Open Access
Numéro
Climatologie
Volume 15, 2018
Page(s) 62 - 83
DOI https://doi.org/10.4267/climatologie.1345
Publié en ligne 3 octobre 2019
  • Azevedo J., Chapman L. & Muller C., 2016. Quantifying the daytime and night-time urban heat island in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sens., 8(2), 153. https://doi.org/10.3390/rs8020153. [CrossRef] [Google Scholar]
  • Benali A., Carvalho A.C., Nunes J.P., Carvalhais N. & Santos A.. 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ., 124, 108–121. https://doi.org/10.1016/j.rse.2012.04.024. [CrossRef] [Google Scholar]
  • Bonnardot V., Carey V.A., Madelin M., Cautenet S., Coetzee Z., Quénol H., 2012. Spatial variability of night temperatures at a fine scale over the Stellenbosch wine district, South Africa. J. Int. Sci. Vigne Vin, 46, 1–13. https://doi.org/10.20870/oeno-one.2012.46.1.1504. [Google Scholar]
  • Bonnefoy C., Quénol H., Planchon O., Barbeau G., 2010. Températures et indices bioclimatiques dans le vignoble du Val de Loire dans un contexte de changement climatique. EchoGéo, 14. https://doi.org/10.4000/echogeo.12146. [Google Scholar]
  • Bosilovich M.G., 2006. A comparison of MODIS land surface temperature with in situ observations. Geophys. Res. Lett., 33, https://doi.org/10.1029/2006GL027519. [CrossRef] [Google Scholar]
  • Essa W., Verbeiren B., van der Kwast J., Batelaan O., 2017. Improved DisTrad for downscaling thermal MODIS imagery over urban areas. Remote Sens. 9, 1243. https://doi.org/10.3390/rs9121243. [CrossRef] [Google Scholar]
  • Ghafarian Malamiri H., Rousta I., Olafsson H., Zare H. & Zhang H., 2018. Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA). Atmosphere, 9(9), 334. https://doi.org/10.3390/atmos9090334. [CrossRef] [Google Scholar]
  • Huang R., Zhang C., Huang J., Zhu D., Wang L. & Liu J., 2015. Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data. Remote Sensing 7, 8728–8756. https://doi.org/10.3390/rs70708728 [CrossRef] [Google Scholar]
  • Huglin P., 1978. Nouveau mode d’évaluation des possibilités héliothermiques d’un milieu viticole. Comptes rendus des séances de l’Académie d’agriculture de France, 64(1117–1), 126. [Google Scholar]
  • Hutengs C. & Vohland M., 2016. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sens. Environ., 178, 127–141. https://doi.org/10.1016/j.rse.2016.03.006. [CrossRef] [Google Scholar]
  • Joly D., Nilsen L., Fury R., Elvebakk A., Brossard T., 2003. Temperature interpolation at a large scale: test on a small area in Svalbard. Int. J. Climatol., 23, 1637–1654. [CrossRef] [Google Scholar]
  • Jones G.V., 2006. Climate and terroir: impacts of climate variability and change on wine. Fine Wine Terroir - Geosci. Perspect., 1–14. [Google Scholar]
  • Kang J., Tan J., Jin R., Li X., Zhang Y., Kang J., Tan J., Jin R., Li X., Zhang Y., 2018. Reconstruction of MODIS land surface temperature products based on multi-temporal information. Remote Sens., 10, 1112. https://doi.org/10.3390/rs10071112. [CrossRef] [Google Scholar]
  • Kloog I., Nordio F., Coull B.A., Schwartz J., 2014. Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens. Environ., 150, 132–139. https://doi.org/10.1016/j.rse.2014.04.024. [CrossRef] [Google Scholar]
  • Le Roux R., de Rességuier L., Corpetti T., Jégou N., Madelin M., van Leeuwen C., Quénol H., 2017a. Comparison of two fine scale spatial models for mapping temperatures inside winegrowing areas. Agric. For. Meteorol., 247, 159–169. [CrossRef] [Google Scholar]
  • Le Roux R., De Rességuier L., Katurji M., Zawar-Reza P., Sturman A., Van Leeuwen C., Quénol H., 2017b. Analyse multiscalaire de la variabilité spatiale et temporelle des températures à l’échelle des appellations viticoles de Saint-Émilion, Pomerol et leurs satellites. Climatologie. https://doi.org/10.4267/climatologie.1243. [Google Scholar]
  • Le Roux R., Katurji M., De Rességuier L., Sturman A., Van Leeuwen C., Parker A., Trought M., Quénol H., 2016. A fine scale approach to map bioclimatic indices using and comparing dynamical and geostatistical methods. Terroir Congress, Oregon, 6p. [Google Scholar]
