The primary driver of climate change is the increase of CO2 in the atmosphere. The importance of biomass in climate and the carbon cycle arises because around 50% of biomass consists of carbon. Hence destruction of biomass due to deforestation and forest degradation leads to carbon emissions to the atmosphere, while uptake of CO2 due to forests growth removes CO2 from the atmosphere. Hence biomass plays a key role in both climate warming and its mitigation. This is why it is recognized as an Essential Climate Variable in the Global Climate Observing System. The most recent estimate of the global carbon budget (Friedlingstein et al., 2020) indicates that the average Land Use Change (LUC) CO2 flux to the atmosphere for 2010-2019 was 5.7 ± 2.6 GtCO2 y-1 (this is a net value including both loss of forests and forest regrowth and should be compared with the fossil fuel emissions of 34.4 ± 21.7 GtCO2 y-1) while the terrestrial CO2 sink (enhanced generation of biomass due to CO2 fertilisation and climate warming) for the same period is 12.5 ± 2.2 GtCO2 y-1. Note that (a) the latter value is model-based; (b) there is not a clear demarcation between the forest growth terms contained in the LUC flux and the terrestrial sink; (c) there are large uncertainties (and very large relative uncertainties) in the land terms. Hence accurate measurements of biomass and its changes are fundamental in quantifying the carbon cycle and reducing these uncertainties. This is the primary objective of the BIOMASS mission. Current estimates of emissions due to deforestation rely on separate estimates of forest change (activity data) and biomass and typically emissions are calculated as the product deforested area x biomass x emission factor, where the emission factor describes the fraction of biomass carbon that is converted to CO2 emissions. The biomass term will usually be some average value based on available ground data, but this may not properly represent the biomass of the cleared area. Instead, BIOMASS will be able to measure the both deforestation activity itself and the biomass where this occurred, thus removing potential biases in the emission estimates. Less clear is the extent to which BIOMASS will be able to measure emissions due to forest degradation (which depends on the sensitivity of the signal to biomass) and forest growth. Although the rate of increase of scattering from the forest canopy as biomass increases is expected to be greatest when forests are young, the high penetration at the P-band wavelength means that the signal is then likely to be strongly affected by soil scattering. Separating out the biomass signal may therefore be difficult, but the availability of full polarimetry on all measurements may help with this.
As well as direct estimates of biomass change, biomass as a static variable gives information on carbon dynamics when it can be combined with productivity information. This is because for a forest system in equilibrium the residence time of a carbon atom in the system is given by B/NPP, where B is the mean biomass and NPP is the net primary production, i.e., the rate of production of biomass. Uncertainty in residence time dominates the uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2 (Friend et al., 2014). Since NPP can be estimated from optical remote sensing measurements and models, estimates of biomass thus allow access to residence time (though note that this needs consideration of both above- and below-ground biomass pools). This relation has been exploited in several studies, using both models and observations, but a key issue in observationally deduced residence time is the well-known saturation of current sensors at higher levels of biomass, which will translate directly into errors in residence time. The BIOMASS mission is specifically designed to minimize such saturation. More generally, use of biomass information and time series of biomass in model data assimilation schemes has been shown to provide information on a range of key parameters controlling vegetation systems (Yang et al., 2021; Smallman et al. 2017).
More generally, BIOMASS will provide a key element of a global system to measure forest structure and biomass. Other important missions in this overall capability are GEDI and NISAR, which are dedicated to forest observations (though NISAR also has other science goals), together with Sentinel-1, IceSat-2 and the JAXA series of L-band SAR missions. These missions are intended not just to meet the needs of the global climate and carbon cycle modelling community, but also those of nations reporting to the UNFCCC for the Global Stocktake as part of the Paris Agreement. Considerable extra value will be added to these missions by the collaborative NASA/ESA Multi-Mission Algorithm and Analysis Platform, which allows free access and joint analysis to the data from all three missions. This will not only make it possible to produce optimized estimates of biomass by combining the strengths of the three missions, but will also greatly improve the usability of the data by providing embedded processing capabilities that remove the need for countries to have very powerful in-house computing facilities.
References
Friedlingstein P, O’Sullivan M, Jones MW, et al. (2020). Global Carbon Budget 2020, Earth Syst. Sci. Data, 12, 3269–3340, 2020 https://doi.org/10.5194/essd-12-3269-2020
Friend AD, Lucht W, Rademacher TT, et al. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2, PNAS, ww.pnas.org/cgi/doi/10.1073/pnas.1222477110
Smallman L , Exbrayat J-F, Mencuccini M, et al. (2017). Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests, J. geophys. Res.: Biogeosciences, doi: 0.1002/2016JG003520
Yang H, P. Ciais P, Wang Y, et al. (2021). Variations of carbon allocation and turnover time across tropical forests, Global Ecology and Biogeography, doi: 10.1111/geb.13302

