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.

Download the PDF

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…

La viticoltura veneta in un contesto di città e industria diffusa: per una lettura integrale del paesaggio della collina pedemontana veronese orientale

l Veneto, come è noto, rappresenta una delle estensioni di superfici a vigneto più importanti in Italia e nell’Europa stessa. Il paesaggio viticolo fino ad oggi è stato ampiamente letto nelle sue componenti

Read More

Carbon footprint as a function of inter-annual climate variability in Uruguayan viticulture production systems

Climate change, driven by greenhouse gas (GHG) emissions, is one of humanity’s most significant environmental challenges.

Read More

The influence of pre-heatwave leaf removal on leaf physiology and berry development

Due to climate change, the occurrence of heatwaves and drought events is increasing, with significant impact on viticulture. Common ways to adapt viticulture to a changing climate include site selection, genotype selection, irrigation management and canopy management. The latter mentioned being for instance source-sink manipulations, such as leaf removal, with the aim to delay ripening.

Read More

Vintel: a single decision support system for irrigation, fertilization and disease management of grapevine

Vine growers face an increasing number of decisions, both tactical and strategic, in a context where available data and constraints are on the rise, such as resources, societal, environmental, climatic, and economic factors. This has led to a growing supply of decision support systems (DSS) and softwares to manage vineyards. Facing this new complexity, growers must now consider several options: giving up the use of DSS, using systems that are compatible with each other but may limit their options, or using a single system that may be too complex to use effectively. In this context, itk has expanded its Vintel® tool, which was originally designed for grapevine water status management (irrigation, inter-row, cover-crop, etc.), to include fertilization and disease management.

Read More

VINIoT: Precision viticulture service for SMEs based on IoT sensors network

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

Read More