wiki:UserApp/Proba-V

Version 40 (modified by Mehr Mohammad Sachal, on Nov 6, 2018 at 4:12:03 AM) (diff)

--

FUGUYS

Table of Contents

    Error: Page TBR/Delete/FUGUYS does not exist

ProbaV

Table of Contents

    Error: Page TBR/Delete/FUGUYS does not exist

The ‘V’ in its name stands for Vegetation: Proba-V will fly a reduced-mass version of the Vegetation instrument currently on board the Spot satellites to provide a daily overview of global vegetation growth.

PROBA V
No image "Probav-shiny_large0.jpg" attached to File

Bearing a different designation from its predecessors, Proba-V is an operational as well as experimental mission, designed to serve an existing user community.

The aim is to guarantee data continuity for the Vegetation dataset once the current Spot missions end.

Proba-V facts and figures

Launch date:mid-2012
Mass:160 kg
Orbit: Sun-synchronised polar orbit, 820 km, with a 10:30 AM local time at the descending node
Instrument:Newly designed version of the Vegetation instrument flown on the Spot series
Guest technology payloads:Gallium Nitride amplifier incorporated in communication subsystem; Energetic Particle Telescope and one other payload to be decided at a later stage

Further Details

Prime contractor:Qinetiq Space Belgium
Payload developer:OIP Space Systems
Ground Station:Satellite’s mission control centre in Redu, Belgium complemented by a data reception station to be located in the north of Europe.
Launcher:To be decided – designed to be compatible with Vega, Soyuz or Falcon 1E launchers.

PROBA-V (Project for On-Board Autonomy - Vegetation)

The PROBA-V (Vegetation) mission definition is an attempt, spearheaded by ESA and CNES, to accommodate an improved smaller version of the large VGT (Vegetation) optical instrument of SPOT-4 and SPOT-5 mission heritage on a small satellite bus, such as the one of PROBA-2.

As of 2008, small satellite technologies have reached a level of maturity and reliability to be used as a platform for an operational Earth observation mission. Furthermore, advancements in the techniques of detectors, optics fabrication and metrology are considered sufficiently mature to permit the design of a compact multispectral optical instrument.

The C/D Phase started in July 2010. The system CDR (Critical Design Review) took place in the spring of 2011. The acceptance review is planned for Dec. 2011 and the flight acceptance review is planned for the spring of 2012.. ESA is responsible for the overall mission, the technological payloads and for the launcher selection.

Background:

The VGT instruments (VGT1 & VGT2), each with a mass of ~160 kg and fairly large size, have provided the user community with almost daily global observations of continental surfaces at a resolution of 1.15 km on a swath of ~2200 km. The instruments VGT1 on SPOT-4 (launch March 24, 1998) and VGT2 on SPOT-5 (launch May 4, 2002) are quasi similar optical instruments operating in the VNIR (3 bands) and SWIR (1 band) range.

The Vegetation instruments were jointly developed and funded by France, Belgium, Italy, Sweden, and the EC (European Commission). The consortium of CNES, BelSPO (Federal Public Planning Service Science Policy), SNSB (Swedish National Space Board) and VITO (Flemish Institute for Technological Research) is providing the user segment services (data processing, archiving, distribution). Vegetation principally addresses key observations in the following application domains:

  • General land use in relation to vegetation cover and its changes
  • Vegetation behavior to strong meteorological events (severe droughts) and climate changes (long-term behavior of the vegetation cover)
  • Disaster management (detection of fires and surface water bodies)
  • Biophysical parameters for model input devoted to water budgets and primary productivity (agriculture, ecosystem vulnerability, etc.).

As of 2008, a Vegetation archive of 10 years of consistent global data sets has been established permitting researchers access on a long-term basis. The SPOT-5 operational lifetime is estimated to expire in 2012. Pleiades, the next French satellite for Earth Observation, is solely dedicated to high-resolution imaging (on a fairly narrow swath) and will not embark any instrument providing vegetation data.

Since the SPOT series spacecraft will not be continued and the SPOT-5 spacecraft will eventually fail — there is of course a great interest in the EO user community to the Vegetation observation in the context of a smaller mission, affordable to all concerned.

PROBA-V will continue the production of Vegetation products exploiting advanced small satellite technology. However, this implies in particular a redesign of the Vegetation payload into a much smaller unit to be able to accommodate it onto the PROBA bus.

Overview of key requirements of the PROBA-V mission

  • Data and service continuity: filling the gap between SPOT-VGT and the Sentinel-3 mission
  • Spectral and radiometric performance identical to VGT
  • GSD: 1 km mandatory, improved GSD is highly disirable: 300 m (VNIR bands), 600 m (SWIR band). Image quality and geometric accuracy, equal to or better than SPOT-VGT
  • Provision of daily global coverage of the land masses in the latitudes 35º and 75º North and in the latitudes between 35° and 56° South, with a 90% daily coverage of equatorial zones - and 100% two-daily imaging, during day time, of the land masses in the latitudes between 35º North and 35º South..
Artist's view of the PROBA-V spacecraft (image credit: ESA)

No filespec given

An extensive feasibility study and trade-off work was undertaken to identify a solution that could meet not only the technical challenges, but that could also be developed and tested within a tight budget of a small satellite mission.

The PROBA-V project of ESA includes the Space Segment (platform contract award to QinetiQ Space NV of Kruibeke, Belgium - formerly Verhaert), the Mission Control Center (Redu, Belgium) and the User Segment (data processing facility) at VITO NV. VITO (Vlaamse instelling voor technologisch onderzoek - Flemish Institute for Technological Research) is located in northern Belgium. VITO’s processing center of VGT1 and 2 data (SPOT-4 and SPOT-5) is operational since 1999. VITO is also the prime investigator and data service provider of PROBA-V for the user community including product quality control.

Implementation schedule:

  • The Phase B of the project started in January 2009
  • SRR (System Requirements Review) is in Q4 of 2009
  • PDR (Preliminary Design Review) in Q2 of 2010
  • HMA (Heterogeneous Mission Access) and QA4EO (Quality Assurance for Earth Observation) implementation for user data. Planned interoperability with GSCDA V2 (GMES Space Component Data Access Version 2).
PROBA-V project organization (image credit: ESA)

No filespec given

Technologies

Proba-V’s biggest technology challenge has been finding a way to re-engineer the Vegetation instrument to fit onto a Proba platform. While Vegetation is one of the smallest payloads aboard the lorry-sized Spot-5 satellite, it still weighs more than 130 kg – bigger than an entire Proba satellite. Vegetation had to be reduced in volume by a factor of 10. At the same time ESA also sought improved performance, including increased spatial and spectral resolution. To achieve this meant taking advantage of all advances in technology since the instrument was first designed in the early 1990s.