  • Madelin M.. 2004. L’aléa gélif printanier dans le vignoble marnais en Champagne : modélisations spatiales aux échelles fines des températures minimales et des écoulements de l’air. Thèse de doctorat, Paris 7, 327p. [Google Scholar]
  • Marchand N., 2017. Suivi de la température de surface du sol en zones de pergélisol Arctique par l’utilisation de données de télédétection satellite assimilées dans le schéma de surface du modèle climatique canadien (CLASS). Thèse de doctorat, Université Grenoble Alpes, 191p. [Google Scholar]
  • McMillin L.M., 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. J. Geophys. Res., 80(36), 5113–5117. [CrossRef] [Google Scholar]
  • Metz M., Andreo V., Neteler M., 2017. A new fully gap-free time series of land surface temperature from MODIS LST data. Remote Sens., 9, 1333. https://doi.org/10.3390/rs9121333. [CrossRef] [Google Scholar]
  • Meyer H., Katurji M., Appelhans T., Müller M., Nauss T., Roudier P., Zawar-Reza P., 2016. Mapping daily air temperature for Antarctica based on MODIS LST. Remote Sens., 8, 732. https://doi.org/10.3390/rs8090732. [CrossRef] [Google Scholar]
  • Mostovoy G.V., King R.L., Reddy K.R., Kakani V.G., Filippova M.G., 2006. Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the State of Mississippi. GIScience Remote Sens., 43, 78–110. https://doi.org/10.2747/1548-1603.43.1.78. [CrossRef] [Google Scholar]
  • Mutiibwa D., Strachan S., Albright T., 2015. Land surface temperature and surface air temperature in complex terrain. IEEE Journals & Magazine [WWW Document]. URL https://ieeexplore.ieee.org/abstract/document/7243313/ [Google Scholar]
  • Noi P. & Kappas M., Degener J., 2016. Estimating daily maximum and minimum land air surface temperature using MODIS land surface temperature data and ground truth data in Northern Vietnam. Remote Sens., 8(12), 1002. https://doi.org/10.3390/rs8121002. [CrossRef] [Google Scholar]
  • Neethling E.. 2016. Adaptation de la viticulture au changement climatique: vers des stratégies à haute résolution. Doctoral dissertation, Université Rennes 2. [Google Scholar]
  • Ollat N. & Touzard J.M., 2014. Adaptation à long terme au changement climatique pour la viticulture et l’œnologie : un programme de recherche sur les vignobles français. Revue des œnologues et des techniques vitivinicoles et œnologiques, 41(152), 11–12. [Google Scholar]
  • Parker A., Cortázar-Atauri I.G.de., Chuine I., Barbeau G., Bois B., Boursiquot J.-M., Cahurel J.-Y., Claverie M., Dufourcq T., Gény L., Guimberteau G., Hofmann R.W., Jacquet O., Lacombe T., Monamy C., Ojeda H., Panigai L., Payan J.-C., Lovelle B.R., Rouchaud E., Schneider C., Spring J.-L., Storchi P., Tomasi D., Trambouze W., Trought M., van Leeuwen C., 2013. Classification of varieties for their timing of flowering and veraison using a modelling approach: A case study for the grapevine species Vitis vinifera L. Agric. For. Meteorol., 180, 249–264. https://doi.org/10.1016/j.agrformet.2013.06.005. [CrossRef] [Google Scholar]
  • Parker A., Cortazar-Atauri I.G. de, Van Leeuwen C., Chuine I., 2011. General phenological model to characterise the timing of flowering and veraison of Vitis vinifera L. Aust. J. Grape Wine Res., 17, 206–216. [CrossRef] [Google Scholar]
  • Quénol H., 2014. Changement climatique et terroirs viticoles. Lavoisier Tec&doc, 444p. [Google Scholar]
  • Quénol H. & Bonnardot V., 2014. A multi-scale climatic analysis of viticultural terroirs in the context of climate change : the “TERADCLIM” project. Int. J. Vine Wine Sci., 23–32. [Google Scholar]
  • Quénol H. & Ollat N., 2015. Changement climatique : de modèles prédictifs mondiaux vers des méthodes d’adaptation à l’échelle de l’exploitation viticole. Rev. Oenologues Tech. Vitivinic. Oenologiques, 42, 7–8. [Google Scholar]
  • Quénol H., 2017. Viticulture – experimentation or adaptation? In ‘Adaptating to Climate Change’, Thiebault S., Laville B. and Euzen A., ediSens, 333–340. [Google Scholar]
