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.