Back then, only a combination of heavy glass lenses could yield Vegetation’s uniquely wide 105° field of view, and separate lenses were required for its four spectral bands. Its sensitive short-wave infrared detectors also demanded a heavy, power-hungry cooling system. To shed mass, glass lenses have been swapped for lighter aluminium mirrors, which have the additional advantage of observing across all spectral bands without the need for duplication.

The challenge has been to produce aluminium mirrors with just the right ‘aspheric’ shape needed for Vegetation’s wide viewing angle. Reducing the size of mirrors needed, the imager has been subdivided into three separate telescopes with overlapping views of 34° each, although even then its mirrors required a manufacturing technique of unprecedented precision known as ‘single-point diamond turning.’

A prototype telescope was developed in 2009 through ESA’s General Support Technology Programme (GSTP), serving to develop hardware to flight readiness. OIP Sensor Systems is overseeing development of the Proba-V imager.

XenICs (BE) is responsible for Vegetation’s short-wave infrared detectors, a particular technical challenge which has been supported through ESA’s GSTP.

To match the Vegetation instrument’s 100° field of view, a total of three very long linear detectors of 1024 pixels each have been designed. These detectors are mechanically butted together to have a slight 80 pixel overlap. The detectors are made from indium gallium arsenide, which delivers high sensitivity without the need for active cooling.

To store all the data produced by the Vegetation instrument, then get it down to Earth, is a challenge in itself for such a small platform. Proba-V incorporates a novel 16 gigabyte onboard storage capacity based on flash memory. It also has a powerful X-band transmitter and utilises advanced compression techniques to downlink data to its data reception station, located in the north of Europe.

Proba-V will carry fewer guest technology payloads than a standard Proba mission, because its main instrument is the mission priority. However it will have an advanced radiation detector called the Energetic Particle Telescope, which will measure the energy, angle and mass of radiation particles in the satellite’s vicinity.

Proba-V’s communication subsystem will also incorporate a gallium nitride amplifier, the first European-sourced GaN device to fly in space. GaN has been called the most promising semiconductor since silicon, capable of operating at higher powers and temperature. It is also inherently radiation resistant. Another guest technology payload slot remains to be allocated.

Platform

Proba-V’s platform – otherwise known as its ‘bus’, the surrounding structure containing all necessary subsystems to let the payload do its work – takes its flight heritage from the Proba series of small satellites. The platform has been demonstrated in orbit by the Proba-1 and Proba-2 missions. The underlying design is recurrent from Proba-2. It is box-shaped, with precise dimensions 765 x 730 x 840 mm3. The platform is based around a H-structure with structural panels made from aluminium and carbon fibre reinforced plastic (CFRP) material. Unlike Proba-2, which has deployable panels, Proba-V will have its solar array mounted onto its satellite body.

The satellite is three-axis stabilised, with an attitude and orbit control system inherited from Proba-2. incorporating a star tracker, GPS receivers, magnetometer and magneto-torquers.

In an significant upgrade from previous Proba missions, the star trackers - as well as the Vegetation instrument payload – have been placed on an optical bench to minimise temperature-driven structural deformation. Navigation and manoeuvres take place on a largely autonomous basis, with minimal input from the ground. The satellite has a design lifetime of 2.5 years, and possesses total system redundancy to make it single-failure tolerant.

Spacecraft:

An industrial team, led by QinetiQ Space NV (Belgium), is supported by several European subcontractors and suppliers, and is responsible for the development of the flight satellite platform, the vegetation payload and the Ground Segment.

The spacecraft bus (fully redundant) is of heritage from the PROBA-1 and PROBA-2 missions (structure, avionics, AOCS, OBS with minor modifications). The PROBA-V spacecraft has a total mass of ~160 kg, and a volume of 80 cm x 80 cm x 100 cm. The three-axis stabilized platform is designed for a mission lifetime of 2.5 years.

The spacecraft resources management is built around ADPMS (Advanced Data and Power Management System), which is currently flying on PROBA-2. The data handling part of ADPMS is partitioned using compact PCI modules. A cold redundant mass memory module of 16 Gbit is foreseen for PROBA-V. The newly developed mass memory will use NAND flash technology.

The power distribution and conditioning part of ADPMS supplies an unregulated bus, with each equipment having its internal DC/DC converter. The power conditioning system is designed around a Li-ion battery.

Overview of PROBA-V subsystems

Overview of PROBA-V subsystems

AvionicsADPMS (cold redundant),MPM (Main Processor Module): LEON2-E Sparc V8 processor, 50 MHz,42 MIPS, 10 FLOPS,Mass memory Module: 16 Gbit Flash, EDAC protectedPROBA-2 ; New development
EPS (Electric Power SubsystemPhoto-Voltaic Array : Triple junction GaAs? cells; Cover glass CMG 100AR coating, 25 strings, 18 cells per string;Battery:12 Ah Li-ion (7s8p) ABSL 18650HC cellsHerschel; PROBA-1
Bus structureAluminum (AA2024-T3);Aluminum (AA7075-T7351);3 CFRP (EX-1515/M55J + Redux 312L) outer panelsNew development
AOCS actuators3 magnetotorquers (internally cold redundant); 4 reaction wheels (3 + 1 for redundancy); 2 magnetometers (cold redundantROBA-2/PROBA-1
AOCS sensors2 star trackers; 2 GPS (cold redundant); AOCS IF box (internally redundant); RW Power Supply box (internally redundant)PROBA-1/-2; New development
Onboard SWOperating System: RTEMS (Real-Time Executive for Multiprocessor Systems)PROBA-2
ThermalPassive (MLI and paint)
RF communicationsS-band TxRx?: 5W BPSK; X-band Tx: 6 W filtered OQPSK; MMU (Mass Memory Unit) = 16 GbitPROBA-1/-2; ; New development
PROBA-V spacecraft accommodation (image credit: QinetiQ Space)

No filespec given

AOCS (Attitude and Orbit Control Subsystem)

AOCS (Attitude and Orbit Control Subsystem) provides three-axis attitude control including high accuracy pointing and maneuvering in different spacecraft attitude modes. The AOCS SW is an extension of the one of PROBA-2, including the following algorithms required by the on-board autonomous mission and payload management:

  • Prediction of land/sea transitions using a land sea mask to reduce the amount of data generated
  • Optimization of attitude in Sun Bathing mode to enhance incoming power while avoiding star tracker blinding
  • Momentum dumping without zero wheel speed crossings during imaging
  • Estimations of remaining spacecraft magnetic dipole to reduce pointing error
  • Autonomous avoidance of star tracker Earth/Sun? blinding
  • Inertial mode with fixed scanning rate for moon calibration.

The AOCS hardware selection for PROBA-V consists of a high accuracy double star tracker head, a set of reaction wheels, magnetotorquers, magnetometers and a GPS receiver.

The main AOCS modes are: Safe, Geodetic, Sun Bathing and Inertial mode.

