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General about sampling the upper oceans, where to sample
Ocean models will tie together all types of sampling devices.
Combining models and sensors to generate sensing strategy to acquire desirable data
Data fusion is still under work
Remote sensing will be able to improve the data from AUVs and ASVs
The sensor equipped AUV is able to adapt its behaviour behavior to sample the desired data
Remote sensing depth capabilites capabilities depends on what you are looking at, up to 10s meters
Should use HSI at several altitudes to confirm atmospheric correction algorthimsalgorithms, and other types of corrections.
Difficult to do properly, need to be done carefully
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Don’t think the right Powerpoint was uploaded to the dropbox folder.
A moving platform will mix the space and time dimension, which needs a model to properly separate. This is difficult if not impossible.
Need to carefully plan data sampling schemes to not regret it in hindsight
Discovers unexpected natural phenomena.
Mackerel is one of the most valuable resources in the norwegian Norwegian oceans
Use LIdars for water penetrating observations
Airplanes were expensive, but lidars are now much more commercially available.
Should be used in drones, could be used i future satellites?
We are moving into a regime that is unknown with HSI, which makes it impossible to use a good model to verify, to begin with.
The challenges of observing the ocean is due to the high rate dynamic effects.
Acoustics are the only observations who preserve spatial and temporal considerations.
A lot of data is being used to make and validate models to observe oceanographic events.
Acoustics might be an underused type of sensor in AUVs, Gliders, buoys, etc.
Remote sensing could/should be used for verification and better model development
Difficult to merge data from different types of sensor and sensor schemes.
Validation of models is important.
Satellite observations is important.
Mariusz Grøtte - Detailed Mission design and analysis
30 % of the time it will not be able to harvest enegry, thus energy budget is important
Mission requirements defines several levels of success. See slides
Need to characterize the camera to better know what we can do and see.
Operational requirements using multiple agents is a future
Trade off between spatial resolution and SNR wrt. Focal length
For geometrical data there will be used orbit data, as of now.
We want to be using adaptive sampling techniques in the satellite as well.
Status of the compression pipeline
Idletech has a lot of software used for spectral data earlier, these algorithms will adjusted space use.
Milicia has developed some of it for the HW we are using. (The MEX-algorithm).
Estimated 6 months of development time to make it ready for SmallSat
This is a short time
It is research and will never be complete (TAJ)
Most remote sensing satellites programmes use post-processing, and does not process on board.
Have not set the minimum requirements for processing at this time.
Plan B is to use RAW data and post-processing, if the processing fails
It will be possible to update software on orbit, this is complicated
Bug fixes is complicated, and is not a simple thing to do in space
It needs to be decided how these fixes will be performed.
The range of the sensor has a range of 300 - 1000 nm with less sensitivity at the edges
It should always be under 3 watts power consumption, but need to know more about the compression pipeline (Julian).
Lossless compression will take a lot of time.
Will we be using more energy on compression than we would on down-linking?
Probably not (TAJ)
Current estimate of processing time consumption will be about 2 minutes
We need to tailor our system for the ocean colour community wrt. Type of data
Tradeoff between spatial resolution and swath angles
Possible to get better spatial resolution than Sentinel.
The satellite is not passively stable, and the actuating will be used for stabilizing
Electrical propulsion is cost effective, but not perfectly accurate.
A gimbal may add error
Should do optimization on the camera optics based on SNR.
A lot of what you see is due to atmospheric effects.
Peak power of 30 watts at satellite, 5 watts peak power on Payload
3 minute operation on peak power ideally
Need to position the S-band antenna in a way that makes it possible to down-link
Our Cubesat provider will be doing system requirements in parallel.
This an ambitious project.
Torbjørn Eltoft - Chlorophyll-a estimation from multispectral remote sensing using ML
CIRFA is doing remote sensing of the artics.
Optical image is useful to verify and validate remote sensed scenes.
Oil slick model dynamics development using JPL/NASA UAV-SAR, SAR images
Repeating the basics of oceanographic remote sensing
It is not trivial to validate the results you get from remote sensing instruments.
At 5 nm spectral resolution we might be able to distinguish between different species of algae or phytoplankton.
Most of the pixels you will see from a satellite will be mixed.
If they are different enough we might be able differentiate them
You will have more information using more bands
There a limitations to traditional band based algorithms for extracting chlorophyll content
Machine learning can garner interesting results, perhaps even better results
There are different ways to calculate chlorophyll, and more data will create better results.
Stian Solbø - Arctic and Ocean remote sensing with Satellites, Airplanes and drones
NORUT, using UAVs and satellites to gather data
A single SAR image can give information about wind, sea state, ocean surface state etc.
