The IFT Committee has identified the following key technology areas which pose challenge to the remote sensing community in the coming years.
1.Reducing size, power requirements, and cost of remote sensing instrumentation to provide more feasible access to space. Applicable technologies include:
2.Developing infrastructure in the rapidly changing world of computing and networking to process and disseminate the terabytes of data per day anticipated from remote sensing platforms.
3.Developing algorithms which fuse different data sources to derive physical parameters needed by the data user community.
Committee members' comments:
On the list you prepared I believe that reducing size, weight, and
and especially COST is one of the most important technology issues
the GRS faces. The other two issues are important and overlap a
lot with the interests of the Data Standards and Distribution
One issue that could be added to the list on #1 would be
the development of commercial applications and products
of remote sensing data to help subsidize the cost of satellite
missions. Some small efforts have been made in this area but
maybe the GRS AdCom should take a good look at this issue
to see what potential is there?
provide input to the formation of data policies
I agree with your list and I would suggest the following challenges closely
related to (but not necessarily only to) the GPR technology:
- Spread the access to remote-sensing data to wider community (e.g.,
broadband internet access);
- Data compression (e.g., handle archived data at different scales or with
different information contents);
- Include more detailed (not highly simplified) physical models into
algorithms used to derive the physical parameters;
- Reduce costs by developing algorithms for automatic processing of large
amount of data (or information) with reduced human interaction.
How about "new system concepts for radars to make them smaller and lighter" as
well as improved hardware. For example MMIC's can be used to reduce the
size/weight/power of microwave sensors too.
1. Reducing size, power reqs., and cost ...
All mentined areas seem reasonable, but it is difficult to put priorities on
the different items. As there are several bottle-necks in a given remote
sensing system, solving one of them immediately means hitting another one.
Deployable large aperture antennas only apply to space based systems whereas
MMICs and digital processors are applicable to all kind of remote sensing
One important parameter which one usually accepts as it is in any system
design is the receiver noise. It immediately imposes several system parameters
such as antenna gain, transmit power, signal integration length, spatial
resolution, etc... Imagine that we could one day reduce the receiver noise
by a factor of 2 or 4, or even more! This would open a completely new system
optimization possibilities (e.g. smaller antenna, lower transmit power,
shorter signal integration, higher spatial resolution, etc...).
Now the question is: "how could we drastically reduce the receiver noise?"
The obvious answer is an efficient receiver cooling. Whatever techniques
which could be developed to achieve an efficient and reliable cooling of
key receiver components/subsystems would be a significant step for generally
improving a large class of microwave instruments. Could the latest high
temperature superconductor technology contribute significantly to such a
reduction of receiver noise? Well, those are rather open questions.
A second area of interest could be the development of fault/failure tolerant
components and subsystems. Current engineering practice uses dedundancies
and vigorous quality assurances to reduce the probability of failure.
Still, there are countless examples of lost or delayed system deployments due
to critical single point failures (e.g. stucked antenna of Galileo, failed
navigation system of Ariane 501, stucked scanning mirror, unstable whisker
contacted mixer, etc...). Those critical components/subsystems should be
designed in the future in such a way that they are by themselves fault/failure
tolerant, without necessarily calling for an internal redundancy. This might
sound like a fairy-tale. But it might worth giving a thought.
2. Developing infrastructure ... + 3. Developing algorithms ...
Since remote sensing is an information technology, I would rather put those
two challenges into one. Your list of items looks rather complete. But I
would like to add the integration of local high resolution models (e.g.
hydrological model, coastal tide and current model, urban micro-climate model,
regional weather model, etc...) in addition to the global climate models.
Indeed, the climate community is a small one, as compared to the rest of the
world which rather needs more detailed information on regional or local
scales. One could expect in the future that those regional/local high
resolution models will be operational into which remote sensing data are
permanently assimilated. There will be no more clear distinction between the
actually measured data and model derived data. The continuous availability
of model derived information, rather than point-wise measured data would allow
users to retrieve information on an arbitrary location at an arbitrary point
in time. Therefore, direct accesses to those model derived information would
be necessary. I remember the time when I was constantly listening to the local
weather radio band of the San Francisco bay area. From the noon on in Summer,
I was in search of good wind for windsurfing. The wind/wave and tide
information was updated every two hours and they were available only at the
locations of buoys. Extrapolating those point-wise information to my favorate
sailing locations was a rather tricky buisiness, and I had to wait for further
two hours if the wind condition was bad everywhere. If a continuous update
of the information was made through running of a regional weather/current/
wave/tidal model, I could have retrieved the most up-to-date (best prediction)
information at any time at any of my favorate sailing spots by just connecting
to say Internet. I could have even known in advance how long the wind will
blow in that afternoon.
Well, I got somehow carried away ...
It is difficult to list exactly what technology items are relevant for such a
scenario to be possible. But your list of technology areas seem reasonable.
Somewhere in your list progress towards very low noise receivers should be included.
This concerns mainly the development of super-conducting (SIS) devices and
receivers in the mm- and particularly in the sub-mm range (airborne and space-borne
So the keywords would be : Low noise receivers using super-conducting SIS
I just returned from the SPIE Aerosense Conference in Orlando on the subject of
Passive Millimeter Wave sensors - This was about the sensors and phenonology for
the applicaitons of all-weather landing systems, military target location, and
contraband weapons search.
In the area of spaceborne passive millimeter wave sensors, I would make the
1) MMIC radiometers at 54, 118, and 183 GHz are being developed under the NASA
IMAS program for nadir sounding of temperature and humidity. The radiometers
are being developed under contracts to TRW and Lockheed Sanders and have
specifications of mass < 2 Kg, power < 5 W, and sensitivity of 0.7K at 20 ms
integration time. A contact at JPL is Todd Gaier at 818-354-4402.
2) The IMAS program is for single-pixel sensors. A much greater advance can be
made by the development of MMIC millimeter wave focal plane arrays which measure
many points in one time interval and can eliminate the need for mechanical
scanning. A focal plane array with 75 pixels in the 100-140 GHz and 170-210
GHz bands was proposed as AMLS (Array Microwave Limb Sounder) to the NASA New
Millenium program. It received high marks but has not been funded at present.
A contact at JPL is Joe Waters at 818-354-3025.
3) Another area needing development is low-cost sensors for the sub-millimeter
range (200 GHz to 2500 GHz). Diode-based integrated circuits (MMIC's) and
micromachined antennas could replace the labor intensive and difficult assembly
of the discrete-diode, precision machined, receivers which are presently in
John P. Kerekes:
Your list of challenges is very good and pretty much complete. I would
add one more aspect to the reducing size, weight, power, etc. and that
is efficient representation of the information in the data. One aspect
of this is data compression, but it also includes application-specific
metrics and the ability to retain all necessary scientific value in the
data. Particularly for EO systems like rapid scanning advanced GOES Imagers,
AIRS, or hyperspectral imagers the data rates are tremendous and will
overwhelm existing infrastructures, but we know significant redundancy exists
in the data; we just don't know how to exploit that in all situations.
I assume this comment is too late for your reply, but I'm sending it
anyway to let you know your messages do get read and there is still
interest out there.