The Biomass space segment for the ESA Biomass mission is under development by an industrial consortium led by Airbus Defence and Space Ltd. The project is in its final development phase. It has passed its Critical Design Review in summer 2021 which declared the adequacy of the system design and released the satellite assembly and verification phase.
The ground segment is based on extensive heritage from previous ESA Earth Explorer missions and its development is progressing nominally. A Vega rocket has been procured to launch the satellite; the launch is currently planned for the end of 2023.
The presentation will provide an overview of the elements of the Biomass system as described above and will give an up to date status of its development. The scientific aspects including Level 2 data processing are being dealt with elsewhere in this session.

Scheduled for launch in 2023, ESA’s seventh Earth Explorer Mission, BIOMASS, will carry the first P-band synthetic aperture radar (SAR) to be flown in space, to gather fully polarimetric acquisitions over forested areas worldwide in interferometric and tomographic modes. The system has been designed to produce consistent global maps of the Earth’s forests during a nominal five-year lifetime.

The primary objective of BIOMASS is to determine the worldwide distribution of forest above-ground biomass (AGB) and its change with time [1],[2],[3]. To fulfil this objective, BIOMASS will carry a fully polarimetric P-band SAR operating at a center frequency of 435 MHz and with a bandwidth of 6 MHz. This is the lowest possible frequency for a satellite SAR which fulfills ITU regulations and avoids the deleterious ionospheric disturbances at lower frequencies. The main reasons for choosing the lowest possible operating frequency are: 1) to increase the penetration in all forest biomes, 2) to enhance the interaction with the larger woody vegetation elements, which improves the sensitivity to AGB, and 3) to increase the temporal coherence and enable repeat-pass interferometry and tomography.

Interferometry and tomography are considered essential for the mission since they add a vertical dimension to the SAR measurements and enable 3D forest measurements. Most importantly, they also enable suppressing the radar backscattering originating from the ground level, which is known to reduce sensitivity and cause errors in AGB estimates [4], [5].

The estimation techniques should also consider the temporal sampling pattern. The BIOMASS orbit has two main phases, i.e., a tomographic phase, which follows directly after the commissioning phase, and an interferometric phase during the remaining five-year mission. Data will be collected, in both phases, in a near-repeat orbit with a three-day cycle to maximize temporal coherence. Seven near-repeats will make up a tomographic stack, which will take 18 days to complete, whereas three near-repeat orbits will create an interferometric stack in 6 days. Coverage will then be built up successively, with the successive tomographic or interferometric stacks adding coverage to adjacent areas. Complete coverage is obtained in approximately 14-16 and 7-9 months in the tomographic and interferometric phases, respectively. This means that significant environmental changes will occur due to the orbit characteristics, which the estimation techniques must be designed to handle.

