Are CubeSats suitable for remote sensing?
Business Model.CubeSat.Remote Sensing
In the last few years CubeSats have become very popular. With the recent launch of some 33 CubeSats from the International Space Station by Planet Labs in a commercial remote sensing mission, the hype is at its peak. The most intriguing question, in my opinion, is the business model. With several commercial American and European high-resolution and multispectral satellites in orbit and several more in the pipeline to be launched, one wonders: Is there enough business for all of them?
We consulted the NASA web site to define the mission for the launched CubeSats and were surprised to see these goals[i] listed:
“Commercial applications of the imagery include mapping, real estate and construction, and oil and gas monitoring.”
It may be interesting to take a deeper look into the commercial applications NASA cites to better understand the commercial model for spaceborne remote sensing. This is especially relevant in light of the problems that RapidEye had to face in a somewhat similar business model.
Planet Labs’ sensor has a GSD of 2-5 meters. RapidEye’s sensor has a GSD of 5-6.5 meters. However, the Planet Lab sensor does not have multispectral capabilities. It is important to keep in mind that many of the applications listed require spectral as well as spatial resolution. The prerequisite for technology to solve agriculture mapping problems, for example, is multi-spectral capability in the sensor. If you do not have such capability the only parameter you can measure is distance. Most agricultural work is out of scope in this instance.
In terms of the constellation, RapidEye has 5 satellites in comparison to Planet Lab’s planned 100 satellites. Planet Lab can provide a much better revisit time for every point covered on earth. RapidEye has invested roughly $230 million US in the venture, whereas Planet Lab has raised approximately $65 million US (far from reaching the end of their mission, Planet Lab will likely invest more money). Both company’s business models target the commercial civil market, namely selling images mostly to nongovernment entities. It is the writer’s opinion, that these companies each in its own way have enabled the industry to use cheaper images which is at least the first step in the commercial usage of routine remote sensing. The question remains, Is that sufficient or are there other missing parts in the supply chain?
Understanding remote sensing for agriculture
In theory remote sensing has huge potential in the agro market. Science shows us that we can measure several parameters related to vegetation stress: water conditions and nutrition, among others factors. The information is available to help farmers reduce production costs. Remote sensing and precision agriculture can help farmers, but only if relevant actionable information is presented and accepted in a practical way the farmer can use immediately.
There are several obstacles with this technology and its usage in the farms. One major obstacle is what information we provide the farmer. In other words, the farmer does not know what to do with NDVI maps, or a veg stress report. Someone has to translate that into practical, on-the-ground actions such as provide more water at a specific area or use fewer pesticides. The local VAR usually does not have the required knowledge to provide this service. The farmer must employ an agronomy or fertilization expert to translate field conditions into actionable processes. The practice for these professionals today is to visit the field and provide recommendations to the farmer. These experts may not understand or like the use of remote sensing and this reluctance to add this technology to their toolkit could serve as an obstacle in the adoption process.
Another problem is that farms are not large enough areas of interest, namely they are rather small in comparison to the collection capabilities of the satellite and they are scattered, which create a very inefficient collection plan. The fact that you need to image an area of say 60 square km as a minimum collection area does not concern the farmer and he would not pay for that, and one cannot be sure that all adjunct fields will buy the service in coordination with any other farmer. In fact different products require different sampling frequency which make such optimizations difficult if not impossible.
In summary, being able to provide images in the right sampling frequency and spatial resolution do not assure us that these images can be used by the farmer. There are more links in the supply chain that are needed which add cost and make the overall solution’s price not attractive to the farmer to assure his profit. Not only that, but the confidence level of the decisions taken based on these images has to be proven before the farmer can decide to use the service. This requires addition practical research and investment which is outside the scope of the satellite industry or that of the VAR and thus the existence of a reliable industrialized overall solution that can be used by the end user, the farmer, is not assured and can prevent the business model of the satellite owner from developing as expected.
The case of the real estate and construction industry
Real estate and construction industries use remote sensing, primarily to monitor and to design.
Construction needs detailed planning of the land used. These details include, among other things accurate maps of the infrastructure, as well as Digital Terrain Model (DTM). The accuracies are in the range of 20 cm which are not achievable with satellite imagery of 3-5 meter GSD. Also, DTM would require the satellite to take stereoscopic images to enable the calculations of the height of the area above sea level. Such capability is currently beyond the limiting power and transmission capabilities of the CubeSats even if they would have the required GSD.
They can, however, be used to understand what the current situation is on the ground before beginning the design, which allows a good forecast of what lay ahead in terms of clearing the area, who is using it now, and for what purpose.
Additional use of remote sensing connected to construction is monitoring changes, especially in an urban environment. A municipality can collect tax based on livable area on an individual’s property. The municipality may see an advantage to detecting all the houses which underwent structural changes such as closing a balcony or adding a storage structure on the land near an existing house . These changes can trigger further action by the municipality which would otherwise have gone unnoticed. However, it is doubtful if GSD of 3-5 meter would be sufficient for that mission either.
Additional possible usage of remote sensing images in real estate would be to create a virtual reality video to show a prospect how a certain design would fit the area, or provide an ability to “walk” though the area as if on the ground inside the space. Such capabilities, in order to look real and be believable, would require higher resolution than 3-5 meters and the ability to image existing structures from all sides so that the structure texture would look real on the screen. Currently not part of CubeSat’s capabilities: Will they ever be?
Oil and gas industry: Any benefit from CubeSat?
Oil and gas industries are very much like the construction industry in the planning phase and in the building phase. There is a lot of talk of leak detection using CubeSat. Would that be possible?
Oil and gas are transferred via pipes from the well to the refinery, and from there as final product to port for shipment to other countries. These pipes are laid in corridors which are a few hundred meters wide and hundreds or even thousands kilometers long. The pipes can be above ground or underground. Along the way there are all kinds of pipe facilities such as pumps and other utilities and maintenance infrastructure. Detecting a leak from a pipe requires that the leak will be substantial and create an oil pond large enough to be detected by CubeSat instrument. Such leak is probably way beyond minimal leakage quantity required to be detected. If an unauthorized connection to the pipe is being done in order to steal oil with trucks then such an activity would create a large enough signature to be detected by 3-5 meter GSD instrument.
Since these corridors are protected areas and any excavation needs to be approved beforehand, there is a need to monitor activities around the corridor to detect possible intrusion. Such a monitoring may be of value if it can sample the area many times, say every few hours or so. With a constellation of CubeSats this may be a realistic requirement. However, it will work only in the daytime and not at night since current IR camera technology requires ten times the diameter of the daytime camera if both have the same GSD. Such a large aperture is not possible on CubeSat.
While it is the belief of this author that CubeSat has a role in remote sensing, we need to have realistic expectations. Using CubeSat as penance for all the problems associated with big satellites is wrong and would not serve the CubeSat industry at all. We need to find those missions which are optimal for the CubeSat and push them ahead. Using the right tool for the right problem will only support the development of the CubeSat industry and increase the usage of CubeSats around the world.
CubeSats are cheap and light, so their launching is cheap as well. This enables the structuring to constellations, which provide better coverage and revisit time than any other solution. They would require further miniaturization of power systems and communication systems to improve capabilities, but they would never be able to overcome the basic physical laws governing the remote sensing instruments and which dictates their size and spatial resolutions. The fact that there are very capable people in the world who manage to convince investors to invest more than $60 million US to finance a constellation of some 100 CubeSats for the purpose of providing panchromatic images with a resolution close to 5 meters, points to a business model which this author fails to recognize.