Volume I-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 315-321, 2012
https://doi.org/10.5194/isprsannals-I-7-315-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 315-321, 2012
https://doi.org/10.5194/isprsannals-I-7-315-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jul 2012

23 Jul 2012

FUSION OF ACTIVE AND PASSIVE MICROWAVE OBSERVATIONS TO CREATE AN ESSENTIAL CLIMATE VARIABLE DATA RECORD ON SOIL MOISTURE

W. Wagner1, W. Dorigo1, R. de Jeu2, D. Fernandez3, J. Benveniste3, E. Haas4, and M. Ertl5 W. Wagner et al.
  • 1Vienna University of Technology, Institute of Photogrammetry and Remote Sensing, Gusshausstrasse 27-29, 1040 Wien, Austria
  • 2Vrije Universiteit Amsterdam, Department of Hydrology and Geo-environmental Sciences, De Boelelaan 1085 1081 HV Amsterdam, The Netherlands
  • 3European Space Agency, ESA-ESRIN, Via Galileo Galilei, 00044 Frascati, Italy
  • 4GeoVille Information Systems, Sparkassenplatz 2, 6020 Innsbruck, Austria
  • 5Angewandte Wissenschaft Software und Technologie, Mariahilfer Strasse 47/3/1, 1060 Vienna, Austria

Keywords: Climate, Hydrology, Fusion, Radar, Radiometry, Calibration, Change detection

Abstract. Soil moisture was recently included in the list of Essential Climate Variables (ECVs) that are deemed essential for IPCC (Intergovernmental Panel on Climate Change) and UNFCCC (United Nations Framework Convention on Climate Change) needs and considered feasible for global observation. ECVs data records should be as long, complete and consistent as possible, and in the case of soil moisture this means that the data record shall be based on multiple data sources, including but not limited to active (scatterometer) and passive (radiometer) microwave observations acquired preferably in the low-frequency microwave range. Among the list of sensors that can be used for this task are the C-band scatterometers on board of the ERS and METOP satellites and the multi-frequency radiometers SMMR, SSM/I, TMI, AMSR-E, and Windsat. Together, these sensors already cover a time period of more than 30 years and the question is how can observations acquired by these sensors be merged to create one consistent data record? This paper discusses on a high-level possible approaches for fusing the individual satellite data. It is argued that the best possible approach for the fusion of the different satellite data sets is to merge Level 2 soil moisture data derived from the individual satellite data records. This approach has already been demonstrated within the WACMOS project (http://wacmos.itc.nl/) funded by European Space Agency (ESA) and will be further improved within the Climate Change Initiative (CCI) programme of ESA (http://www.esa-cci.org/).