This paper presents methods and algorithms developed to estimate biophysical parameters from BIOMASS measurements and their implementation in the BIOMASS level 2 (L2) prototype processor. The L2 processor will generate global maps of forest Above Ground Biomass (AGB), Forest Height (FH), Forest disturbance (FD). Accurate generation of these products requires the L2 processor to be closely inter-linked with the BIOMASS interferometric processor [6], in order to produce phase-calibrated interferometric stacks, retrieve sub-canopy terrain topography, and generate a 3D representation of forest structure by use of SAR tomography.

AGB estimation results will be shown using BIOMASS-like acquisitions derived from campaign data acquired over six tropical forests in South America and Equatorial Africa. The algorithm is observed to be capable of achieving a relative RMSD of 20% with respect to in situ data using only two “good” calibration points where reference AGB is available, although retrieval accuracy was observed to depend significantly on the quality of the available calibration points. For this reason, the recommendation is made that the global AGB estimation scheme for BIOMASS relies on calibration and validation with AGB estimates from in situ inventories, which are assumed to be less prone to systematic errors. The AGB estimation performance is observed to depend on the AGB range and degrades when ground topography is significant. Good performance is achieved when the AGB interval is large (> 400 t/ha) and the average is in the interval 200–250 t/ha. [5]

[1] ESA. “BIOMASS—Report for Mission Selection—An Earth Explorer to Observe Forest Biomass”; SP-1324/1; European Space Agency: Noordwijk, The Netherlands, 2012.
[2] T. Le Toan, S. Quegan, M.W.J. Davidson, H. Balzter, P. Phaillou, K. Papathanassiou, S. Plummer, F. Rocca, S. Saatchi, H. Shugart, L. Ulander, “The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle,” Remote Sensing of Environment, vol. 115, pp. 2850-2860, Jun. 2011
[3] Shaun Quegan, Thuy Le Toan, Jerome Chave, Jorgen Dall, Jean-François Exbrayat, Dinh Ho Tong Minh, Mark Lomas, Mauro Mariotti D'Alessandro, Philippe Paillou, Kostas Papathanassiou, Fabio Rocca, Sassan Saatchi, Klaus Scipal, Hank Shugart, T. Luke Smallman, Maciej J. Soja, Stefano Tebaldini, Lars Ulander, Ludovic Villard, Mathew Williams “The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space” Remote Sensing of Environment, Volume 227, 2019, Pages 44-60, ISSN 0034-4257,
[4] Soja, M., Quegan, S., Mariotti d’Alessandro, M. Banda, F. Scipal, K., Tebaldini, S. Ulander, L.M.H. “Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data”, Remote Sens. Environ. Volume 253, February 2021, 112153
[5] M. Mariotti d’Alessandro, S. Tebaldini, S. Quegan, M. J. Soja, L. M. H. Ulander and K. Scipal, "Interferometric Ground Cancellation for Above Ground Biomass Estimation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 9, pp. 6410-6419, Sept. 2020
[6] M. Pinheiro at al., “BIOMASS DEM Product Prototype Processor”, EUSAR 2021

Several potential secondary mission objectives arise from the opportunity to explore Earth for the first time with a P-band SAR system. The Biomass Secondary Objectives Assessment Study (Paillou et al., 2011) identified a variety of secondary applications and assessed whether their requirements could be accommodated within the mission specifications. In particular, three objectives are expected to benefit significantly from the long P-band wavelength, while at the same time being feasible and compatible with the Biomass mission design. The presentation will detail each science objective and provide current insights on these applications for Biomass.

1. Mapping subsurface geology
Access to freshwater resources is already a major concern: in Saharan and sub-Saharan Africa, most people do not have access to safe water supplies, and the situation is expected to get worse in the future. Geological maps are crucial for mineral and groundwater exploration, and remote sensing is an important tool in establishing such maps. However, in arid regions such as North Africa, the geology is mostly hidden under a thin layer of dry, sandy sediments. Low-frequency SAR is able to penetrate dry sediments and map the subsurface down to several metres, because of low absorption and limited volume scattering. For example, L-band SAR has proven capable of penetrating a few metres of dry, homogeneous material such as sand (McCauley et al., 1982). If the sand surface is smooth, dry and thin, the subsurface of interest will not be masked, and the measured backscatter will provide an image of the subsurface roughness and slope. This can then be turned into information that is useful for exploration and geophysical prospecting (Paillou, 2017). Aircraft campaigns have illustrated the capacity of P-band SAR to penetrate at least 4 m of dry sediment (Paillou et al., 2011). The enhanced penetration capabilities of a P-band SAR, being less sensitive to the covering sediments, will be important in groundwater exploration but will also offer a unique opportunity to reveal the hidden and still unknown past hydrological history of deserts.

