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

By Markus Neteler. Remote Sensing, 2010

358 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/52a17ab0d1ae5218651b59133a67f22c034b435f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Martin Landa
2. M. Hamish Bowman
3. Markus Metz
4. Corey J. A. Bradshaw

In Ecology

1. T. Wernberg
2. M. Thomsen
3. S. Connell
4. B. Russell
5. Markus Metz
6. Martin Landa
7. M. Hamish Bowman
8. C. Gurgel

In Environmental Engineering

1. Martin Landa
2. M. Hamish Bowman
3. Markus Metz
4. Corey J. A. Bradshaw
5. Markus Neteler

Citations in the corpus

• Distribution models predict large contractions of habitat‐forming seaweeds in response to ocean warming (2018, methodology): “Additionally, we also considered the Nearest Neighbour correction (Neteler, 2010), if software specific error issues were detected, that is, MODIS Reprojection Tool software (MRT) was used instead of GDAL.”
• GRASS GIS: A multi-purpose open source GIS (2012, methodology): “The usage of GRASS on a cluster has been demonstrated by (Neteler, 2010; Neteler, 2005) for MODIS satellite sensor time series processing.”