With this in mind, hyperspectral imaging could be seen as the next obvious place for EO to go, with other sensors looking into the middle and long-wave infrared not too far behind.
This op-ed originally appeared in the March 25, 2019 issue of SpaceNews magazine.
The Earth observation sector continues to evolve at a fast pace. Over the last five years, hundreds of millions of dollars have been invested to support the development of new commercial ventures. Most these funds have gone into two camps: low-cost constellations — both optical and synthetic aperture radar — to support data collection at high frequency; and data analytics companies aiming to take this data and develop information solutions.
The notion driving this activity is the anticipated arrival of several constellations offering 1-meter resolution imagery in sufficient abundance to drive down the price point of the data, which can then be leveraged to build analytics as a service. A key premise behind new solutions is the opening of the Earth observation market to more end users across further sectors; the idea of more customers paying for lower-cost solutions that are increasingly responsive to their needs targets an opening up of the business-to-business market. This would expose service providers to a wider audience, often quicker to adapt to new processes, and would scale up procurement if cost-benefit can be demonstrated.
Despite the investment levels in the sector, much still needs to come to fruition. Companies such as Planet and Spire are moving toward the completion of their respective constellations; other companies, however, remain in the building process. This means that analytics companies must continue to wait for new data supply to come online for them to be able to build information solutions. Euroconsult remains positive, anticipating a services market topping $9 billion by 2027 on the assumption that new services will be built targeting communities across sectors such as finance, insurance, retail, location-based services and smart cities, among others.
Assuming the development of new services built from data collected from low-cost constellations, where does Earth observation go from there? Effectively, ground resolution limits for commercial data are being reached (or are at least being deregulated so that high-resolution data from commercial supply becomes permissible); high geolocation accuracy is being achieved on higher-mass satellites; and revisit is being significantly reduced through the constellation approach. The next logical step would therefore seem to be spectral resolution.
In some ways it is surprising that this has taken so long, as the ability to image across the spectrum is one of the fundamentals of satellite-based remote sensing. The U.S. Geological Survey’s workhorse Landsat 7 satellite has eight spectral bands, while its successor and plenty of environment-monitoring satellites have more. Commercial satellites, however, (with a few exceptions) have tended to focus on visible channels — red, green, blue — with perhaps a band or two in the near-infrared (NIR) to support vegetation monitoring and land-use classifications. There are several likely factors why commercial operators have stayed in this area, but the main reasons are the cost of building satellites with more bands and the availability of lower hanging fruit such as the building of applications focused mainly in the visible part of the spectrum. Much can even be done with one panchromatic band in which higher ground resolutions can be achieved.
Looking at what can be done commercially with bands in different parts of the spectrum has always been around, if perhaps a little below the surface. With this in mind, hyperspectral imaging could be seen as the next obvious place for EO to go, with other sensors looking into the middle and long-wave infrared not too far behind. Through extended data collection across the NIR and short-wave infrared (SWIR) in narrow spectral ranges, greater delineation of objects can be achieved based on their spectral signatures. Hyperspectral technology is relatively proven; it is, for instance, already utilized with sensors on board drones to support agriculture applications. Creating an operational satellite system with sufficient ground resolution (less than 10 meters per pixel), good signal-to-noise ratio and decent revisit times, however, has proven to be a challenge. The power requirements to support collection across bandwidth ranges at a sufficient ground resolution would lead to a reduced image swath, thus hampering revisit times from a single satellite. Reducing revisit times requires a constellation, with obvious cost implications. Stable platforms are also needed, meaning it is challenging to base this on a lower-cost small satellite design (although this is being explored by Satellogic). Despite the potential limitations on ground resolution, much detail can be garnered from imagery.
If a ground resolution of sharper than 10 meters and weekly or better revisits can be achieved, then the commercial opportunity for hyperspectral starts to become more interesting. Particularly in the defense realm, there is sensitivity in using hyperspectral data to detect true versus camouflaged objects. Agriculture is also a key area for hyperspectral. Today, satellite-based agriculture applications are based on multispectral solutions with bands (nominally three to five channels) spanning visible red into the NIR (the “red-edge”). By being able to scan the same spectral range in tens or hundreds of bands of more detail, crop health and yield can be assessed. The benefit of having SWIR channels would also support further application development into biophysical and chemical crop properties. The larger markets would be expected to be defense and agriculture, both communities already having some experience in handling hyperspectral data and services through proprietary defense hyperspectral sensors and drone imaging. There is further applicability to serve the forestry, oil and gas, and environment monitoring sectors. Given the complexity in handling hyperspectral data, it is expected that the market will be geared more toward derived services and information products rather than selling data alone. In this regard, there are already a number of specialized geospatial services providers out there that have built expertise in deriving hyperspectral services through the use of aerial data.
The number of satellites carrying a hyperspectral payload is expected to remain limited; most future solutions are from the government. In total, about 15 missions are identified for launch in the next decade. Most missions have a ground resolution of greater than 20 meters, which is less applicable for commercial use; their focus is likely more geared to R&D and scientific usage. There are, however, several commercial solutions gaining some traction. Montreal-based NorthStar Earth & Space plans a 40-satellite constellation to offer daily revisit with an expected ground resolution of better than 10 meters. It recently completed a $52 million financing round (in addition to the $31 million already achieved) which includes contributions from the federal government of Canada and the provincial Quebec government. Further partners include Telesystem, Telespazio and Thales Alenia Space. HyperSat LLC also announced that it has secured an initial $85 million investment from an equity consortium led by Incentrum Group to fund the development of a hyperspectral constellation. The company targets better than 10-meter ground resolution from an initial constellation of six satellites. The first two satellites are expected to be in orbit in 2020. The Satellogic solution targets 30-meter ground resolution hyperspectral data; it announced a $27 million Series B financing round in 2017 with investment led by Tencent. The full cost to develop these systems will not be cheap. These companies are expected to be seeking further financing. However, it should be expected that a hyperspectral solution will emerge over the next five years to push the boundaries of commercial data collection even further.