The European Geosciences Union General Assembly 2019 from the MULTIPLY perspective by Gerardo Lopez-Saldana

The EGU General Assembly always is a great opportunity to both show your science and catch up with the latest scientific findings. EGU2019 was no exception. The MULTIPLY team was offered the opportunity to present various developments. I had the chance, on behalf of the Assimila and UCL-Geography teams, to present the use of MODIS and Sentinel-3 OLCI data to better characterise the land surface. What’s the result of combining MODIS and OLCI observations? The MOLCI.

The highlight of the week for me however, was the hands-on MULTIPLY experience session. The main goal of the almost 2 hour session was to demonstrate the MULTIPLY platform and provide some theoretical background about Radiative Transfer (RT) models  such as:

  • the JRC-TIP;
  • the integration of a priori knowledge when retrieving land surface parameters, e.g. Leaf Area Index (LAI); and ultimately,
  • how to combine observations and prior information in a Bayesian scheme.

Great lecture, really well done Joris!.

The hands-on started…… and the challenges started. When you have a reasonably good processing server, performing an atmospheric correction of one Sentinel-2 MSI image using the marvellous Sensor Invariant Atmospheric Correction (SIAC) approach developed by the UCL-Geography group, everything runs smoothly. When you have 10 simultaneous processes, performance starts to slows down. This brings an interesting question, namely: you, as a user of the platform, where do you want to run your processing? Where can you do it? Would you pay for it if needed? In MULTIPLY we will address this in the near future. However this is a common issue nowadays. There are multiple cloud-computing providers with access to Earth Observation data. Perhaps the most used one being the Google Earth Engine (GEE), where you do some actual processing. Nevertheless GEE’s web code editor uses Java Script to do the processing. We don’t want to develop a RT model in Java Script. You can use its Python Application Programme Interface (API) but then the computing power will be yours, not the GEEs. Hence, the GEE is a great tool to perform some tasks but not necessarily to perform atmospheric correction of Sentinel-2 data and retrieve biophysical parameters. At least not yet.

Then, we showed some more Jupyter Notebooks. The hardcore ones we used in the platform. They brought some more interesting questions such as: “can I use my own priors?” and “could it be possible to use a different RTM?”. Basically the answer is, “yes” and “yes”. We are trying to develop the platform being as flexible as possible. In the end, if you are a scientist who knows your area of study, it doesn’t matter if it’s a micro-basin in the middle of the Amazon or a set of agricultural fields in East England. You know the characteristics of the area, you have a broad expectation of what the outputs would be. In MULTIPLY we want to take advantage of this knowledge. Hence, it’d be possible to use your RTM model of preference, create some emulators so it can run super-fast (the scientific world will always be thankful for this Jose!) and use it within the Data Assimilation MULTIPLY inference engine, KaFKA. Obviously, it won’t be that straightforward as it sounds: you might need some help of the MULTIPLY team (in exchange of food or beer) but the point is: it is possible. Right now we have three different RTMs that take different inputs, from broadband albedo to narrow band reflectance and microwave backscatter. Additionally, if you know the inputs of the RTM, you can create your own priors. In the end a prior is only the probability distribution where you can express your belief about a specific quantity before any observations are taken into account.

d21d1a89-697c-468c-9a35-09efba64952eAfter two hours of lecture and Python and plots and logfiles and questions and answers, it was clear that the MULTIPLY project is facing a great challenge and providing some solutions. But we are still short as an Earth Observation scientific community to embrace the use of multi-sensor products, rather than relying on a per-sensor product and to use uncertainties along a whole processing chain, all the way from the sensor observations to biophysical parameters. Therefore our task within MULTIPLY is to widen even more our scope to show, particularly early-career scientists that, this approach can make the most of all available observations and provide an uncertainty, a sense of how good the retrieval is. Once again EGU2019 was great but the best part was the chance to interact with scientists, looking to make a difference using Earth Observation data. And of course, the MULTIPLY Platform will be there to help them.

MULTIPLY launched!

From the press release as published on the Website of Leiden University.

Leiden University launches Earth Observation platform

A new online platform makes it possible to estimate the state of agricultural crops and nature area’s around the world. This enables scientists and other users to consistently combine observations of different satellites for the first time.

The platform is called MULTIPLY and was launched in November by the Institute for Environmental Sciences (CML) of Leiden University. For 8 years, researchers from CML worked together with European partners to develop the platform.

Information of multiple satellites

The platform is unique because it combines the information of multiple satellites with varying resolutions and information, instead of using only one individual satellite. This enables MULTIPLY to generate breakthrough information on vegetation and soil moisture.

This data is crucial for different applications such as mapping evapotranspiration during droughts, monitoring declining trends of biodiversity, and quantifying ecosystem services.

Oil-palm plantations

Researchers of the CML have used the platform to quantify the impact of oil-palm plantations in Northern Borneo on biodiversity for the first time using earth observation data. The high resolution of the platform data enabled them to distinguish between the different land uses. The study confirmed a significantly lower biodiversity for the Northern Borneo oil palm plantations, indicating higher risks to ecosystem services.

Currently, the MULTIPLY platform has only been made available to scientists for the purpose of testing it on their own research. During this trial-period, these scientists can explore the benefits of the novel approach, but also provide feedback on how well the earth observation information matches ground measurements. Next to these studies, MULTIPLY will further expanded to even more satellites. Afterwards, the platform will be delivered to the European committee which will allow this service to be available to the general public.


City of Toulouse in Southern France with surrounding agricultural fields. Captured on 10 July 2017 by Sentinel-2 and processed by ESA.

Review Meeting at Tartu Observatory

At certain points during a Horizon 2020 Research & Innovation Action, the project consortium meets for a review meeting. Together with the project officer of the European Commission and with an external reviewer they review the progress of the project. For the MULTIPLY project, on the 28th and 29th of Augustus, one of these review meetings took place at the Tartu Observatory in Estonia.

“It was a really nice and constructive meeting where we could present the current state of the project to the reviewers,” says Dr. Lea Hallik, team member of MULTIPLY and researcher at the University of Tartu. “As we are now finalizing the tests of the beta version of the platform, they were happy with our progress.”

Different types of users
“An interesting discussion was about how the platform should be accessible for two types of users. On the one hand, the more technical programmer that wants to create and improve products using satellite data. On the other hand, the earth observations consultants who are less technical and want to access only the end products. This is challenging and something we will have to work on during the next months.”

There was also time for some social activities like a nice tour along the visitor center and the space technology laboratory. “The location was great. It is in a beautiful green setting, 20 kilometers away from the city Tartu and its light pollution.”

MULTIPLY consortium members at the Tartu Observatory

MULTIPLY consortium members at the Tartu Observatory

From local to global
Hallik and her colleagues from Tartu University, study what kind of plant traits can be measured with satellites. Therefore, she collected data in the field on traits of both evergreen and deciduous trees during the past two summers. With this knowledge, she can validate the measurements from satellites. “I like that we, as a small research group, can contribute with local field measurements to such a big project.”

“In Estonia, we have six towers where you can reach the highest leaves of the trees. There we sample and measure leaf traits like reflectance, transmittance, pigment content, dry mass area, and water content. Because natural vegetation is very complex, especially in a forest, with multiple species and different vegetational layers, satellite data can also be challenging,” Hallik explains. “It is important to understand these time series of forest leaves because an important aim of MULTIPLY is to create time series and make seasonal changes visible.”


Samling at Järvselja forest, Estonia

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Observation tower at Järvselja forest, Estonia

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Fieldwork at Järvselja forest, Estonia