2. Ice sheet applications
Large changes of the Greenland and Antarctic ice sheets have been observed over recent decades, and SAR data have shown a significant acceleration of the glacier velocities both in Greenland and in Antarctica. One way of estimating the mass balance of ice sheets is by mapping the ice velocity at a flux gate with known ice thickness. Accurate ice velocity maps are also needed when modelling the response of ice sheets to climate change. In Greenland, ice sheet velocity maps are generated on an operational basis (Solgaard et al. 2021), and the velocity fields of the Antarctic ice sheets have also been mapped (Rignot et al. 2011). The measurement accuracy, however, is 1 m/yr to 17 m/yr with the currently available data, while the histogram for the entire Antarctica peaks at 5 m/yr (Rignot et al. 2011). To achieve a high velocity sensitivity, SAR data must be acquired with a long temporal baseline, and the correlation time increases with decreasing frequency, as seen when comparing L- and C-band results (Rignot and Mouginot, 2012). P-band excels by an even longer correlation time, as deep penetration makes the radar signal interact with stable subsurface scatterers in the dry snow zone. A temporal baseline defined by BIOMASS’ 8 months global mapping cycle is not unrealistic (Dall et al. 2013). Ice applications are dependent on sufficient compensation for ionospheric scintillations, which are particularly severe at high latitudes. Without any compensation, the ionosphere is the primary error contributor at L-band, and at P-band the ionosphere may be prohibitive, because the impact of the ionospheric scintillations increases with decreasing frequency.

3. Terrain Topography under Dense Vegetation
Digital Terrain Models (DTMs) represent the elevation of the ground in the absence of vegetation, buildings and so on. These ‘bare-earth’ images are crucial in a range of applications, including ecology, forest management, water resource management, mineral exploitation, national security and scientific research. However, currently available large-scale products are more accurately described as Digital Elevation Models (DEMs) because in forested areas they differ significantly from a true DTM. At P-band, vegetation causes less attenuation, therefore Biomass can fill this major gap in our knowledge of global topography. In addition, the scattering centre of the tree-ground double bounce- signal occurs at ground level and can be isolated using polarimetry.
Over its lifetime, Biomass will produce a DTM of the terrain topography under dense vegetation, thus removing the biases in DEMs using shorter wavelengths, such as the Copernicus DEM. Biomass will also be able to exploit this new DTM for slope corrections associated with the primary objectives, allowing initial products generated with current DEMs to be reprocessed, thus refining the biomass products.

References:

