Terroir 2020 banner
IVES 9 IVES Conference Series 9 Methodology of climate modelling using land surface temperature downscaling: case study case of Gironde (France)

Methodology of climate modelling using land surface temperature downscaling: case study case of Gironde (France)

Abstract

Aim: Climate modelling in viticulture introduced new challenges such as high spatio-temporal monitoring and the use of dependable time series and robustness modelling methods. Land surface temperature (LST) is widely used and particularly MODIS thermal satellite images due to their high temporal resolution (four images per day). However, this data is not completely adapted to regional scale with its medium spatial resolution (1-km). Downscaling methods can improve spatial resolution using machine learning algorithms implementing multiple predictors as topographical variables and vegetation indices. In the last decades, classical bioclimatic temperature-based indices showed a specific spatial distribution depending on topographical variables and at once a significantly non-correlation with vegetation growing trend.  

Methods and Results: In the current study, an assessment of SVM Machine learning method was used to downscaling daily LST using topographical variables and vegetation indices as predictors at multiple spatial resolution. The aims of this study were to (1) evaluate daily LST time series through 2012-2018 period, (2) assess the impact of topographical variables and evolution of vegetation indices during vegetative season and (3) calculation of bioclimatic indices on the wine-growing area of the Gironde The dataset included: 1) daily time series of MODIS LST at 1-km (MOD11A1 and MYD11A1) and 2) topographical variables derived from Digital Elevation Model at 500 m (GMTED10). The first step was the pre-processing and reconstruction of time series. The second step was the downscaling of LST using SVM with topographical variables as predictors. For each day, a model was calibrated and validated to predict daily LST at finer spatial scale. The third step was the calculation of bioclimatic indices (Winkler and Huglin). The methodology was applied for the fourth LST MODIS products acquired at different times. For example, for the 2012 wine growing season Huglin index and Winkler index were calculated with the daily predicted LST (without vegetation indices as predictors but only topographical variables) on the Gironde area and have a globally similar spatial structure. The lowest values (≈ 1900°C for Huglin and 1340°C for Winkler) are concentrated on the coastline to the west and south of the Gironde. The highest index values (> 2000°C for Huglin and > 1700°C for Winkler) are located from the centre of the Gironde to the north-east. These warmer sectors are concentrated in the valley bottoms of the Dordogne and Gironde with higher values in the south of Libourne. LST predictions should be downscaled for the whole period (2012-2019) and the second experiment of the downscaling method includes vegetation indices as predictors.

Conclusion: 

The advantage of LST is their temporal and spatial covers in all the areas. However, data availability and bias must be taken into account and minimized. 

Significance and Impact of the Study:  At the scale of Gironde region, this downscaling method has been tested for the first time with MODIS Land Surface Temperature derived from thermal satellite images in a wine-growing context.

DOI:

Publication date: March 17, 2021

Issue: Terroir 2020

Type: Video

Authors

Gwenaël Morin1*, Renan Le Roux2, Pierre-Gilles Lemasle1 and Hervé Quénol1

1LETG-Rennes, UMR 6554 CNRS – Université Rennes 2, Place du Recteur Henri Le Moal, Rennes – France 
2CIRAD, Forêts et Sociétés, F-34398 Montpellier, France

Contact the author

Keywords

Climate modelling, topographical downscaling, thermal satellite imagery, bioclimatic indices, Gironde

Tags

IVES Conference Series | Terroir 2020

Citation

Related articles…

How to improve the success of dead vine replacement: insights into the impacts of young plant‘s environment 

Grapevine faces multiple biotic and/or abiotic stresses, which are interrelated. Depending on their incidence, they can have a negative impact on the development and production of the plant, but also on its longevity, leading to vine dieback. One of the consequences of vine dieback on production is the increased replacement rate of dead or missing vines within a parcel.

“Gentle” sustainable extraction from whole berry by using resonance waves and slight over CO2 overpressure

The traditional methods of grape extraction of enochemical compounds use very often mechanical energy by pistons such as the pigeage or mechanical energy produced by must (delestage, pumping over). Recent trend by winemaker is trying to introduce in the fermentation tank, whole berry grape to avoid even minimal oxidation. Unfortunately, the use of the traditional mechanical techniques aforementioned, very often do not guarantee the optimal extraction with residual sugars in the marc. Use of resonance waves (airmixingtm) and a slight overpressure by CO2 (adcftm) permit to work on whole berry guaranteeing the perfect extraction.

Characterization of bunch compactness and identification of associated genes in a diverse collection of cultivars of Vitis vinifera L.

Compactness is a complex trait of V. vinifera L. and is defined ultimately by the portion of free space within the bunch which is not occupied by the berries. A high degree of compactness results in poor ventilation and consequently a higher susceptibility to fungal diseases, diminishing the quality of the fruit. The easiness to conceptualize the trait and its importance arguably contrasts with the difficulty to measure and quantify it. However, recent technical advancements have allowed to study this attribute more accurately over the last decade. Our main objective was to explore the underlying genetics determining bunch compactness by applying updated phenotyping methods in a collection of V. vinifera L. cultivars with a wide genetic diversity.

Mapping and tracking canopy size with VitiCanopy

Understanding vineyard variability to target management strategies, apply inputs efficiently and deliver consistent grape quality to the winery is essential. However, despite inherent vineyard variability, the majority are managed as if they are uniform. VitiCanopy is a simple, grower-friendly tool for precision/digital viticulture that allows users to collect and interpret objective spatial information about vineyard performance. After four years of field and market research, an upgraded VitiCanopy has been created to achieve a more streamlined, technology-assisted vine monitoring tool that provides users with a set of superior new features, which could significantly improve the way users monitor their grapevines. These new features include:
• New user interface
• User authentication
• Batch analysis of multiple images
• Ease the learning curve through enhanced help features
• Reporting via the creation of colour maps that will allow users to assess the spatial differences in canopies within a vineyard.
Use-case examples are presented to demonstrate the quantification and mapping of vineyard variability through objective canopy measurements, ground-truthing of remotely sensed measurements, monitoring of crop conditions, implementation of disease and water management decisions as well as creating a history of each site to forecast quality. This intelligent tool allows users to manage grapevines and make informed management choices to achieve the desired production targets and remain profitable.

An intra-block study of bunch zone air temperature and its impact on berry and wine attributes

Temperature is a key environmental factor affecting grape primary and secondary metabolites. Even if several mesoscale studies have already been conducted on temperature
especially within a Protected Designation of Origin area, few data are available at an intra-block scale. The present study aimed at i) assessing the variability in bunch zone air temperature within a single vineyard block and the temporal stability of temperature spatial patterns, ii) understanding temperature drivers and
iii) identifying the impact of temperature on grape berry attributes.