  • The satellite Safe mode is used to detumble the spacecraft after separation from the launcher and it will be used to recover from spacecraft anomalies.
  • The Geodetic mode is used during nominal observation of the Earth’s vegetation. In this mode the payload is pointed towards the geodetic normal to the Earth’s surface. An extra steering compensation, yaw-steering, is added in this mode, to minimize the image distortion caused by the rotation of the Earth. This yaw-steering maneuver ensures that the spectral imagers are oriented such that the lines of pixels are perpendicular to the ground-trace at each moment. In this mode the star trackers and the GPS receiver are used as sensors and the reaction wheels as actuators.
  • On each orbit, the spacecraft enters the Sun Bathing mode from -56º latitude until entry of eclipse. This is to enhance the incoming power.
  • The Inertial mode coupled with an inertial scanning of the Moon at a fixed rate is used for monthly radiometric full moon instrument calibration purposes. The pointing towards the moon takes 2.5 min, 9 min for scanning the moon and 2.5 min to return to nominal observation mode. It is sufficient to have the moon in the FOV of the SI (Spectral Imager) for a number of pixels.

Beyond the technology demonstration through the PROBA program, it is also noted that the AOCS software technology developed in the course of this program is now the baseline of the AOCS of a major operational mission of the GMES (Global Monitoring for Environment and Security) program: Sentinel-3. NGC Aerospace Ltd (NGC) of Sherbrooke, (Québec), Canada was responsible for the design, implementation and validation of the autonomous GNC (Guidance, Navigation and Control) algorithms implemented as part of the AOCS software of PROBA-1 and PROBA-2. NGC has the same responsibilities for the PROBA-V mission.

EPS (Electric Power Subsystem): The PVA (Photo-Voltaic Array) uses GaAs? triple junction cells with an of efficiency of 28%. To obtain the operating voltage of 31.5 V, 18 cells are included in each string in series with a blocking diode. The PVA consists of a total of 25 solar strings taken into account the loss of one string on the most contributing PVA panel. The average solar string power under EOL conditions (summer solstice and T = 40°C) yields 12.8 W. The maximal incoming power at EOL during an orbit is 144 W. The energy budget for PROBA-V is derived for a bus power consumption of 140 W assuming a worst case day in the summer and while not taken into account the effect of albedo. A worst case power budget analysis indicated a maximum capacity discharge of 1.66 Ah. Use of a Li-ion battery. The battery cells provide a capacity of 1.5 Ah per string. The PROBA-V battery is sized to 12 Ah taking into account capacity fading and loss of a string.

Launch

Launch: A launch of the PROBA-V spacecraft as a secondary payload is planned for Q4 2012. The primary launcher is currently assumed to be Vega with the VESTA adapter.

Orbit: Sun-synchronous orbit, altitude = 820 km, inclination = 98.8º, LTDN (Local Time on Descending Node) = 10:30 hours (with a drift limited between 10:30 and 11:30 AM during the mission lifetime).

RF communications: S-band for TT&C transmissions and low-gain antennas with omni-directional up- and downlink capability. The uplink symbol rate will be fixed at 64 ks/s, while the downlink can be set to a high rate (< 2 Ms/s) for nominal imaging or to a low rate at 329 ks/s for off-nominal conditions. The CCSDS protocol is used for the TT&C transmissions.

X-band downlink of payload data is in X-band at a data rate of 35 Mbit/s. The onboard mass memory is 88 Gbit. The Redu station (Belgium) is being used for TT&C communication services. The X-band uses two cold redundant high-rate X-band transmitters and two nadir pointing isoflux antennas, both RHCP.

The S-band transceivers will be connected to RS422 outputs (cross strapped) of ADPMS while the X-band transmitters (8090 MHz) will be connected to the LVDS outputs not cross-strapped. The X-band link budget results in a link margin of 6 dB which will allow a reduction of the RF output power. Therefore the X-band transmitter will be designed (customer furnished item) to support various output power settings such that after commissioning, a lower output power might be selected.

Data compression: The massive amount of data produced by the instrument is beyond the capabilities of the bandwidth available on board of a small satellite. Data are reduced by using a lossless data compression algorithm implemented in a specific electronics. The data compression ratio obtained using standard CCSDS compression algorithms (CCSDS 133.0 B-1) is shown in Table 2.

Compression ratio

Spectral band
Blue10.8
Red7.2
NIRSWIR 2
Red5.4

Table 2: Overview of compression rates S-band

The selection of an S-band transceiver and the development of an innovative and generic X-band transmitter for small satellites has been initiated in a collaborative program between CNES and ESA and is funded under GSTP-5 (General Support Technology Program-5). The X-band transmitter is a high-performance device optimized for the needs and constraints of small platforms for which small volume, low mass, low power consumption, and low cost cost are important parameters. Moreover, some key features such as modulation (filtered Offset-QSK), coding scheme (convolutional 7 ½), data and clock interfaces (LVDS packet wire serial interface) have been selected in compliance with CCSDS recommendations, but also to ease the interoperability with most of the existing on-board computers and ground station demodulators.

X-band

The development of the new X-band transmitter is based almost exclusively on COTS components to achieve at the same time high performances and low recurrent cost. The transmitter also features an innovative functionality with an on-board programmable RF output power from 1-10 W which allows to match finely with the chosen bit rate, and to reduce as much as possible the margins of the link budget and therefore the consumption power. PROBA-V is the first mission to use this newly developed transmitter. The transmitter has a mass of 1 kg, a size of 160 mm x 115 mm x 46 mm, an in-orbit life time of 5 years, and a radiation hardness of 10 krad. Data rates from 10-100 Mbit/s are available. The X-band transmitter was manufactured by TES Electonic Solutions of Bruz, France.

Overview of the transmitter architecture (CNES, TES)

No filespec given

Photo of the X-band transmitter (image credit: CNES, ESA)

No filespec given

Sensor complement: (VGT-P)

The PROBA-Vegetation payload is a multispectral spectrometer with 4 spectral bands and with a very large swath of 2285 km to guarantee daily coverage above 35 latitude. The payload consists of 3 identical SI (Spectral Imagers), each with a very compact TMA telescope. Each TMA, having a FOV of 34º, contains 4 spectral bands: 3 bands in the visible range and one band in the SWIR spectral range.

VGT-P is restricted to imaging land and dedicated calibration zones. On-board the spacecraft there is for each spectral imager a land sea mask that is provided by the PI (Principal Investigator). The land sea mask removes the pixels that contain only sea and it dictates when each SI should be in imaging mode.

