satellite

Research topics

Understanding land-atmosphere interactions is crucial if we want to assess and model the effect of climate (change) on ecosystem dynamics, the hydrological cycle, sea level rise, etc. and quantify their feedbacks on climate. Satellite remote sensing plays an important role in understanding these land-atmosphere interactions. Firstly, because it allows to quantify spatio-temporal variations in land-surface processes (e.g. changes in snow/ice properties, vegetation dynamics) and link them to climate (anomalies); especially at extensive scales or in locations where in-situ data is sparse. Secondly, because satellite remote sensing provides an essential tool to evaluate and improve land-atmosphere models, which often still have large uncertainties related to land-surface processes and land-atmosphere feedbacks.

EarthMapps focuses on the opportunities at the intersection of remote sensing and land-surface models. More specifically it concentrates on the use of multi-source remote sensing to improve our understanding of atmosphere-snow/ice and atmosphere-vegetation interactions in order to improve their representation in land-atmosphere models. This is particularly important as the uncertainties in these interactions have a large effect on our projections of future climate, hydrological cycle, sea level rise, vegetation dynamics. For example, the current understanding of the future state of the Greenland Ice Sheet (GrIS) and Antarctica and their contribution to sea level rise is still partly hampered by the understanding of the (sub-)surface processes and their representation in land-atmosphere models; or the projection of future vegetation dynamics and their climate feedbacks is still strongly determined by the limited understanding of vegetation response to climate anomalies.

tune

Research tools

Technologically we work on the interface between multi-source satellite imagery, radiative transfer models, land-surface models (e.g. snowmodels) and climate models. Within this framework we aim at developing and integrating innovative methodologies to assess the Earth's surface properties, mainly snow/ice and vegetation related, and understand their complex spatio-temporal response to climate. These methodologies range from improved data processing and data assimilation/merging, to big data solutions and time series analysis (e.g. tipping points). A lot of this work is done in close collaboration with our colleagues of the Department of Geoscience & Remote Sensing .

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 (e.g. MODIS, Sentinel-2, Landsat, Proba-V, etc.), SAR backscatter data (e.g., Sentinel-1), microwave radiometer data (e.g. AMSR-E), scatterometer backscatter data (e.g. Quikscat, Ascat) atmospheric remote sensing data (e.g. Cloudsat, Calipso, Ceres) etc.

The remote sensing data are used in combination with a range of models. These range from radiative transfer models (e.g., Tartes or SNICAR for snow albedo) in order to translate surface/atmosphere process to satellite observations, to snow models that model snow, firn, ice processes (e.g., Snowpack, Snowmodel,...). Alternatively, we combine the remote sensing data with climate model output provided by international collaborators (e.g., regional climate models RACMO or COSMO, or earth system models as CESM ) to help to evaluate/improve the representation of land surface processes.