Mapping Bioclimatic Indices by Downscaling MODIS Land Surface Temperature: Case Study of the Saint-Emilion Area

Gwenaël Morin,Renan Le Roux,Pierre-Gilles Lemasle,Hervé Quénol

doi:10.3390/rs13010004

Abstract

Thermal conditions, influenced by the local environment, impact the development of the vine and determine the composition of the grapes. Bioclimatic indices, based on cumulative air temperatures, are modelled and mapped using statistical methods integrating local factors. Air temperature data from sensors networks are limited in space and time. We evaluated the potential of land surface temperature (LST) to identify comparable spatial distribution, and not to replace air temperature, by using a support vector machine algorithm to compare bioclimatic indices calculated from air temperature or LST. This study focused on the 2012–2018 period in the Saint-Emilion winegrowing area of France. The use of several digital elevation models with high spatial resolution (i.e., GMTED10 (1000, 500 and 250 m) and SRTM (90 and 30 m)) enabled LST to be downscaled at each resolution. The same topographic variables (elevation, slope, orientation coordinates) were used as predictors, and identical algorithms and cross-validation parameters were implemented in both mapping methods. Bioclimatic indices were calculated from daily air temperature, daily LST or weekly LST. The results of the daily and weekly downscaling of the MODIS time series at several spatial resolutions are encouraging for application to viticulture and have allowed to identify an optimal resolution between 500 m and 250 m limiting bias.

Highlights

Grapevines are highly sensitive to environmental conditions, which influence yield, grape composition, wine quality and wine style [1]
We studied the daily relationship between air temperature and land surface temperature (LST) per year for the 90 sensor locations that recorded air temperature and the two types of MODIS data extracted for these same locations: daily LST (M*D11A1) and 8-day composite LST (M*D11A2)
Several studies have used linear regression to demonstrate the strong relationship between air temperatures and MODIS LST in different geographical contexts and time series lengths

Summary

Introduction

Grapevines are highly sensitive to environmental conditions, which influence yield, grape composition, wine quality and wine style [1]. Among the many climatic variables that influence grapevine physiology and phenology, temperature is often considered the most important [2,3,4]. Bioclimatic indices calculated from climate data are the most common way to identify impacts of climate on grapevine growth. They are used to classify and compare winegrowing regions based on cumulative air temperature (e.g., Winkler index (WI), Huglin index (HI), Jones index) [5,6,7] and can be combined with phenological stages [8]. Atmospheric parameters at the boundary layer depend on surface conditions (e.g., roughness, type), which can cause high spatial variability over relatively small areas (i.e., a few square meters to a few square kilometers)

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