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Remote Sens. 2010, 2(1), 333-351; doi:10.3390/rs1020333
Article

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

IASMA Research and Innovation Centre, Environment and Natural Resources Area, Fondazione Edmund Mach, 38010 S. Michele all'Adige, TN, Italy
Received: 1 December 2009 / Revised: 8 January 2010 / Accepted: 11 January 2010 / Published: 18 January 2010
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Abstract

Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000–2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained.
Keywords: complex terrain; map reconstruction; MODIS LST; time series; GIS; growing degree days; threshold maps; mountainous environments; meteorological station data complex terrain; map reconstruction; MODIS LST; time series; GIS; growing degree days; threshold maps; mountainous environments; meteorological station data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Neteler, M. Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data. Remote Sens. 2010, 2, 333-351.

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