McCauley J. F., G. G. Schaber, C. S. Breed, M. J. Grolier, C. V. Haynes, B. Issawi, C. Elachi, R. Blom, “Subsurface valleys and geoarchaeology of the eastern Sahara revealed by Shuttle Radar,” Science, vol. 218, pp. 1004-1020, 1982.
Paillou Ph., J. Dall, P. Dubois-Fernandez, I. Hanjsek, R. Lucas, K. Scipal, BIOMASS Secondary Objectives Assessment Study, ESA ITT AO 1-6543/10/NL/CT, 210 p., 2011.
Paillou Ph., O. Ruault du Plessis, C. Coulombeix, P. Dubois-Fernandez, S. Bacha, N. Sayah, A. Ezzine, “The TUNISAR experiment: Flying an airborne P-Band SAR over southern Tunisia to map subsurface geology and soil salinity,” PIERS 2011, Marrakesh, Marocco, march 2011.
Paillou Ph., “Mapping palaeohydrography in deserts: Contribution from space-borne imaging radar,” Water, vol. 9, no. 194, doi:10.3390/w9030194, 2017.
A. Solgaard, A. Kusk J.P. Merryman, J. Dall, K.D. Kankoff, A.P. Ahlstrøm, S.B. Andersen, M. Citterio, N.B, Karlsson, K.K. Kjeldsen, N.J. Korsgaard, S.H. Larsen, R.S. Fausto, Greenland ice velocity maps from the PROMICE project”, Earth System Science Data, Vol. 13, No. 7, pp. 3491-3512, November, 2021.
E. Rignot, J. Mourinot, B. Scheuchl, “Ice Flow of the Antarctic Ice Sheet”, Science, Vol. 333, No. 9, pp. 1427-1430, September 2011.
E. Rignot, J. Mouginot, Ice flow in Greenland for the International Polar Year 2008-2009”, Geophysical Research Letters, Vol. 39, L11501, pp. 1-7, June 2012.
J. Dall, U. Nielsen, A. Kusk, R.S.W. van de Wal, “Ice flow mapping with P-band SAR”, Proceedings of the IEEE 2013 International Geoscience and Remote Sensing Symposium, 4 p., Melbourne, July 2013.

BIOMASS is the ESA 7th Explorer Mission. It features for the first time ever a spaceborne quad polarimetric SAR at P-band. BIOMASS is a polar orbiting satellite aiming primarily at deriving forest biophysical variables essential to the understanding of the carbon cycle.
The mission features a space segment (the satellite) and ground segment responsible for the planning, commanding, acquisition, processing, calibration and archiving of the BIOMASS data. The BIOMASS Payload Data Ground Segment (PDGS) implements the full processing chain from the Level-0 to the forest products that will be used by the scientists.

In order to further support the science behind the upcoming BIOMASS satellite mission, BIOMASS Ground segment will be complemented with a cloud-computing platform called Multi-Mission Algorithm and Analysis Platform (MAAP) currently under development. The MAAP is jointly developed with NASA such as to strengthen the scientific cooperation. It will provide high performing computing capabilities and algorithmic resources closer to the BIOMASS data (but also other satellites, airborne and in situ data)

To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open-source policies. As one such example of best collaborative and open-source practices, the BIOMASS data processing algorithms are developed on MAAP under the umbrella of an open-source scientific software project called BioPAL. In addition to aiding researchers, the MAAP will focus on sharing data, science algorithms and compute resources in order to foster and accelerate scientific research.


Index Terms— Biosphere, Data dissemination, Cloud computing, Open science, Open data, Open Source

1. INTRODUCTION

With the launch of new satellite missions and growing understanding of the complexity of ecological processes, the scientific community is faced with a unique and immediate need for improved data sharing and collaboration. This is especially evident in the Earth sciences and carbon monitoring community. While the new Earth Observation missions and the corresponding research leading up to launch, which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. Due to the constraints of existing organizational infrastructures, these large data volumes will place accessibility limits on the scientific community and may ultimately impede scientific progress.


2. THE BIOMASS MISSION

Selected as European Space Agency’s seventh Earth Explorer in May 2013, the BIOMASS mission will provide crucial information about the state of our forests and how they are changing [1]. This mission is being designed to provide, for the first time from space, P-band Synthetic Aperture Radar measurements to determine the amount of biomass and carbon stored in forests [2]. The data will be used to further our knowledge of the role forests play in the carbon cycle.


3. BIOMASS GROUND SEGMENT ARCHITECTURE

The BIOMASS PDGS (Payload Data Ground Segment) is responsible for the planning, commanding, acquisition, processing, dissemination and archiving of the BIOMASS data.
The talk will give an overview of the PDGS architecture and will focus on the data processing aspects and the related technical budget..
It will in particular present the BIOMASS product family and further describes the processing model from the Level-0 to the user L3 products.


