the economical sustainability of farming wine grapes in this region. Due to the recent drought and expected labor cost increase, the wine industry is thrilled to understand how to improve fruit quality while maintaining the yield with less water and labor input. The present study aims to study the interactive effects of mechanical leafing and deficit irrigation on yield and berry compositions of Cabernet Sauvignon grown in warm climate of California.

The effects of cane girdling on berry texture properties and the concentration of some aroma compounds in three table grape cultivars

The marketability of the table grapes is highly influenced by the consumer demand; therefore the market value of the table grapes is mainly characterized by its berry size, colour, taste and texture. Girdling could cause accumulation of several components in plants above the ringing of the phloem including clusters and resulting improved maturity. The aim of the experiments was to examine the effect of girdling on berry texture characteristics and aroma concentration.

Application of a fluorescence-based method to evaluate the ripening process and quality of Pinot Blanc grape

The chemical composition of grape berries at harvest is one of the most important factors that should be considered to produce high quality wines. Among the different chemical classes which characterize the grape juice, the polyphenolic compound, such as flavonoids, contribute to the final taste and color of wines. Recently, an innovative non-destructive method, based on chlorophyll fluorescence, was developed to estimate the phenolic maturity of red grape varieties through the evaluation of anthocyanins accumulated in the berry skin. To date, only few data are available about the application of this method on white grape varieties.

Different yield regulation strategies in semi-minimal-pruned hedge (SMPH) and impact on bunch architecture

Yields in the novel viticulture training system Semi-Minimal-Pruned Hedge (SMPH) are generally higher compared to the traditional Vertical Shoot Positioning (VSP). Excessive yields have a negative impact on the vine and wine quality, which can result in substantial losses in yield in subsequent vintages (alternate bearing) or penalties in fruit quality. Therefore yield regulation is essential. The bunch architecture in SMPH differs from VSP. Generally there is a higher amount but smaller bunches with lower single berry weights in SMPH compared to VSP.