OIP (Optronic Instruments & Products, Belgium) is the industrial prime contractor for the payload and is responsible for the design and development of the PROBA-V instrument and AMOS (Belgium) is responsible for the manufacturing and alignment of the telescope. The major payload challenge lies in the fact that the wide-swath imaging instrument has to fit into a small satellite with limited resources. The TMAs and the SWIR FPA have to be developed for the VGT-P since no COTS products are available

Each SI (Spectral Imager) contains one telescope, a beam splitter to separate the VNIR from the SWIR spectral bands, spectral bandpass filters to select the spectral bands, and the VNIR and SWIR focal plane arrays. The spectral bands will be realized by spectral bandpass filters centered on 460, 658, 834 and 1610 nm, with bandwidths of respectively 42, 82, 121 and 80 nm. The filters will be applied on the detector windows.

The optical axis of the central telescope will point to nadir and the two outer telescopes will point 34º from nadir. Together the three TMAs will cover a complete FOV of 102º. The optical system is telecentric, and the aperture is located at the position of the second (spherical) mirror.

Conceptual accommodation of the VGT-P inside the PROBA-V spacecraft (image credit: OIP, ESA)

No filespec given

Figure 6 shows the payload mounted on the PROBA-V platform. Given the reduced size of the platform, a H-shape structure, the only practical location of the payload is on the anti-velocity panel. This accommodation, with respect to a solution with the payload in the middle of the structure, has the advantage of a very simple assembly and clean mechanical interface. The drawback is a larger temperature gradient due to the close vicinity of the payload to the solar panel.

Block diagram of the VGT-P (image credit: OIP)

No filespec given

Legend to Figure 7:

  • ROE (Read Out Electronics)
  • PSU (Power Supply Unit)
  • DHU (Data Handling Unit)
  • PEU (Peripheral Electronics Unit)
  • MLI (Multi-Layered Insulation)

TMA telescope development

TMA telescope development: VGT-P makes use of a set of three such telescopes, identical to each other. The purpose of the related ESA GSTP (General Support Technology Program) development is to demonstrate the feasibility of one item of the set with respect to its required optical quality, and to secure the instrument development. The entire telescope is an athermal design made of the same aluminium material. The mirrors quality is achieved by SPDT and the alignment rely on the very precise matching of the mirrors with the mounting structure.

Taking into account the mission constraints and objectives, including the innovative features of the instrument, a full-aluminum design was selected. This choice allows taking benefit from the recent developments in ultra-precision milling and turning techniques, as well as in optical aluminum production. Furthermore, this leads to a homothetic telescope behavior. The optical performance requirement of the telescope with regard to MTF (Modulation Transfer Function) is given in Table 4.

SPDT (Single Point Diamond Turning): Diamond turning is a process of mechanical machining of precision elements using Computer Numerical Control (CNC) lathes equipped with natural or synthetic diamond-tipped cutting elements. The SPDT process is widely used to manufacture high-quality aspheric optical elements from crystals, metals, acrylic, and other materials. Optical elements produced by the means of diamond turning are used in optical assemblies in telescopes, scientic research instruments and numerous other systems and divices. Diamond turning is specifically useful when cutting materials that feature aspheric shapes such as TMA surfaces.

Performance requirements of MTF
BandNominal MTF (%) MTF (%)Max. frequency (lp/mm)
Blue68.15338.5
Red68.55438.5
NIR6853.738.5
SWIR7162.420.0
Optical design concept of the TMA (ray tracing diagram), image credit: OIP

No filespec given

Baffle design (Ref. 20): The aim of the baffle design is to block the out-of-field light which could enter the instrument and reach the detector, directly or through one or several reflections on the mirrors. This 1st order analysis didn’t consider vanes on the baffles and diffusion on M1 of out-of-field light.

The preliminary baffle layout is presented in Figure 9. It comprises 7 baffles: 1 at the entrance aperture of the instrument and 6 placed inside the instrument. An aperture stop is also placed at the level of the secondary mirror.

The baffle #1 is placed at the entrance of the instrument. Its role is to limit the out-of-field light that could directly reach the mirrors. The combination of the baffles #1 and #2 stops the direct view of the M3 mirror through the instrument entrance. The length of the upper side of the entrance baffle is defined to stop the light which could directly reach the M3 mirror and that could not be stopped by the lower side of the entrance baffle and by baffle #2. Some out-of-field light can also reach the M2 and M3 mirrors after reflecting on M1. This cannot be totally avoided but the length of the lower side of baffle #1 has been chosen in such a way that this straylight is stopped by the baffle #3 after reflecting on M3. The baffle #3 is placed below the M2 mirror and stops the direct view of the M1 mirror by the VNIR detector. The baffle #4 is a critical location where reflection or diffusion on the M2 structure can occur and bring stray light to the VNIR detector which is very close. Vanes will be placed at this location. The baffles #5 and #6 are placed near the focal planes to isolate the detectors from each other. The baffle #7 avoids a direct view to the SWIR detector from the M1 or M3 mirrors.

Proba-V TMA preliminary baffles layout (image credit: CSL, OIP, ESA/ESTEC)

No filespec given

Figure 10: Illustration of the optical assembly of VGT-P and two star trackers on the optical bench (image credit: OIP)

No filespec given

SWIR detector development: This development concerns the large format SWIR focal plane array containing at least 2704 pixels with 25 µm pitch. The solution selected uses the mechanical butting technique with 3 overlapping detectors of 1024 pixels and approximately 80 pixels in the overlap area. In Figure 11 the linear detector arrays are shown in green, while the ROICs (Readout Integrated Circuits) are presented in red. Xenics NV of Leuven, Belgium, is developing the InGaAs? SWIR detector array.

Several techniques were evaluated to realize the required alignment accuracy of the 3 PDA (Photo Diode Array) subarrays in the FPA. The requested alignment accuracies are:

  • In plane alignment accuracy, ?X and ?Y = ± 12.5 µm
  • Out of plane alignment accuracy, ?Z = ± 50.0 µm
  • Subarray PDA separation = < 1.5 mm.
Schematic view of of the mechanically butted SWIR detector array (image credit: OIP, Xenics)

No filespec given

Figure 12: Drawing of the subarray alignment tools with the 3 PDAs (green) mounted on the mount (image credit: OIP, Xenics)

No filespec given

Photo of the fully assembled FPA in its package (image credit: OIP, Xenics)

No filespec given

Thermal design of the VGT-P instrument:

One of the major drawbacks of using multiple optical systems in parallel while imaging, is the effect of pointing inaccuracies due to thermo-elastic and mechanical deformations. It is obvious that such pointing errors can easily destroy the quality of the images. For the VGT-P, the stringent geo-location requirements demand the instrument to be thermally stabilized as much as possible to reduce any thermo-elastic disturbances.

Since the PROBA platform is fairly limited in the delivery of power, VGT-P needs to be very efficient in its power use. As a direct consequence, there is no possibility to have an active thermal control system to stabilize the instrument. The thermal design of the instrument must therefore be very carefully assessed and engineered.

