Workshop Purpose

Severe and ongoing changes in the Earth’s environments have been witnessed over recent years, resulting from anthropogenic climate forcing. For decades now, remote sensing data are being collected from space observations, providing unique insights into the state of the Earth’s spheres. ECVs time series data form an indispensable basis for the quantification of climate change effects. Prominent examples are sea surface temperature and land surface temperature, the gauging of the Earth’s water budget, or the understanding of imbalances in Earth’s energy flux (amongst others). Their observation strongly relies on global satellite remote sensing techniques at different levels. Amongst others, central research questions to be addressed in the workshop will be

  • the quantification of systematic and stochastic uncertainties and errors in EO time series data, and the effect on climate change detection from essential ECVs
  • the effect, modelling and propagation of errors from low-level into higher-level remote sensing products have, and the related uncertainty validation
  • the exploration of state-of-the-art and new data fusion/assimilation tools (i.e., machine learning methods), to optimally combine different remote sensing datasets and/or physical Earth system models.

The workshop aims to facilitate the exchange of the current state of research in the particular fields, unveiling the state-of-the-art in ECVs uncertainty quantification, while encouraging participants to collaboratively explore innovative approaches, fostering a collective effort to advance their fields by further reducing and explaining uncertainties of ECVs and related EO-data.

Image Credit: Graph: Annual global average sea surface temperature (°C), relative to the average for the 1991–2020 reference period. Data sources: HadSST4.0.1.0 (1850–2022, black; grey shading indicates the uncertainty[1]), ERSSTv5 (1880–2022, orange), and ESA CCI/C3S SST Climate Data Record v3.0 (1991–2022, red). Credit: C3S/ECMWF/UK Met Office.