Discovery lecture

MULTIPLY: Combining Satellite Observations in order to investigate biodiversity patterns

In view of the launch of the MULTIPLY platform Dr. Joris Timmermans introduced the platform to students and staff of the Faculty of Science at Leiden University. He explained the limitations of current remote sensing practices, the aim of MULTIPLY, the specific design choices and results. 

Human society increasingly relies on information derived from Earth Observation data. In particular, there is a growing demand for information on land surface variables. To facilitate these demands, the number of satellites and small cube-sat constellations are projected to increase dramatically over the next years. This increase provides enormous challenges to retrieve consistent high-quality information from this big data. Current approaches entail creating individual land surface products for each satellite mission. As such, the majority of available products are created using single-sensor approaches. This production-heterogeneity severely limits the advancement of research fields due to inconsistencies in comparing land surface estimates.

An additional challenge is that land surface parameter retrieval suffers from ill-posedness: the fact that there are fewer observables than the number of desired parameters (required for accurate retrieval). By only considering observations from singular satellites, this ill-posedness is worsened especially considering the various spectral-sensitivities of different land surface parameters. A multi-sensor approach capable of integrating such sensitivities, by accurately modeling the physical radiative processes, resolves these limitations. Furthermore, such a multi-sensor approach also allows benefiting from synergies of using multi-scale/heterogeneous observation types/varying temporal frequencies of different sensors.

Based on these concepts, the MULTIscale SENTINEL land surface information retrieval PLatform (H2020 MULTIPLY) was created to obtain the best possible estimate of the land surface state, taking into account the different characteristics of different sensors and data streams. The MULTIPLY platform builds upon the original ideas implemented in the EOLDAS system but has major advances on operationalization, enhanced consistency (across sensor types), higher computational efficiency and improved gap-filling. Specifically, with the MULTIPLY platform, it has become possible to

  1. Apply generic atmospheric pre-processing algorithms;
  2. Derive estimates of land surface variables that are gap-free;
  3. Combine data from multiple satellite constellations within one internally consistent retrieval based on radiative transfer models;
  4. Combine data from SAR observations with optical remote sensing data using compatible radiative transfer models;
  5. Derive a set of internally consistent data products that couple different (coarse and high) resolutions.

The prototype of the MULTIPLY has been implemented as a cloud service and is currently being tested by researchers.