Big data

Satellite remote sensing plays an important role in understanding the Earths’s surface-atmosphere processes and interactions; especially, since the unprecedented volumes of earth observation data gathered today allow understanding the land-atmosphere processes in much more detail.  This remote sensing data source in combination with in-situ and model data however is often highly dimensional, requiring innovative data mining and analysis tools including big data methods.

GRS focuses on the use and development of the mathematical and statistical methods to extract insights about the functioning of the Earth, from this emerging remote sensing data-cube. We combine multi-source satellite imagery with other data sources (e.g. model data, in-situ data) and aim at developing and integrating innovative methodologies to extract application oriented insights. These applications range from assessing the Earth’s surface/atmosphere properties for climate studies to understanding the complex response of the Earth’s surface to climate, natural hazards, anthropogenic change. We apply methodologies ranging from improved data processing and data assimilation/merging, to big data solutions and time series analysis (e.g. tipping points).  We exploit a broad collection of remote data sets that often bridge the gap between land remote sensing and atmospheric remote sensing. For example, in the past we have worked with multi-spectral optical satellite imagery, SAR, scatterometers, satellite based radar and lidar measurements etc. The remote sensing data are used in combination with a range of models including radiative transfer models in order to translate surface/atmosphere process to satellite observations and land models to help to evaluate/improve the representation of land surface processes in current climate models.

By working on (big) data solutions to bridge the gap between remote sensing technology and its (scientific) applications, GRS aims at offering new means to extract valuable information in a rigorous manner from multi-source remote sensing data.

/* */