Our first stop is the German Research Center for Geosciences in Potsdam located on the Telegraphenbach, in a historic science campus surrounded by forest. At the GF said, scientists study the dynamics of planet Earth, not just the solid Earth and the surface on which we live, but also the hydrosphere, atmosphere, and biosphere, and the chemical, physical, and biological processes that connect them. Among others, Earth observation is a key technique for understanding the Earth. It has a long tradition here on Telegraphenbach. Besides the GF said, there are other research centers here on campus, such as the Alfred Wagner Institute for Polar and Marine Research, and the Potsdam Institute for Climate Impact Research, as well as historic buildings like the famous Einstein Tower. For our first expert interview, we will meet very close to the Einstein Tower. I'm a specialist in hypospacial remodencing and have been working in this field for the past 25 years now, and especially I'm a specialist in sole spectroscopy and digital soul mapping. We are part of a section of remote sensing and geopharmatic, and we are actually pioneers in the field. We have been working on advanced optical remote sensing sensor definition and also new methodologies for the past 25 years now, and nowadays we are one of our key players internationally. So in 2003, this is our section under the lead of Charlie Kaufman, who was the head of a section at that time. We proposed there was a DLR announcement of opportunity for German national missions, and we have a one who proposed a hypospacial satellite. This mission is now funded, so nowadays we have a head of a science segment of a satellite. We are coordinating the Edmab Science and Advisory Group. We are coordinating the Edmab PI project, which is a project to support science development and algorithms development for the exploitation of Edmab data. So this is a national consortium with four partners in Germany. So in the phase from 20 to 23, we are covering the pre-launch phase, the commissioning phase, and the start of a nominal phase, and it's focusing on the mission preparation for the exploitation of the data and support to the DLR. In particular, some focusing these current phases are the development also of educational tools, like the Hyper-EDU platform, some flight campaigns, and some algorithms development in the Edmab box. Another area of development is free and open data and free and open software. So the Edmab data will be offered for free for the user community, and software's are available for free also. Right now in the context of a big climatic and anthropogenic changes, there's lots of soil degradation, and hyperspectral remote sensing technique is something that can really help in characterizing on terms of quantifications, composition of the source. Spaceborne hyperspectral remote sensing is very interesting because the difference of field or laboratory data that are available in a test case study, and also field and laboratory data are very costly. You need to organize a team going there. It takes time, it takes lots of instruments. So the Spaceborne allows to have a repetition of acquisition of a data, and so you can do also monitoring of Earth surface processes and mapping some changes that the surface leads to anthropogenic and climate changes. So the advantage of a hyperspectral remote sensing technique compared to multispectral is that we have access to the anostics signal that is really characteristics of a mineral surface. For example, we can directly from looking at the physical signals received by the satellite we can directly identify is this a calcite, is it iron, is it clay, is there some organic carbon content. So all these are really diagnostic from the signal, and this is something that only the hyperspectral technique can provide. For example, we had a very nice demonstration scientific work that was based in southern Spain, so in the Cabo de Catana, natural park. There is an agricultural area, it's an iron-witch source, and in this area thanks to hyperspectral remote sensing we were able to simulate upcoming in-map images, and we could also simulate upcoming end-map products in terms of iron oxide content map, and also click on that map, which is very nice because it delineates the areas with more thermostatic and more fertility. And in another science study, we are working in the Terreno long term observatory in North and Germany, so the name of a test area is Demine. And in this area we have very high variability in organic carbon, other top source, at the surface of the source. And in this area we were able to simulate end-map imagery also, and to develop new tools and to map so organic carbon content at the surface. So in another science issue, we looked at the impact of hyperspectral remote sensing to map raw materials at the surface and in the mining environment. So this is a study that tests sites in Namibia and North and South Africa, and they were able to do a very nice simulation of end-map products with identifying the different geological minerals. So this is really a very nice study because it shows that in such an arid environment and also it's an area, it's a very rare terrain where normally for geologists it's very hard to access, you can develop such products. So the range of users is endless, includes NGOs, governmental institutions, public authorities, individual farmers like individual people, so the user groups are very endless and very wide. Current challenges now are in the expectation of data because they are still some methodology that involves big data, data science, machine learning and artificial intelligence that are now in full development and those new methods will be very useful to extract the complexity inside the hyperspectral data. And the important avenue and development and current challenge now is that the community is still not trained to use or to know the potential of this data and we have to develop a big educational program such as MOOC here or such as HAPPA in the U and we have such educational programs also international leave at our end-of-the-world months so that the users will learn how useful is data and how we can process it.