Thermal isolation:

Firstly, as the surrounding satellite panels are heavily fluctuating in temperature during the orbit, it is of the utmost importance to shield the instrument thermally from these platform variations. To reduce the radiative heat loads from the environment, the instrument is completely wrapped in a 12 layer MLI. To reduce the conductive heat loads from the mounting plane, the instrument is mounted by means of titanium quasi isostatic mounting feet. These quasi isostatic mounting feet also play a major role in the transfer of the thermo-mechanical deformations from the underlying platform to the optical bench as they strongly reduce these deformations. Therefore, these titanium flexures as they are called not only serve as a thermal isolation, but also acts as a thermo-elastic isolator.

Power reduction: A natural step to reduce the thermo-elastic effects on the instrument is to reduce as much as possible the heat load on the optomechanics. Therefore, all non critical and heavy heat dissipating detector read-out electronics are separated from the optics. The FPAs of the telescope only contain the detector and electronic components which drive the radiometric performances of the instrument. These FPA electronics are connected through a flex rigid to the ROE (Read-Out Electronics) which is thermally and structurally disconnected from the optomechanics. All major heat dissipating components are located in there.

Obiously, also the central electronics (DHU and PSU) are separated from the optomechanical imaging system. By doing this, the total power dissipation on the optical bench is only 9W, which is less than ¼ of the total power dissipation of the complete VTG-P instrument.

Heat dissipation:

To dissipate this heat load, a radiator is needed. Several concepts were proposed and analyzed. The most efficient radiators point towards deep space which would enable us to cool down the complete instrument to very cold temperatures. This had a drawback that additional heaters would have been needed to stabilize the thermal regime of the instrument to normal working temperature. Moreover, as the instrument is always pointing downwards towards Earth, the radiator would have been located on the side of the instrument which naturally induces an asymmetry in the optomechanics. Such asymmetry is not desired in an imaging sensor with stringent pointing requirements. Moreover, heat pipes would have been mandatory to extract as efficient as possible all heat of the detectors towards the radiator which unnecessarily complicated the complete design.

From a thermo-elastic point of view, it was highly desirable to respect the symmetry of the instrument as much as possible and to symmetrically extract the heat from the FPA’s on the optical bench. Thus, it was chosen to locate the radiator in front of the instrument and point it towards the earth surface. As the earth is thermally quite stable at a fairly modest temperature and as the payload is always pointed nadir, this is the perfect heat drain for the instrument. The implementation of this concept reduces the complexity dramatically: the radiator, covered with aluminized Teflon, is connected through two thermal straps towards the front of the instrument without the need to install heat pipes.

Stability:

Stability is the key aspect of thermo-elastic performance. Of course, without the possibility of an active thermal control system, stability is quite difficult to achieve in a thermal environment which is constantly varying over the orbit.

To tackle this problem, the first stage was to avoid the randomness in the heat loads on the instrument and to have constant thermal regime along the orbit. As the payload is encircling the Earth with its radiator pointing at nadir, the heat load on the radiator is subjected to a varying regime from sunlit to eclipse and back. From the point of view of efficient power use, the imaging circuits on the instrument are switched off by the satellite if no imaging is needed (over the oceans, over the poles, during eclipse). This would induce different thermal regimes from one orbit to the other, which is not acceptable from pointing point of view. But leaving all electronics switched on during non operation is a no go considering the lack of power. As a compromise, during sunlit conditions and when the imaging electronics is powered off, a heater located on the detector with a heat load equal to the heat load of the detector and FPA is powered. In this way, the heat loads on the optical system remain constant during sunlit. During eclipse, all is switched off. - As a consequence, a constant thermal regime on the optics is established: during 1/3 of the orbit (eclipse) the radiator faces only IR and the instrument is switched off. During the 2/3 of the orbit, the radiator sees IR and albedo and the instrument is switched on.

Gradients:

The final challenge in the thermal design is to avoid thermal gradients in the instrument as gradients are hard to control and can severely affect the thermo-elastic performance. As already described, the heat extraction has respected the symmetry of the instrument. An unavoidable asymmetry is the location of the Star Trackers as they have their own limitations. The heat load from the FPA and the detectors on the telescopes is normally entering the instrument through the TMAs to the top skin of the optical bench. However, this would heavily distort and bend the optical bench as the top skin will expand more than the bottom. To reduce this effect, thermal straps are designed to extract most of the heat (4/5) from the detectors and the FPA towards the optical bench, the rest is still entering the TMA structure. To reduce the thermal bending, the heat straps are mounted on the side of the optical bench to avoid the bending of the bench.

For further info File:ProbaV Auto6.jpeg

PROBA-V PERFORMANCE ASSESSMENT

ABSTRACT

The Belgian Federal Science Policy Office (BELSPO) has initiated a Preparatory Evalua-tion/Validation Programme for the products of the new PROBA-V satellite to be launched in 2012. The satellite will allow daily monitoring of terrestrial vegetation cover through remote sensing, and will cover the data provision gap between the closure of the SPOT/VEGETATION Programme and the launch of the SENTINEL-3 mission. The aim of this study is to evaluate the improvements that PROBA-V will bring along for forest monitoring in the Atlantic Biogeographical Region of Europe, and lies within the objectives of the FM@PROBA-V project. A representative site in Northern Por-tugal is selected for this reason. VEGETATION, LANDSAT-ÔÌ5, and MODIS data along with the JRC Forest Cover Map are used to train the classifiers, simulate PROBA-V data, apply the classi-fiers at 250 m, 1/3 of a km, and 1 km pixels, and validate the results, while quantifying the accura-cies. Maximum Likelihood (ML), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) methods were tested. From the confusion matrices the best result is obtained by MODIS 2 bands with ANN classifier. Further analysis on the base of those confusion matrices will be applied to define the best classifier taking into account all the parameters of the matrices. The best per-forming classifier will then be recommended to examine its robustness against sudden disastrous events, like fire, in the same area, performing change detection between sequential dates (before and after the event). The performance of the data and classifiers are demonstrated, and the pre-liminary results are discussed.The Belgian Federal Science Policy Office (BELSPO) has initiated a Preparatory Evalua-tion/Validation Programme for the products of the new PROBA-V satellite to be launched in 2012. The satellite will allow daily monitoring of terrestrial vegetation cover through remote sensing, and will cover the data provision gap between the closure of the SPOT/VEGETATION Programme and the launch of the SENTINEL-3 mission. The aim of this study is to evaluate the improvements that PROBA-V will bring along for forest monitoring in the Atlantic Biogeographical Region of Europe, and lies within the objectives of the FM@PROBA-V project. A representative site in Northern Por-tugal is selected for this reason. VEGETATION, LANDSAT-ÔÌ5, and MODIS data along with the JRC Forest Cover Map are used to train the classifiers, simulate PROBA-V data, apply the classi-fiers at 250 m, 1/3 of a km, and 1 km pixels, and validate the results, while quantifying the accura-cies. Maximum Likelihood (ML), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) methods were tested. From the confusion matrices the best result is obtained by MODIS 2 bands with ANN classifier. Further analysis on the base of those confusion matrices will be applied to define the best classifier taking into account all the parameters of the matrices. The best per-forming classifier will then be recommended to examine its robustness against sudden disastrous events, like fire, in the same area, performing change detection between sequential dates (before and after the event). The performance of the data and classifiers are demonstrated, and the pre-liminary results are discussed.

