Next Article in Journal
Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data
Previous Article in Journal
A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery
Open AccessArticle

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

1
LETG-Rennes, UMR 6554 CNRS—Université Rennes 2, Department of Geography, Place du Recteur Henri Le Moal, 35000 Rennes, France
2
CIRAD, Forêts et Sociétés, F-34398 Montpellier, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(1), 4; https://doi.org/10.3390/rs13010004
Received: 18 November 2020 / Revised: 13 December 2020 / Accepted: 15 December 2020 / Published: 22 December 2020
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. View Full-Text
Keywords: bioclimatic indices; land surface temperature; topographic predictors bioclimatic indices; land surface temperature; topographic predictors
Show Figures

Graphical abstract

MDPI and ACS Style

Morin, G.; LE ROUX, R.; Lemasle, P.-G.; Quénol, H. Mapping Bioclimatic Indices by Downscaling MODIS Land Surface Temperature: Case Study of the Saint-Emilion Area. Remote Sens. 2021, 13, 4. https://doi.org/10.3390/rs13010004

AMA Style

Morin G, LE ROUX R, Lemasle P-G, Quénol H. Mapping Bioclimatic Indices by Downscaling MODIS Land Surface Temperature: Case Study of the Saint-Emilion Area. Remote Sensing. 2021; 13(1):4. https://doi.org/10.3390/rs13010004

Chicago/Turabian Style

Morin, Gwenaël; LE ROUX, Renan; Lemasle, Pierre-Gilles; Quénol, Hervé. 2021. "Mapping Bioclimatic Indices by Downscaling MODIS Land Surface Temperature: Case Study of the Saint-Emilion Area" Remote Sens. 13, no. 1: 4. https://doi.org/10.3390/rs13010004

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop