IGF 2021 – Day 0 – Event #108 Earth Observation satellite data to power digital economy

The following are the outputs of the captioning taken during an IGF virtual intervention. Although it is largely accurate, in some cases it may be incomplete or inaccurate due to inaudible passages or transcription errors. It is posted as an aid, but should not be treated as an authoritative record.



>> MONIKA KRZYZANOWSKA: Welcome, everybody.  My name is Monika Krzyzanowska from CloudFerro, and I would like to welcome you.  And in particular, I would like to welcome Kinga Gruszecka from Polish Space Agency, a cohost of this panel, and our distinguished panelists from the work of Earth Observation, Science Industry Administration.

Normally we should have with us Mr. Agnov; Mr. Krzysztof Sterenczak, who is with us; Mr. Jedrzej Bojanowski; Mr. Oleg Dubovik, who would connect remotely; and Mr. Stanislaw Dalek, who is on‑site. 

I am informed that there are some technical issues to connect remote panelists.  I hope they will be solved soon, but we shall start. 

So we should, as well, thank the organizers for this opportunity to discuss how satellite technology and, in particular, Earth observation, contributes to the digital economy

As you know, satellite observation is with us for over 60 years.  The era of satellite remote sensing began in 1957 and then followed by Explorer and Vanguard in '58.  Today, providing more than a thousand observations, as we say, remote sensing satellites.  Those satellites carry sensors that measure different sections of visible infrared or microwave segments of the electromagnetic spectrum.  Most of the satellites carry passive tools that measure, observe.  But some have as well active sensor, emitting energy emitting reflective or backscattered signals. 

We generally talk about the minimum size of the object we can see.  Spectrum characteristics, so what exactly does a satellite see?  Which spectral channels?  Radiometric?  How sensitive is the satellite?  And temporary resolution, and how often we can see the same area. 

Another aspect is the orbit altitude.  The type of orbit, geostationary or (Inaudible). 

The data required by the satellite are transmitted, processed, and stored in dedicated repositories for further use.  For example, Sentinel 2 from Sentinel constellation provides data from 13 bands, visible, near infrared, short wave, and infrared, and has a time of ten days to get the same angle.  Special resolution depends on the sensor.  It can be 10, 20, 30, 40, or 60 meters.  And it is an orbit around a bit less than 800 kilometers.  The images you can see or you see on the screen are examples from the Sentinel constellation from the Copernicus program.  Copernicus is a common program from the EU.  It is implemented in partnership with the Member States with European Space Agency, European Organization for the Exploration of Meteorological Satellites, European Center for Medium‑Range Weather Forecasts, ECMWF.  Vast amounts of global data provided by Sentinel satellites are free and openly accessible to users.  Since 2014, there are over 50, 70 petabytes of those data, and they are growing every day. 

>> Thank you, Monika, for this introduction.  I hope it will bring satellites closer to our audience.  And as it was said, the images that I hope you can see are from the competition of this year.  As the space agency, we are trying to promote that it's never too late to get into Earth observation, either to use the products of Earth Observation or to start serving such products and services. 

I should add that Earth Observation and Copernicus is not the only program of European Commission that brings us valuable data from satellites from orbit.  We should remember that there is Calilo and Agnos that brings information about, for example, positioning, GPS signal, and it helps us not only in geodesic, for example, but also for example, in banking.  And then European Commission is working currently on satellite connectivity and communication, satellite communication.  The program will be called GOVSATCOM, so if anyone is interested in this, connectivity in remote places, like for example, ocean, you might want to visit trusted website where you find more use cases on connection and satellite connectivity. 

Today we will focus on Earth Observation, and we will discuss something that (?) space is the backbone of the (Inaudible). 

And we will start with a short technical check.  First of all, if you will have any questions to our panelists, please write them in Chat.  We will check the Chat, and during Q&A we will address the questions.  And then I would like to check if Alain is with us. 


>> MODERATOR: There are still technical issues.  So while we are waiting, let's introduce him shortly so when he joins he will already be presented. 

>> Definitely, yes.  So we invite Mr. Alan to join us due to his excessive knowledge on maritime.  First of all, he graduated from ECO Polytechnic, his PhD in 1997, and he cofounded a few companies, also he has an experience with French Space Agency and European Space Agency.  Right now, since 2017, he is leading the digital transition at Mercator projection as collaborative with Omensat, and he is an organizer of digital twin of the ocean initiative. 

>> MODERATOR: Moving from ocean to forest, Professor (Inaudible) Deputy Director of research and science for the Forest Research Institute.  He deals with broadly understood application of Joe metrics as the forest and monitoring of the environment.  In particular, several projects connected with forestry and forest‑related issues. 

>> Jedrzej, who is also with us here, he is head of Remote Sensing Center of the (?) he received his PhD degree from International Institute for Geoinformation Science and Earth Observation.  And his main domain is agriculture. 

>> MODERATOR: Following agriculture, we wanted to focus on air pollution, and we have invited Dr. Dubovic, who should ‑‑ hopefully he is connected. 

>> He is not connected. 

>> MODERATOR: He is not connected, but hoping that he will be able to connect.  We will just say that (?) and that he was working in the field of remote sensing of the atmosphere and Earth observation for many years, and at least ten of those years were at NASA.  He is an expert in the field of hearth science and received several professional recognitions. 

And our last panelists. 

>> (?)

>> What we can do right now with Earth observation data.  I hope you will have this valuable voice on what we can do and why we then talk about accessibility of data. 

>> MODERATOR: Coming back to the topic of our panel, could I invite (?) to present to us related to space? 

>> Maybe I will start with introduction who can use and how the data is used in forestry.  So I would say there is a wide range of possible use.  So we start from forest fires, which are hot topic, especially in the southern part of Europe, and of course, worldwide.  Forest health, concerning the climate change is another issue which I think changed a lot from the practical point of view.  And I think makes the society open for space data.  And of course, has a huge influence on the condition of forests worldwide.  We have biomass.  We have carbon storage.  We have a growing stock volume.  Those are the most important issues related to forest characteristics and use worldwide.  We have many products, global products.  For example, a project funded by ISSA, which shows us how the biomass looks like and how it changes during that time. 

Of course, as far as dynamic, the huge impact of what people are doing is, of course, related to how forests change, and with use of multispectral data and multitemporal data, we can study this phenomenon. 

Of course, deforestation is another issue related especially to southern America, where we are really ‑‑ would like to say the Amazon forest will stay as it is now

And who can use it?  I would say everyone.  We have policymakers who can create a policy based on information taken from Earth observation data.  We have governments.  For some parts of the world, the remote sensing data is only one information source for them.  There is no additional information.  So this is a crucial issue for them to have access to that remote sensing data. 

Of course, there are public institutions, and of course, researchers, which developed algorithms to better analyze the data, and of course, to push the technology forward.  And on the end, there are practitioners, foresters, which use the products from the Earth observation data in their practice.  So there is a huge branch of remote sensing application in the market. 

>> MODERATOR: Thank you very much.  We are still waiting for ‑‑ we will ask ‑‑

>> Yes, we will discuss use cases of agriculture. 

>> I will start with the sentinel 1 and 2 satellites because these two satellites offer, like, unique unprecedented combination of temporal and special resolution, and this is a big change and opportunity for agricultural monitoring because for the first time, we can basically monitor each particular field how it was growing and how it is growing.  Of course, some limitations, but for the first time on the large scale, country scale, original scale we can do that.  That's really a game changer.  For the use cases, we are just about to finish two big projects in cooperation with the Center for Polish and Academic Sciences, funded by European Space Agency and the National Center for Research and Development.  So to exploit Earth observation data, satellite data, for Statistics Poland, so the national statistical office, and the agency which is responsible for paying the subsidies to farmers under the common agriculture policy. 

So for Statistics Poland, the objective of the project was to get the statistical information on agricultural production.  And for this, you need two sides.  One thing is we need to know the area of the crop zone, we need to recognize crops in the field.  And the other aspect is expected crop yields for the unit area, so the multiplication of these two sides, you can get the core production.  So we built a system, operational system, and it runs in statistics Poland, getting automatic information on core production assessment along the season.  So this is one implementation of the ‑‑

>> If I may add, I heard that that's only such use for statistics in Europe. 

>> I also heard that. 

>> Congratulations. 

>> Thank you.  To start.  I think it's not the first attempt to use Earth observation, but it's the first operational system fully implemented on‑site. 

This is one use, and the second is for bank agency because it needs to control the farmers' declarations based on which farmers are paid. 

So there is a second system also operational where we create reports on each field that was declared for the subsidy, where we checked if the crop zone is the one that was declared, if the agricultural activities were performed according to some rules; we check if the vegetation cover, which is necessary to be maintained against the soil erosion, is there.  And some other, more detailed aspects.  And this is also important, this implementation, because since Statistics Poland is not obliged to implement anything, bank agencies obliged and all Member States are obliged to start so‑called control by satellite monitoring, I think starting in 2023, so next year our system will be, like, operational ‑‑ it's operational, but it will be a phase at the beginning to be sure that Poland will comply with the requirements of common agriculture policy. 

And the last word is that none of those will be possible without IT infrastructure we use for where data is stored, but I guess we will talk about it later. 

>> But you already started a little bit the discussion about the maturity of the services and the (?) as well. 

I want to check if Oleg is with us, if he can connect.  I cannot see him neither hear him. 

>> MODERATOR: But since I think it's a very important part of the panel, air pollution, I think we should at least show the audience what are the topics we wanted to address.  And to say the great advantage of the space for the object of air pollution, they provide very quick global coverage.  They are neutral.  Those measurements are (Off microphone).  Space missions related to Earth observation are quite extensive, and of course, the resolution is still not satisfactory to tell if the air in your backyard is clean. 

>> I think what is also important to tell you about atmospheric applications is this is a kind of application that is quite similar to what is being done with weather forecasting.  And what was said before regarding the general mechanics of the satellite ‑‑ observation satellite is that most of the imagery related observation satellites that we are discussing are low orbiting satellites, satellites that orbit on polar orbits that are 700, 800 kilometers high, and a revolution lasts around two hours.  While for atmospheric applications, it is often very important to be able to stay over one point over the Earth all the time, to target the same area, to have ‑‑ to have a constant view of the area.  Those are much higher than the low orbit.  The stationary orbit is 36,000 kilometers. 

>> We are trying to optimize the use of orbits as well, and we have to protect them.  I hope our audience will remember that even though satellite imagery provides us valuable information, and on that we will discuss more, we have to remember that they either are checked by data from other sources or they are complemented, and they aid the atmospheric climate information.  Without sensors on Earth, the information provided by satellites is very limited.  Only with the combination of both data we can actually understand what's going on. 


>> MODERATOR: You have talked a little bit about remote sensing (Off microphone).  How does it look from a forestry point of view?  Actually for agriculture, is there more data needed? 

>> Yeah, we say that the experience with use of remote sensing data in general for forestry use is, of course, wide and old, I would say.  It started from the beginning.  So there is a multiple applications.  Of course, this depends on the country, on the infrastructure know‑how, because we know that data itself is kind of information to really provide a suitable information, you need to process.  And on the end, there are some valuable information.  But for example, we know that our huge group of people working on the global scale, so there are a lot of programs checking how the forest looks like to the whole world, especially the Amazon.  As I said, there are systems dedicated to forest fire.  Europe, I would say that the use of data is more on a homogeneous ‑‑ to provide homogeneous information about the forest because we know that forestry is like 300 years already of history.  How to protect and manage the forest to provide wood.  So in Europe, for example, there is a huge history of forest management.  So basically, we have maps of each specified part of the forest.  But then the forest definition or the forest structure in each country is different.  So when we want to have information on the European scale, then we have problems with some definitions, some issues related to how we see the forest and the role it plays.  I think in this work you can see there are programs, for example, European Forest Institute started a program to have a homogeneous similar from the (?) point of view information from the forest status in Europe. 

When you think about Poland, this is once again a specific situation.  We have a national state forest, which is huge, managing about almost 80% of our forest, so they have a unique and similar digital system in which they store almost 200 variables related to the forest.  So of course, for managing forest health, I think this is a crucial issue related to the dynamic changes in the forestry going on not only in Poland, but worldwide.  So I would say, to summarize, it depends on which part of the world we are talking about, the use of space‑borne information is different, but if you want to have a similar information about the forest in Europe, in the North, we wouldn't do this without the remote sensing data. 

>> Maybe ‑‑ so we discussed the forestry and how it's needed.  We also heard about how available data are for agriculture.  But then let us understand if those data, the services ‑‑ because data is one thing, but then you have processing the data.  You are offering the services.  So is that for everyone, or it's just for large institutions like statistical office, the institution that is are responsible for forestry?  What benefits have the ordinary person?  I know Poland is seen as agricultural country. 

>> Well, it's a complex question, I would say, because I guess for the institutions it's maybe easier to formulate the user needs because the dialogue, you have to discuss with one entity or two entities.  So this may be a bit more straightforward. S although still difficult because both sides need to learn, you know, what is really needed, what is possible, what are the limitations, and that's always a process.  But in terms of agriculture for public, I mean, of course, we have a huge parallel, I would say, field which is precision agriculture, where you use Earth observation data as well.  And of course, then the information is more dedicated to individual farmers or companies having, like, bigger area, but still looking at particular fields.  And I say that it's a bit parallel because, I mean, the methods are quite different.  We are not yet there where we can use really precise information on fields, even within fields, and then aggregate it to be available information for institutions like I mentioned, like Statistics Poland.  Probably we will get there at some point.  But I think the benefits for precision agriculture may need the fertilizers measurements, where you have to water plants were not so optimization, reduce costs.  Probably a bit of yield forecasting at each field.  Agriculture activities support by satellite also indirectly because the navigation is also satellites in part. 

>> So before I will ask for whom data and services of the forestry, I would like to check with you one thing about in situ data.  I remember I was talking with Statistics Poland, and they told me that the validation of data and the service that you mentioned about took them two years to find out what the satellite is and what you think it is, it's right there. 

>> Yeah, so I mean, without in situ data, we cannot do much because we cannot not only validate, but we cannot train the models and learn.  Of course, there is a long history of observation, so we have some knowledge about, you know, what the satellite signal means for ‑‑ I mean, what physical properties of plants, for example, need for a certain signal. 

In agriculture, one thing is easier to measure, which is just to have in situ information about soil because this is very objective, to a certain extent.  But let's say we can recognize crops on the field.  And this was done by Statistics Poland in field campaigns for many years, and it's a huge effort because there are thousands of points in whole Poland, so the cost is really high.  The solution is the farmers' declarations, because then we have many more points, but subject to uncertainty because since we need to control farmers, it means that we assume that maybe there are errors there in this data.  Which is more tricky thing, in situ information on crop yields because this is not so easy to measure.  It's not so ‑‑ it's a bit subjective because one thing is biomass, one thing is grains, quality of grains, and so on.  And this is still a really limiting factor.  I think the only solution for that, to have the homogeneous data on this, is probably synchronization of detectors on harvesters.  And I know that European Commission thinks about it, to have common standard for data, because without this, it's really difficult to go forward because the number of sites where we have data is really limited.  I mean, for modern machine learning techniques, we need really huge samples.  So yeah. 

>> I think this issue of data homogeneity is something we see across the panel and the different use cases of observation data, and we often find that the users of our platform ‑‑ we run a platform for storing and processing Earth observation data ‑‑ we often see that users spend a an awful lot of time homogeneitizing data.  Often the most important data comes from in situ observations that have different degrees of certainty and quality.  Then they have data from several generations of satellites that have quite incompatible data, both formats and precision, and this leads to, especially in machine learning, where the source data is very important, it leads to many difficulties and biases.  So this is the major effort. 

Talking about the applications and users of the data, a trend is now happening that we see a large democratization of this Earth observation data.  Something that was once reserved to military, then to big institutions and scientists, then comes the industry commercial businesses that make more and more use of this data.  And then the challenge now is to simplify the technology enough to make it accessible to students, to citizens ultimately, to make it much easier to access and consume this data and extract information out of it. 

>> Yeah, but I think European Commission is, I think, extra mild when it comes to making data available.  And I think (?) is one of the examples of the how.  And Monika once showed me how the platform works, and I am not a scientist.  I am not even remotely connected to imagery.  And it was super easy to use. 

But then you mention another very important topic, that everyone can now view Earth observation satellite.  So as I said in the beginning, it's not only everyone can use it, not everyone can analyze it.  I mean, yes, everyone can analyze it, but it's not reserved.  Everyone can participate in exploration of our Earth to understand what the process is. 

But I would like to get back on the users and cases.  And in situ data, getting in situ data is a challenge.  In agriculture, is it in forestry as well? 

>> Yeah, I think even more. 

>> Why? 

>> Yeah, we say about forest that there are not crops.  You cannot find one crop.  We are talking about biodiverse forest, multilayer, so it makes things more complicated.  So yeah, there is a lot of limitation.  Especially with spectral imaging.  When it comes to the in situ data, you know, so you have very different forests in the world.  You have different species composition.  You have different age classes.  Which means that you should have information on the single tree level.  The example of very good in situ data, an example from Poland, which is a project I managed, it from the National Center of Research, so we count 3.5 thousand trees to measure the volume at the single tree level, and then we move to the volume, then to carbon and biomass.  But this is just one tree.  Now, when you consider on the sample level, even 40, 50 individuals, then to generate this information, the sample, this is still a sample.  And then you need to transfer to the country.  So there's a lot of issues. 

What you can see is there is wide use of data for maps, widely used for calibrate models, space remote sensing data, or to evaluate the quality.  So I think for the practical forestry, for the really precision forestry, there are some data on the market.  Of course, we need to pay for that.  But after we move to, say, one meter resolution, for free, I think we will be in a place where everyone wants to be. 

>> Are we moving to one‑meter resolution? 

>> I hope. 

>> That's the point.  As you say, commercial. 

>> You know, we need to update information.  There are some dynamic feeds.  For example, in Czech, Germany, Poland, U.S., so everyone wishes to have, let's say, every week, every two or three days precise information, and then we can use it for management.  Otherwise, if we have once or two times per month or even a half‑year time, one or two images, we cannot really use this in the operational forestry. 

>> I am checking time, and we have some time for maritime. 

>> MODERATOR: (?) but I think our panel wouldn't be complete if we didn't say a few words of the use of remote sensing for maritime applications.  So of course, I am ‑‑ not being an expert, I cannot tell you all that was said here, but it still can be said that Earth observation data are widely used in the ocean and maritime applications.  There are over 30,000 users from the Mercator Ocean.  There are what's called Copernicus marine services, and for instance, such information are available to everybody as the global ocean modeling and measurement.  Water maps, heat, there is a really huge amount of data information and services related to this very, very important part.  This is just to complement our range of topics. 

>> I wanted to add to that about maritime because this is quite a unique field in Earth observation because it's something we cannot do without Earth observation.  And this was the same for polar regions.  At the first, people started with Earth observation, I don't know, 30 or 40 years ago on land, especially in Europe or U.S.  You have a lot of information from the ground, from the field service.  But on the ocean, like there is no other ways.  And ‑‑ which is a challenge because it's like in situ data.  But on the other hand, you can provide some information that, you know, something to really, really use, and it's quite an exciting field. 

>> And we see all those pictures from the competition that very often shows the beauty of ocean but, at the same time, we better understand what our action has when it comes to oceans' health and sea health. 

>> Yes, the ocean is two‑thirds of our globe.  That's why it is very important to take care of that. 

>> Yeah, and we have also the very dedicated application, the services that helps tourists fund vacation.  So going from the global scale and what's on Earth, we also can apply it to our everyday lives. 

>> And also the other issue is that since special resolution is so important, there are some aspects that still it's important, but so what we benefit, we can have information for, like, 40 years now about the ocean.  There's something we cannot really talk about Sentinel 2, so high resolution we can use.  We have five years of data, and it's really limited what you can do. 

For the ocean, I mean, of course, there are some aspects.  Also data homogeneity, and there are some work on that, but you can really observe long‑term changes. 

>> Yes, on the contrary, with moderate satellites that have higher resolution, higher refresh rates, like for the fisheries monitoring all ‑‑ monitoring of all illegal ‑‑ potentially illegal stuff that happens at sea.  These are the new applications that are enabled with high resolution frequency. 

>> Including pollution. 

>> Including pollution, of course. 

>> I often get a question because my role is to promote satellite data to, for example, public administration, I often get a question how often I can get information.  So it's very important also to, and it was already said, to have dedicated infrastructure.  And now we introduce you to someone who will comment on technology. 

>> Yes, so I was injecting my few words into this previous conversation, but generally the challenge of Earth observation data is around its variety of sources and volume of this data.  This is one of the big data areas.  The quantities of this data augment massively with the number of satellites flying around, with higher visit times and higher resolutions, and all those factors multiplied result in an explosion of the quantity of data we are talking about. 

So, ten years ago, people used to acquire this data from different sources.  But in order to do something useful with the data, as was said, you usually need to combine data from different sources.  You need multispectral imagery and radar, for instance.  Or you need to combine it with some in situ data.  And very often to combine data if it's a long time series, if it's a long analysis, from different satellites that were around some years ago.  And in order to do that, you had to gather this data and spend a lot of time on assembling, downloading, and making it homogeneous.  You also had to have a huge data center to process this data and to pull something useful out of it. 

Now with platforms such as one run by CloudFerro or WEkEO, we are also participating to, you can get this data assembled and homogenized in one big database that's searchable and where you can access this data easily.  And you can just grant the processing power that you need.  You don't need to own the data center to process data, regional now or even at the global scale, to run huge processing.  And this all makes the technology more and more accessible to everybody. 

As I said before, it is ‑‑ there is this democratization of technology, and major challenge right now is to simplify the technology.  We often see the major group of users are scientists or institutional users, and they are not necessarily experts in the mass part of data processing.  This is what a platform such as ours brings to the table.  It makes it easy to scale the processing up to the level of a global processing campaign.  You just need to know and to understand your specific matter, whether it is forest or agriculture or other applications.  And to know the imagery or radar data processing algorithms that are necessary to extract the information that you need.  The platform performs the scaling for you, and this is a great advantage. 

So the challenge ‑‑ so our ‑‑ we see simplification of Earth observation information as our mission, but to engage more and more people in it, to make it more available for everybody, and to make this engagement be lateral, I would say, so that users, this is very much oriented with, connected with the in situ data that ultimately, with the proliferation of IoT and handheld devices and all sorts of sensors that people carry on with them, more and more data will be coming from citizens and users themselves.  So there will be a closed feedback loop to feed such data and to make it, to combine it with satellite data to make it even more useful. 

>> I see Maps Google observation data and everyone providing information, which is very beneficial, I assume, if we want to have information from less than one meter resolution.  At the same time, well, it's somewhere we are going. 

>> I think considering the general social situation nowadays, it is very important that satellite data provides this objective data that we can say it's ground truth, but it's space truth.  Yes? 

>> MODERATOR: Thank you. 

>> If I may just add one sentence because we mentioned a few times that imagery is often covered by cloud, and that when ‑‑ that's the case when we are dealing with some satellites.  While satellites enable us to observe and utilize the cloud information.  But now I just found out that European space Agency is highly promoting solutions that enable satellite to recognize if the image is cloud, covered by cloud, or not, to not download imagery that is not needed.  And save also some money. 

>> MODERATOR: I think that we are shortly going towards the end of our panel, so I would like to ask the panelists to comment on one of the two topics, but having in mind we have mentioned a few times modeling, digital, we focused more on measuring, but then what about modeling of the future?  And one of the two topics we'd like you to comment is keeping in mind (?) or is digital economy now new space and Earth observation data?  Could we start with you? 

>> Yeah.  I think the answer is obvious.  Yes.  But I think ‑‑ I just want to comment one.  I think that I would avoid a statement that Earth observation provides always real data or, let's say, not reference, but real data.  This depends.  There was a huge discussion about JRC paper in nature about deforestation.  My colleagues from different countries were disappointed with the conclusion, so there was another paper in Nature Survey guided by Director of Forest Research ‑‑ European Forest Institute, and I would say that the conclusions were different.  So I think that we really want all people to really use the data, but they need to have knowledge.  Otherwise, we make Facebook from the science.  So everyone who has an account is an expert in Facebook.  We need to avoid this in the science.  There needs to be ‑‑ really know how people process the data, what were used to evaluate the quality of the product.  This should be open and free, available so everyone can check it and have a discussion.  I think we should rather think how to communicate it in these times.  Because there is so much different data available, and I think it's always hard to decide what should I use, especially the technology really changed dramatically, and for example, we had in forestry a standard data introduced a long time ago, and there is many studies how we can use it, but not many places where we are using it.  And now we are talking about pooled space.  Now we have full wave form standard data, the science is here, the practice is here, so the gap between is growing.  So I think we should first think how we, having access to the data, should communicate and talk about quality, talk about products, not against each other.  And this is the issue, I think.  Especially, of course, we have policymakers.  They ‑‑ I hope ‑‑ create a policy based on the data.  But in the end, we have a public or even NGOs which can check the data and maybe has different conclusions.  So in the end, those experts who have done the work need to meet and agree how and why they have these differences and the different outputs of the study.  So I think we have very nice times, as you mentioned, there was images from a 1997 program where they collected data for Poland.  And the images were very poor quality, everyone argued, but we all were happy that they were.  And now we have access to Sentinel, almost daily information.  We have, of course, different platforms which can make image of the Earth every day.  So they are very challenging and nice times, but I think we should concentrate how to communicate and understand each other. 

>> Yeah, even at my work, the very easy to observe gap, you mentioned, science and, for example, industry.  I often feel like there's not enough discussion, and industry brings a perspective often, not always, but often brings the perspective of what's needed.  They bring the understanding of market.  And scientists often focus how to do it.  What's the challenge?  How to solve the problem. 

>> And while we are talking about forestry, for example ‑‑ this is what was mentioned ‑‑ the new technology didn't provide much new information.  Easier way, maybe access every week, et cetera.  So the issue is to understand each other.  I think this is the key question because sometimes scientists cannot understand, so how we can expect let's say foresters to understand scientists?  So this is something. 

>> In the Chat, if there are any questions, please send us the questions.  We still have a few minutes.  And now to agriculture. 

>> Okay.  I can try to merge answer for both questions you asked.  Because one thing we also ‑‑ because to make it a better place, one thing is that we want to understand the processes on Earth.  And something we didn't maybe mention yet is that maybe even in a bigger extent satellites are used to assimilate to models that describe what's going on on Earth.  Maybe it's less visible for users, but it's probably, if you count the amount of data which is really used every day, more it's used in the models that describe weather interactions between atmosphere and so on.  This is one thing. 

In talking about agriculture, I think there are huge opportunities, but we have to use the data wisely if we want to make a profit for us.  Because one thing is that we can optimize, identify agricultural production, but with the cost of diversity loss because of greenhouse gas emissions and so on.  But on the other hand, if we use it to really, like in the farm‑to‑fork strategy under the European Green Deal, if we want to make the food more healthy and agriculture production more sustainable, I cannot imagine this without support of Earth observation.  So I guess it depends on our objectives.  We can use it in a good way, but also in a bad way. 

>> Always depends on us. 

Maybe I will start with saying that when we prepared for the discussion, Monika and I, and we met for the first time with our panelists, I had this feeling that digital economy is not any longer possible without data from satellites.  Either it's imagery, navigation, positioning, and soon to be also connection, connectivity in remote area.  Today it was said that satellite data, satellite imagery are often the only source of data for places like oceans and very often also on land where there's no crucial infrastructure or no data from other sources.  And to understand what's going on in there and how we can either help to build healthy society, we need data that are basically simple data.  Not information that is processed, but that bring us larger perspective.  And we have it thanks to satellite imagery. 

We discussed also that satellite imagery is almost for everyone.  Not everyone can process the data, but everyone can benefit from it.  And I think that's very, very important to understand that it's not about large institutions, it's not about scientists.  It's not, finally, about just people who are space geeks.  It's also about every single citizen.  And I hope that is an outcome that will come from this discussion. 

>> MODERATOR: I think that I can only say that you have summarized our discussion.  I hope we have convinced our audience that yes, Earth observation data is there, satellite technology is there.  It's a tool that ‑‑ to be used by us for the Earth to become a better place. 

>> I see there is no questions from the Chat, but if you would like to ask, people who are on‑site or online, you can ask on Twitter with the hashtag of our session, which number is 108. 

I see one comment from Mark Corbin, thank you very much.  And this is about micro ‑‑ about there is a group promoting the use of existing connectivity for observation, GEANT in Europe, RedCLaRa in Latin America, ASREN in Arabic countries are members of GEO.  

>> To comment on Mac ‑‑ Mark's comment, our data is connected, so scientists have good access to that data. 

>> Okay.  Thank you very much, then.  I hope you will join us for future opportunities.  Thank you.