Next Article in Journal
Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery
Previous Article in Journal
Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 586; doi:10.3390/rs8070586

Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach

1
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), UMR 8212, CNRS-CEA-UVSQ, Orme des Merisiers, 91191 Gif-sur-Yvette, France
2
Centre National de Recherches Météorologiques (CNRM-GAME), UMR 3589, Météo-France-CNRS, 42 Avenue G. Coriolis, 31057 Toulouse, France
3
Digital Research Center of Sfax, Advanced Technologies for Medecine and Signals, P.O. Box 275, Sakiet Ezzit, 3021 Sfax, Tunisia
4
Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), UMR1114, INRA-UAPV, Domaine St Paul, 84914 Avignon Cedex 9, France
5
Instituto Português do Mar e da Atmosfera (IPMA), Rua C ao Aeroporto, 1749-077 Lisboa, Portugal
Current address: Laboratoire de Météorologie Dynamique (LMD), UMR8539, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau, France
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 18 February 2016 / Revised: 10 June 2016 / Accepted: 5 July 2016 / Published: 11 July 2016
View Full-Text   |   Download PDF [3361 KB, uploaded 11 July 2016]   |  

Abstract

A wide range of environmental applications require the monitoring of land surface temperature (LST) at frequent intervals and fine spatial resolutions, but these conditions are not offered nowadays by the available space sensors. To overcome these shortcomings, LST downscaling methods have been developed to derive higher resolution LST from the available satellite data. This research concerns the application of a data assimilation (DA) downscaling approach, the genetic particle smoother (GPS), to disaggregate Meteosat 8 LST time series (3 km × 5 km) at finer spatial resolutions. The methodology was applied over the Crau-Camargue region in Southeastern France for seven months in 2009. The evaluation of the downscaled LSTs has been performed at a moderate resolution using a set of coincident clear-sky MODIS LST images from Aqua and Terra platforms (1 km × 1 km) and at a higher resolution using Landsat 7 data (60 m × 60 m). The performance of the downscaling has been assessed in terms of reduction of the biases and the root mean square errors (RMSE) compared to prior model-simulated LSTs. The results showed that GPS allows downscaling the Meteosat LST product from 3 × 5 km2 to 1 × 1 km2 scales with a RMSE less than 2.7 K. Finer scale downscaling at Landsat 7 resolution showed larger errors (RMSE around 5 K) explained by land cover errors and inter-calibration issues between sensors. Further methodology improvements are finally suggested. View Full-Text
Keywords: land surface temperature; downscaling; data assimilation; genetic particle smoother; Meteosat; MODIS; Landsat land surface temperature; downscaling; data assimilation; genetic particle smoother; Meteosat; MODIS; Landsat
Figures

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mechri, R.; Ottlé, C.; Pannekoucke, O.; Kallel, A.; Maignan, F.; Courault, D.; Trigo, I.F. Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach. Remote Sens. 2016, 8, 586.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top