INTRODUCTION

World forests cover roughly 31% of land area and in the last decade it has been reduced at a rate of 13 million hectares per year (1). According to the global forest resources assessment 2010 of the Food and Agriculture Organization, significant progress has been made towards reversing the overall trend of forest area loss and a positive trend over time has been shown in some countries and regions such as in Europe where the forest area continued to expand.

Considerable efforts are needed to improve or at least maintain this positive trend. The forest monitoring gives crucial data to decision makers helping them allocate appropriate financial re-sources for effective forest conservation and management plans. One of these crucial data is For-est Area, which has been selected as one of the 60 indicators for monitoring progress towards the Millennium Development Goals, the 2010 Biodiversity Target and the Global Objectives on For-ests. Forest Area and its changes are relatively easy to measure, especially nowadays with the technological progress made in remote sensing fields.

The available high temporal resolution images allow operational and near real-time applications at global, continental and regional scales for forest area mapping and monitoring. The VEGETATION instruments, which were developed with the objective to provide data specifically for vegetation canopy monitoring, are on board two SPOT5 satellites and will be available until 2013. The instru-ments of this programme (VEGETATION 1 and VEGETATION 2) have monitored and mapped the worldwide vegetation on a daily basis for more than 12 years now. They provide essential infor-mation on terrestrial vegetation cover for a large community of users. Therefore, to ensure the continuity of the data after 2012, the SENTINEL mission, which will be launched at the earliest at the end of 2013 (2), will provide data which will fulfil the needs of the current VEGETATION data users. To fill the data gap between VEGETATION-2 and SENTINEL-3, the Belgian Federal Sci-ence Policy Office (BELSPO) has decided to build a satellite mission called PROBA-V which is expected to meet all the specifications of the vegetation user community (2). PROBA-V will have an increased spatial resolution in comparison to the VEGETATION instrument, an enhancement expected to provide new opportunities for forest monitoring.

The aim of this study is to evaluate forest cover classification methods on the European Atlantic biogeographical region by applying the Maximum Likelihood (ML), Artificial Neural Network (ANN) and Support Vector Machine (SVM) classification algorithms on simulated PROBA-V, VEGETATION and MODIS data, in order to determine the optimal classification methodology for the region. This work is carried out within the framework of the FM@PROBA-V project1 towards evaluation and quantification of the improvements of the products for forest monitoring offered by PROBA-V, in relation to its predecessor, VEGETATION. The preliminary results of the study are presented and discussed in this paper.

Study area

The area of interest is the southern part of the Atlantic biogeographical region of Europe (Figure 1) which is located in the north of Portugal. The region is one of the rare mountainous areas in this region, and mountains in the area can reach an altitude of 1500 m. Forest cover is sparse and agriculture is not very intensive in the region

Methods

Dataset and preprocessing

The data used in this study included the images used for the evaluation process of the forest cover mapping in the European Atlantic biogeographical region and the data used to create the refer-ence data for the classifications. The reference data originated from the classification of a Landsat TM image, acquired on 11 July 2009, using training samples and reference points identified from the JRC Forest Cover Map 2006 and Google Earth images. A small subset (20 „e 20 km2) of that scene was used to simulate a part of a PROBA-V scene, a process undertaken by VITO NV, Bel-gium. The datasets used to evaluate classification algorithms included the simulated PROBA-V image (Red, NIR, approximated SWIR), a 10-day synthesis VEGETATION (VGT) image at 1000 m spatial resolution with 3 bands (Red, NIR, SWIR), and an 8-day synthesis MODIS image at 250 m spatial resolution (again Red, NIR, resampled/ upgraded SWIR). The simulated PROBA-V subset was relatively small (20 „e 20 km2) and the results were expected to be of questionable statistical significance. Hence the MODIS image was additionally evaluated, as its similar charac-teristics with PROBA-V data (Table 1) would allow better evaluation of the potential of the upcom-ing sensor, and its comparison with VGT data. Since only the red and NIR channels are available at 250 m resolution, the SWIR 500 m channel was resampled to 250 m and two MODIS datasets were evaluated, with and without the SWIR data. All images, including the JRC forest cover map where registered to the Landsat TM image in UTM WGS84, Zone 29N projection, WGS84 datum. The extent of MODIS and VGT images was reduced to a subset matching the extent of Landsat TM, which was topographically normalized with the ASTER GDEM.

Image data characteristics
VEGETATION II Modis Proba V
Blue
0.43 – 0.47 0.45 - 0.47 0.44 – 0.48
Red
0.61 – 0.68 0.62 – 0.67 0.62 – 0.69
NIR
0.78 – 0.89 0.84 - 087 0.79 – 0.90
SWIR
1.58 – 1.75 1.62 –1.65 1.56 – 1.65
Spatial Resolution
1.15 km 250m (R, NIR), 500m (SWIR) 300m (VNIR), 600m (SWIR)

Generation of reference data

High resolution reference data were necessary for this study, in order to train and validate the classi-fication algorithms. However, the only available European-wide data, was the JRC Forest Cover Map, compiled in 2006. Since our datasets were collected in 2009, the Landsat TM data, acquired in 2009, were classified in order to produce a stocked/non-stocked classification map, which would be used to evaluate the classification algorithms in the medium/low resolution datasets.

Generation of reference data

High resolution reference data were necessary for this study, in order to train and validate the classi-fication algorithms. However, the only available European-wide data, was the JRC Forest Cover Map, compiled in 2006. Since our datasets were collected in 2009, the Landsat TM data, acquired in 2009, were classified in order to produce a stocked/non-stocked classification map, which would be used to evaluate the classification algorithms in the medium/low resolution datasets.

For the classification of Landsat TM in stocked and non-stocked areas, a set of 400 points was generated by stratified random sampling, following the estimation of the extent of the two classes through an unsupervised classification. These points were used as training samples, and were identified as stocked or non-stocked by using the JRC Forest Cover Map and visual interpretation of Google Earth images. (Figure 2). A radius of two pixels was considered around each point in order to identify it as stocked or non-stocked. A second set of 300 points was generated using the same method, to be used for the validation of the classification. A Maximum Likelihood supervised classification was performed on three different band combina-tions: 6 bands (excluding the thermal band), 4 bands (Blue, Red, NIR and SWIR), and 4 bands + NDVI generated from the Landsat TM data, in order to find the best result. The same training samples were used for the three band combinations. The four classes defined in these classifications were "stocked areas", "non-stocked areas", “water” and "No data". The confusion matrices, for the three classification’s results, were performed using the same 300 validation points.