It will be beneficial to use the same payload in the UAV as well as the satellite
Calibration is important to get useful data for the scientific community
NDVI values are the most important for vegetation measurements, can create 3D maps
Use a commercial airplane to gather data, existing approach. A lot of paperwork
Could put our payload on a high altitude (commercial) plane in an attempt to recreate the condition closer to the conditions we expect to see in space.
Dornier DO-228 could be used to calibrate and validate our SmallSat
NLIVE - web portal to interact with data gathered by NORUT
Should combine HSI, radar and models create high level datasets for Oceanographers
Martin Ludvigsen - Challenges and operations in seabed mapping for marine mining, Arctic andarchaeology exploration
Norway might be blessed with marine minerals
There are no methods for regional mapping for marine mining as of now.
The undersampling is also present when searching for marine mining sites
The presence of the instruments generate a lot of light pollution
Ship wreck detection (and classification?) using HUGIN generating 3D models.
Jon Harr / Eirik Blindheim - Norwegian SmallSat program – status and strategies
Norsk Romsenter - link between government, industry and ESA
The ocean is important for norway (6/7 of norwegian territory is sea)
The norwegian government wants to have Norwegian satellites in space.
Challenges can be counteracted with “new space”-strategies, smallsat approach
Even though they have low life expectancy, some satellites far exceeds early estimates
AISSat and NorSat are new space success examples.
A launch of AISSat 3 now on tuesday! Supposed to make contact 9.00 UTC
Several ground stations used to down-link / communicate with NorSat and AISSat
AISSat and NorSat provide a more complete registration of ship data
The norwegian space center welcomes the HSI SmallSat program
Use AISSat data to improve attitude determination
The main challenges for our project lies in the following
The payload, will it work, will it be able to provide useful data?
Will the ADCS be able to accurately control attitude in a satisfactory manner
Communication will be dependent on a a lot of factors
The data rate will change between every pass
An average data rate is more useful than to say what is possible.
Andreas Nordmo Skauen - FFI cubesat-lab
FFI research areas contain the fields of electronics, rockets and nuclear energy technology. A very multidisciplinary institute.
AISSat program consists of several simple iterations.
Experience show that the performance shown in a datasheet is for ideal situations.
There is a large need for thermal, vibration and vacuum-testing, FFI have experience.
FFI could be a useful asset due to experiences with NorSat and AISSat
Cubsat kits can be useful to “play” around with
GOMspace and other providers have software development kits
There exists 5 day training courses for this (Mariusz was there)
It's difficult to model everything (in 3D and scenarios), should build it and throw it around
Mainly the cables
FFI want to help us
Could integrate our payload in their kit, maybe
FFI/Andreas is interested in having competence similar to GOMspace at NTNU
FFI create realistic test by
Simulating communication loss
Sun lamps (strong lamps)
Thermal chambers, etc.
Ajit Subramaniam - extra on remote sensing
Be careful about what kind of terms you use when you claim what you are able to see
It is important to remember that we are always observing backscattering
Limits and magnitudes will affect the inverse relationship
CDOM will make it difficult to see low concentration of chlorophyll, not trivial
High concentrations of chlorophyll are more trivial to distinguish
Hyperspectral data will provide useful insight not gained from multispectral
Atmospheric correction is complicated
A lot of the signal will be lost due to atmospheric effects i.e. aerosols
By slewing there might be problems with BRDF
Uncertainties at 30 % is not uncommon for oceanographers.
Discussions and next steps
The derivatives will be great as a HSI
Need high SNR
Algorithms only using the slope will not work well in type 1 waters
Multivariate analysis might give great unknown insight
Need to be compared with traditional methods
Who are we producing this data for?
Clarify the objective.
We want to be relevant to the ocean colour community
It is ambitious
We have have have the flexibility to work on different use cases but need something to work towards
Idle tech algorithm will use soft modeling to meet the models of oceanographers
Valuable spatial resolution of oceanographers is anything between [10 … 10 000] meters
SNR will get better by several overpasses, speaks for non-nadir views
Better spatial resolution comes at a cost of the size of the payload
Propulsion is needed for altitude control, if not placed in the correct orbit
There exists polarization filters than can be turned on and off.
Might be useful for atmospheric correction.
Will come at a significant signal strength cost
Saturation is bad, and we will not be able to get good data
Need numbers regarding SNR requirements
What are NASA and other companies referring to when they say SNR
TOA, water reflectance? What wavelengths? All wavelengths?
Second order effects need to be characterized.
It might be valuable to use several cameras
Can replace the slewing
Will cost power consumption
Slewing is not the difficult part
Use different cameras for different wavelengths
During the slew maneuver it is reasonable to expect high accuracy.
Might use mirrors (nah)
Redundancy is a nice thing to have in space.
More cameras is more complex and we should not try to drown ourselves
Could be useful to increase SNR at the cost of spectral resolution
Should consider changing the lens diameter to gain better signal.
Could be done, should be done
Everything should be available at the Dropbox folder as of now