terclim by ICS banner
IVES 9 IVES Conference Series 9 Downscaling of remote sensing time series: thermal zone classification approach in Gironde region

Downscaling of remote sensing time series: thermal zone classification approach in Gironde region

Abstract

In viticulture, the challenges of local climate modeling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation, and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Identification and monitoring of spatial thermal structures by unsupervised clustering method from land surface temperatures modelled at 500m using the topographical factors
Evaluation of the land surface temperature clustering method by statistical analysis based on topographical factors.

The first results have demonstrated the potential of the clustering method to identify thermal variations on a regional scale during the vegetative season between 2012 and 2018 without the need for ground climate data.

DOI:

Publication date: May 5, 2022

Issue: Terclim 2022

Type: Poster

Authors

Gwenaël Morin1, Pierre-Gilles Lemasle2, Renan Le Roux and Hervé Quénol3 

1LETG-Rennes, UMR 6554 CNRS – Université Rennes 2, Rennes, France
2US 116 Agroclim, INRAE, Avignon, France

Contact the author

Keywords

climate modelling, thermal satellite, land surface temperature, regional scale, topographical variables

Tags

IVES Conference Series | Terclim 2022

Citation

Related articles…

Soil survey and chemical parameters evaluation in viticultural zoning

The most recent methodological developments in soil survey and land evaluation, that can be taken as reference in the viticultural field, go over usage of the GIS and database. These informatic tools, which begin to be widely utilised, consent to realise evaluations at different geographic scale and with different data quality and quantity in entrance.

Grapevine yield estimation in a context of climate change: the GraY model

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Exploring microbial interactions between Saccharomyces cerevisiae and non-Saccharomyces yeast starters in vinification

Winemaking is a complex microbial process involving the co-existence and interactions of various microorganisms [1].

Unveiling the secrets of catechin: insights from NMR spectroscopy

Catechins, a class of flavonoids found in foods and beverages such as wine and tea, exhibit potent antioxidant properties that contribute to various health benefits.[1]

Smartphone as a tool for deficit irrigation management in Vitis vinifera  

Vine water status is one of the most influential factors in grape vigor, yield, and quality (Ojeda et al., 2002; Guilpart et al., 2014). Severe water deficits during the first stage of crop development (bud break to fruit set) impact yield in the current year and the following year. While during grape ripening, water availability impacts berry size, grape composition, and health status. Therefore, a correct assessment of plant water status allows for proper water management with an impact on grape yield and composition (McClymont et al, 2012; Pereyra et al., 2022).