  • Shi L., Liu P., Kloog I., Lee M., Kosheleva A. & Schwartz J., 2016. Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: A statistical modeling study. Environmental research, 146, 51–58. https://doi.org/10.1016/j.envres.2015.12.006. [CrossRef] [Google Scholar]
  • Sismanidis P., Keramitsoglou I., Bechtel B. & Kiranoudis C., 2016. Improving the downscaling of diurnal land surface temperatures using the annual cycle parameters as disaggregation kernels. Remote Sens., 9(1), 23. https://doi.org/10.3390/rs9010023. [CrossRef] [Google Scholar]
  • Sohrabinia M., Zawar-Reza P., Rack W., 2015. Spatio-temporal analysis of the relationship between LST from MODIS and air temperature in New Zealand. Theor. Appl. Climatol., 119, 567–583. https://doi.org/10.1007/s00704-014-1106-2. [CrossRef] [Google Scholar]
  • Southey T.O., 2017. Integrating climate and satellite remote sensing to assess the reaction of Vitisvinifera L. cv. Cabernet Sauvignon to a changing environment. Thesis, Stellenbosch University, 293p. [Google Scholar]
  • Sturman A., Zawar-Reza P., Soltanzadeh I., Katurji M., Bonnardot V., Parker A.K., Trought M.C., Quénol H., Le Roux R. and Gendig E., 2017. The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions. OENO One, 51(2), 99–105. [CrossRef] [Google Scholar]
  • Van Leeuwen C. & Seguin G., 2006. The concept of terroir in viticulture. Journal of wine research, 17(1), 1–10. [CrossRef] [Google Scholar]
  • Vancutsem C., Ceccato P., Dinku T., Connor S.J., 2010. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ., 114, 449–465. https://doi.org/10.1016/j.rse.2009.10.002. [CrossRef] [Google Scholar]
  • Wan Z., 2008. New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens. Environ., 112(1), 59–74. https://doi.org/10.1016/j.rse.2006.06.026. [CrossRef] [Google Scholar]
  • Wan Z., 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ., 140, 36–45. https://doi.org/10.1016/j.rse.2013.08.027. [CrossRef] [Google Scholar]
  • Wan Z., Hook S., Hulley G., 2015. MOD11A1 MODIS and MYD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006. Distributed by NASA EOSDIS Land Processes DAAC. https://lpdaac.usgs.gov/ [Google Scholar]
  • Wan Z. & Dozier J., 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens., 34, 892–905. https://doi.org/10.1109/36.508406. [CrossRef] [Google Scholar]
  • Wan Z., Zhang Y., Zhang Q., Li Z.-L., 2004. Quality assessment and validation of the MODIS global land surface temperature. Int. J. Remote Sens., 25, 261–274. [CrossRef] [Google Scholar]
  • Williamson S.N., Hik D.S., Gamon J.A., Kavanaugh J.L., Flowers G.E., 2014. Estimating temperature fields from MODIS land surface temperature and air temperature observations in a sub-arctic alpine environment. Remote Sens., 6, 946–963. https://doi.org/10.3390/rs6020946. [CrossRef] [Google Scholar]
  • Winkler A.J., Cook J., Kliewer W., Lider L., 1974. General Viticulture. University of California Press, Berkeley. 710p. [Google Scholar]
  • Xia H., Chen Y., Zhao Y., Chen Z., Xia H., Chen Y., Zhao Y., Chen Z., 2018. Regression-then-fusion or fusion-then-regression? A theoretical analysis for generating high spatiotemporal resolution Land Surface Temperatures. Remote Sens., 10, 1382. https://doi.org/10.3390/rs10091382. [CrossRef] [Google Scholar]
  • Zhao W., Duan S.B., Li A. & Yin G., 2019. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221, 635–649. https://doi.org/10.1016/j.rse.2018.12.008. [CrossRef] [Google Scholar]
  • Zeng L., Wardlow B., Tadesse T., Shan J., Hayes M., Li D. & Xiang D., 2015. Estimation of daily air temperature based on MODIS land surface temperature products over the corn belt in the US. Remote Sens., 7(1), 951–970. https://doi.org/10.3390/rs70100951. [CrossRef] [Google Scholar]
  • Zhang L., Huang J., Guo R., Li X., Sun W., Wang X., 2013. Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data. J. Zhejiang Univ. Sci. B, 14, 144–161. https://doi.org/10.1631/jzus.B1200169. [CrossRef] [Google Scholar]
  • Zhu W., Lű A., Jia S., 2013. Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sens. Environ., 130, 62–73. https://doi.org/10.1016/j.rse.2012.10.034. [CrossRef] [Google Scholar]

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