Forest cover reference maps creation

The classification map with the higher accuracy was resampled to a 10 m resolution, using nearest neighbour,and the “no data” and “water” classes were masked out, leaving the two main classes, “stocked” and “non-stocked” with values of 1 and 0, respectively.

In order to create reference maps with the same resolution as the medium/low resolution images, the 10 m resolution map was aggregated to 250, 300 and 1000 m resolution. The new value of each aggregated pixel was the percentage of the original “stocked” pixels that were aggregated to the lower resolution pixel. According to the pixel values obtained after aggregation, each map was reclassified to five classes according to the percentage of forest cover (0 – 10%, 11% - 30%, 31% - 50%, 51% - 75%, 76% - 100%), see Figure 3. Those classes were chosen in order to ensure that the entire range of forest cover fractions would be represented within the broad “stocked” and “non-stocked” classes.

The Maximum Likelihood supervised classification on the three different combinations of Landsat TM bands produced the highest overall accuracy (93.12%) and Kappa Coefficient (0.904) when all six bands (excluding the thermal) were used, as shown in Table 2. The resulting stocked/non-stocked map was used as a reference map for the study.

Results of the accuracy assessment of Landsat TM classification

TM 6 bandsTM 4 bandsTM4 + NDVI
Overall accuracy
93.1214% 92.9878% 92.4135%
Kappa coefficient
0.9045 0.9027 0.8947

Evaluation of classification algorithms

The produced reference data were used to generate training samples for the classifications and the validation points for the confusion matrices. Training samples were generated for the five clas-ses using stratified random sampling method and subsequently the regions of interests were merged in order to create the two main classes, Non-stocked (0% - 10% and 11% - 30%) and Stocked (31% - 50%, 51% - 75%, and 76% - 100%).These training samples were used to perform the Maximum Likelihood (ML), Support Vector Machine (SVM) and Artificial Neural Network (ANN) supervised classifications on the two- and three-band MODIS, VGT and PROBA-V datasets. Con-fusion matrices were generated for all classification results using the LandsatTM-derived 250m, 300m and 1000m reference maps , respectively for MODIS, PROBA-V and VGT classifications.

Results

RESULTS The results of the confusion matrices are presented in Figure 4. It appeared that the two-band 250 m MODIS dataset produced more accurate classifications with all classification methods, in comparison with the three-band dataset, which employed the SWIR data resampled from 500 to 250 metres. Comparison between the classification methods for all datasets shows that the ANN classification gives a slightly higher overall accuracy than the other methods for the two- and three-band MODIS data, as well as the simulated PROBA-V, while SVM gives the highest overall accuracy for VGT. However the differences between the different classifiers for each dataset are very small and could be considered to be of low significance. According to these preliminary results, the best accuracy and kappa coefficients were achieved by the ANN classification on MODIS image with two bands at 250 m spatial resolution and the lowest overall accuracy was performed by ML classification on simulated PROBA-V image .

In addition to the classification accuracy the percentage of stocked area distribution in each map was also calculated . For MODIS and VGT data, the estimated forest cover was overestimated significantly, from 27% which was the estimated forest cover using the Landsat TM data, to 41 to 48%. On the contrary forest cover estimation with PROBA-V, using the small subset of the scene, for which the simulated PROBA-V data were available, showed significant differ-ences between the classifiers. The Landsat TM data produced an approximate 70% forest cover, while Maximum Likelihood underestimated the forest cover (57.55%) and MODIS and VGT over-estimated the forest cover (80.72 and 79.39% respectively).

Figure 4: Overall accuracy and Kappa Coefficients or ML, ANN and SVM classification result on MODIS, VGT and PROBA-V (B: bands)

No filespec given

Figure 5: Class distribution of the different classification results comparing to Landsat TM refer-ence (B: bands).

No filespec given

Figure 6: Classes distribution of simulated PROBA-V classification compared to the subset of the Landsat reference map.

No filespec given

Discussion and Conclusions

The error matrix is the most common method to evaluate classification accuracy (4), and it is the starting point for many analysis techniques (4,5). In addition to the overall accuracy, the entire confusion matrix was considered through the use of the kappa statistic. These analyses were used to allow a comparison between classification results.

Forest cover in the study area showed significant fragmentation, which tends to pose problems when classifying satellite images (6). This fact proved to be detrimental to the performance of cer-tain datasets. The main aim of this study was to compare the potential performance of PROBA-V in estimating forest cover, with that of VEGETATION (VGT). Because of the small extent of the simulated PROBA-V scene, 250 m MODIS data were also evaluated as an alternative sensor with comparable spatial resolution, which could deliver more statistically significant results. The lack of 250 m SWIR data (the native 250 m product contains data only in the Red and NIR channels) forced the resampling of the 500 m SWIR data to 250 m. Comparison between the classification accuracies between the three-band (with the resampled SWIR data) and the two-band (without the SWIR data) MODIS data showed that the latter achieves more accurate classifications. Spectral variation of the SWIR signal within the 500 m pixel could not be retrieved with the resampling pro-cess and, as a result, the combination of accurate 250 m Red and NIR data with “false” SWIR data led to considerable misclassifications between stocked and non-stocked areas On the contra-ry, the use of just the Red and NIR data produced more accurate classifications.

Comparison between classifications of the two-band MODIS data and the VGT data, revealed that the former again produced more accurate classifications. The availability of SWIR data in the VGT dataset could not assist the classification sufficiently in order to make up for the increased uncer-tainty brought about by the 1 km spatial resolution of the data. As expected, extraction of forest cover information in a fragmented landscape using low resolution data, proves to be quite prob-lematic at a local to regional scale (4).

The simulated PROBA-V data covered only a 20 „e 20 km2 area and consisted of approximately 3600 pixels. From a statistical point of view it is very difficult to extract statistically significant re-sults from such a small sample size, which is the reason MODIS data were also used in this study. Nevertheless, the classification accuracy of PROBA-V data was very similar to the one produced by VEGETATION. On the other hand, the low kappa values of the classifications are caused by the amplification of the errors in the confusion matrix, brought about the small sample size.

The accuracies of the classifications produced by the different classification algorithms on the same dataset showed that there is effectively no difference in their performance on all three da-tasets. The only exception was Maximum Likelihood classifier on the simulated PROBA-V data, which was by 5% less accurate in comparison with ANN and SVM, a fact probably attributed to the quality of the simulated PROBA-V data The Artificial Neural Network classification provides the most accurate classifications and appears to deal best with the mixture of different cover types within each coarse resolution pixel (7) and the fragmentation of the landscape (8).

Non-thematic errors may be one major problem in the use of confusion matrix and associated ac-curacy analysis (9). This is particularly true for errors of image misregistration, since the images were registered to an image of a higher spatial resolution. The methodology used of aggregation and forest coverage percentage class was used to minimize these errors.

