GIS and Remote Sensing Unit at FEM-CRI

MODIS Land Surface Temperatures (LST)

From Land Surface Temperature (LST) maps, derivable indicators include temperature minima and maxima at annual, monthly, and decadal periods, the identification of late frost periods, unusual hot summers, growing degree days, spring temperature increase and autumnal temperature decrease (Neteler, 2005; Rizzoli et al., 2007; Carpi et al., 2008).
While the raw data are of limited interest to landscape epidemiological applications, time series aggregation of the new sensor data leads to a new quality of ecological indicators which have not been available earlier. A special challenge is the complex terrain as it dominates the Southern Alps in Italy. It requires special attention to data processing and outlier detection.

MODIS LST (7 Apr 2006, 13:30) satellite with QA map and outlier detection applied:
cirrus cloud fields remain undetected

MODIS LST (7 Apr 2006, 13:30) satellite with second outlier detection applied:
cirrus cloud fields removed

MODIS LST (7 Apr 2006, 13:30) satellite with reinterpolated with
elevation as additional variable and exposition correction (preliminary results)

Temperature time series from MODIS LST

We use above method to produce reconstructed time series from MODIS LST. Here some preliminary results:

Raw (blue) and reconstructed (red) MODIS LST time at Arco (TN), Italy (click to enlarge)

To better understand the quality of the reconstruction, see below a close-by meteorological station. The reconstruction above is completely independent from the meteorological data and only based on remote sensing data.

Tmin at 2m from meteorological station at Arco (TN), Italy (click to enlarge)

The developed method will be published in detail in 2010.

Related publications

• Neteler, M., 2010: Estimating daily Land Surface Temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sensing 2(1), 333-351. (DOI) [ Abstract | PDF ]

• Neteler, M., 2010: Spatio-temporal reconstruction of satellite-based temperature maps and their application to the prediction of tick and mosquito disease vector distribution in Northern Italy. EDEN PhD summary. Catalogued by the EDEN EU/FP6 Steering Committee as EDEN0176

• Carpi G., Cagnacci F., Neteler M., Rizzoli A, 2008: Tick infestation on roe deer in relation to geographic and remotely-sensed climatic variables in a tick-borne encephalitis endemic area. Epidemiology and Infection,136, pp. 1416-1424. (DOI) (ISI 2008: 2.36) [ PubMed ]

• A. Rizzoli, M. Neteler, R. Rosà, W. Versini, A. Cristofolini, M. Bregoli, A. Buckley, and E.A. Gould, 2007: Early detection of TBEv spatial distribution and activity in the Province of Trento assessed using serological and remotely-sensed climatic data. Geospatial Health, 1(2), pp. 169-176. [ PubMed | PDF ]

• M. Neteler, 2005: Time series processing of MODIS satellite data for landscape epidemiological applications. International Journal of Geoinformatics, 1(1), pp. 133-138 (PDF)