In conclusion, comparison between classification algorithms on MODIS and VGT data, showed no significant differences between them, in terms of classification accuracy. The higher accuracy achieved by those classifiers when using the MODIS data, which have comparable spatial resolu-tion with PROBA-V, compared to that achieved with the VGT data, suggest that the increased spa-tial resolution will provide more accurate forest cover mapping, particularly in areas with fragment-ed forest coverage. The usefulness of SWIR data could not be evaluated, but it could prove useful in discriminating between forest and agricultural land, and assist even further the accurate map-ping of forest cover. More research on this topic is recommended in order to quantify the useful-ness of SWIR data at a 300 m resolution.

User Segment

The Proba-V user segment, responsible for the processing of the Vegetation instrument data and for the distribution of the resulting information products to users worldwide, is under the Primeship of VITO (Belgium). The user segment development is a complete new development started at the same time as the flight segment. It will be hosted in Mol (Belgium) where the present SPOT/Vegetation data processing and distribution facility is located.

From 2012 the Proba-V user segment will take over the distribution of the SPOT/Vegetation products.

PROBA missions

The following table provides an overview of existing PROBA missions performed by ESA.

PROBA Missions overview Proba II Proba V
Launch 11/2009 Planned in 2012
Mass 130 Kg160 Kg
Size600 x 700 x 850 mm800 x 800 x 1000 mm
OrbitAltitude between 700 km and 800 km, Sun-synchronous, Inclination 98.298 degreesSun-synchronised polar orbit, 820 km, with a 10:30 AM local time at the descending node
LauncherRockotTo be decided – designed to be compatible with Vega, Soyuz or Falcon 1E launchers.
Power consumption53–86 Watts131.2 Watts
RFS-band, 64 kbit/s uplink; 1 Mbit/s downlinkS-band (TxRx:) 5W BPSK; X-band Tx: 6 W filtered OQPSK; MMU= 16 Gbit
Nominal Life2 years2.5 years
Ground StationRedu (Belgium)Satellite’s mission control centre in Redu, Belgium
Developed byConsortium led by QinetiQ Space nv of BelgiumOIP Space Systems

Proba II

Proba V mission is based on Proba II, here you will find a little bit of information about Proba 2

PROBA 2 Mission Summary

Following on from the success of PROBA-1, which successfully completed its technological goals in its first year of flight and continues to provide valuable scientific data now into its fifth operational year, PROBA-2, now in phase C/D and due for launch in September 2007, will once again fly a suite of new technology demonstrators with an ‘added value’ science package of four experiments. Altogether there are seventeen new developments being flown on Proba-2, divided into two groups: platform technologies which are part of the infrastructure and are mission critical and passenger technologies to gain flight heritage and experience before committing them to the infrastructure of other missions. Of the four Science experiments, two are dedicated to solar physics. The two other will study the space weather (plasma physics) The paper will provide an overview of the PROBA-2 mission and spacecraft along with a description of the scientific payload and technology experiments

No image "Proba-2-in-orbit-rear-view.jpg 400px" attached to File

1.1. Mission objectives

The PROBA 2 mission objectives, as deduced from the ESA requirements, can be summarized as follows:

  • PROBA 2 will be a platform to demonstrate and validate new, advanced technologies in order to promote their usage in future missions,
  • As such, PROBA 2 shall accommodate a number of selected technology experiments,
  • PROBA 2 shall furthermore accommodate a series of scientific payloads, in the fields of space environment (plasma) and solar observations;
  • The PROBA 2 system shall be designed to support an in-orbit operational lifetime of 2 years;
  • The PROBA 2 orbit shall be preferably a LEO Sun-synchronous orbit with minimized eclipse time;
  • PROBA 2 shall have a high degree of spacecraft autonomy and ground support automation.

1.2. Launch and orbit

PROBA 2 is planned to be launched from Plesetsk, Russia, in September 2007, on a Rockot launcher. PROBA 2 will be a secondary-passenger of the launch of the SMOS (ESA) spacecraft. It will be directly injected in a Sun-synchronous LEO orbit, with an altitude between 700-800 km (baseline 728km) and with the LTAN at 6:00 AM +/- 15 minutes. The orbit injection accuracy provided by the launcher is sufficient to guarantee that the LTAN will remain within 6:00 AM +/- 45 minutes without the use of onboard propulsion. The orbital period is approximately 100 minutes. The targeted orbit is eclipse-free for 9 months per year, thus making the orbit well suited for the solar observing instruments. Maximum eclipse duration during the eclipse season is less than 20 minutes. Since the orbit remains acceptable for the solar observations during the complete mission lifetime, propulsion is not needed to support the mission. However, as is documented below, a propulsion system is accommodated onboard PROBA 2 as a technology demonstration.

PROBA II Space Segment description

Like Proba V, PROBA 2 has a weight of less than 130 kg which is 30kg lighter than Proba V and belongs to the class of the mini-satellites . Its structure is built using aluminum and CFRP honeycomb panels. Triple junction Gallium Arsenide solar cells, body mounted on 1 panel and mounted on 2 deployable panels, provide the power to the spacecraft and a Li-Ion battery is used for energy storage. A battery-regulated, centrally switched 28V bus distributes the power to the units and the instruments. A high performance computer, based on the LEON processor provides the computing power to the platform and for instrument data processing. It accommodates the memory for house-keeping data storage as well as a mass memory for the payload image data. The telecommunications subsystem is designed to establish and maintain spaceground communications link with the ground segment while the spacecraft remains sun-pointing. It is CCSDS compatible for up- and downlink in the S-band. The set of ACNS units support Sun-pointing, inertial 3-axis attitude pointing as well as Earth pointing and a series of attitude maneuvers. Furthermore, it performs all required navigation and maneuvering computations onboard. The spacecraft platform provides full redundancy.

PROBA 2 Block Diagram
No image "PROBA2_Auto11.jpeg" attached to UserApp/Proba-V

1.3. Ground segment

As for PROBA1, the PROBA2 spacecraft will be operated from the Redu Ground station (Belgium).

Proba 3

Proba-3 is devoted to the demonstration of technologies and techniques for highly-precise formation flying. It consists of two small satellites launched together into a highly elliptical orbit to separate and fly in formation, to prepare for future formation flying missions and characterise sensors and other related technologies.

PROBA 3
No image "PROBA3-02-LR_large" attached to UserApp/Proba-V

Mission objectives and Orbit

The mission will demonstrate formation flying in the context of a large-scale science experiment, the paired satellites together forming a 150-m distance solar coronagraph with an accuracy down to a few millimetres to study the Sun’s faint corona.

The mission is planned to have a lifetime of around two years, following a launch in 2015-2016. The satellites will be launched together in a stack configuration, with the larger ‘coronagraph’ spacecraft on the bottom to provide control and the smaller