4. CONCEPT OF MISSION ALGORITHM AND ANALYSIS PLATFORM (MAAP)

In the context of innovative satellite missions and an evolving ground segment, the concept of Mission Algorithm and Analysis Platform dedicated to the BIOMASS mission is proposed [3]. This Mission Algorithm and Analysis Platform will be a virtual open and collaborative environment. The goal is to bring together a data centre (Earth Observation and non- Earth Observation data), computing resources and hosted processing, collaborative tools (processing tools, data mining tools, user tools, …), concurrent design and test bench functions, application shops and market place functionalities, accounting tools to manage resource utilisation, communication tools (social network) and documentation.
This platform will give the opportunity, for the first time, to build from a community of user of this new Earth Observation mission around this innovative concept.

5. BIOPAL: THE OPEN-SOURCE BIOMASS PROCESSOR

To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open-source policies. As an example of best collaborative and open-source practices, the BIOMASS Processing Suite (BPS) will be made openly available within the MAAP. This Processing Suite contains all elements to generate the BIOMASS upper-level data products and is currently in development under the umbrella of the open-source project called BioPAL [4]. BioPAL is developed in a coherent manner, putting a modular architecture and reproducible software design in place. BioPAL aims to factorize the development and testing of common elements across different BIOMASS processors. The architecture of this scientific software makes lower-level bricks and functionalities available through a well-documented Application Programming Interface (API) to foster the reuse and continuous development of processing algorithms from the BIOMASS user community.

6. OBJECTIVES OF THE MAAP PROJECT

The goal for the MAAP is to establish a collaboration framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by NASA and ESA EO data users.
The objectives of the MAAP for the BIOMASS missions are to:
1) Enable researchers to easily discover, process, visualize and analyze large volumes of data from both agencies;
2) Provide a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation;
3) Provide a version-controlled science algorithm development environment that supports tools, co-located data and processing resources;
4) Address intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms.
7. REFERENCES

[1] T. Le Toan, S. Quegan, M. Davidson, H. Balzter, P. Paillou, K. Papathanassiou, S. Plummer, F. Rocca, S. Saatchi, H. Shugart and L. Ulander, “The BIOMASS Mission: Mapping global forest biomass to better understand the terrestrial carbon cycle”, Remote Sensing of Environment, Vol. 115, No. 11, pp. 2850-2860, June 2011.

[2] T. Le Toan, A. Beaudoin, et al., “Relating forest biomass to SAR data”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, pp. 403-411, March 1992.

[3] Albinet, C., Whitehurst, A.S., Jewell, L.A. et al., “A Joint ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) for Biomass, NISAR, and GEDI”, Surveys in Geophysics, 40, 1017–1027 (2019). https://doi.org/10.1007/s10712-019-09541-z

[4] BioPAL project site:
http://www.biopal.org

Biomass calibration concept towards mission operations
Philip Willemsen(a), Antonio Leanza(b), Adriano Carbone(c), Ernesto Imbembo(a), Björn Rommen(a), Michael Fehringer(a), Maktar Malik(a), Tristan Simon(a), Klaus Scipal(d)
a ESA-ESTEC, Noordwijk, The Netherlands
b SERCO B.V. for ESA-ESTEC, Noordwijk, The Netherlands
c Rhea System B.V. for ESA-ESTEC, Noordwijk, The Netherlands
d ESA-ESRIN, Frascati, Italy
The Biomass mission is an Earth Explorer Mission in the ESA Earth Observation Programme.
The primary objective of Biomass is to determine the worldwide distribution of forest above-ground biomass in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere.
The Biomass Satellite industrial Prime contractor is Airbus Defence and Space Ltd. The radar instrument is built by Airbus Defence and Space, Friedrichshafen.

The Biomass satellite carries a fully-polarimetric (HH, VV, HV, VH) P-band SAR which operates in strip-map imaging mode with a carrier frequency of 435 MHz.
The Biomass satellite employs a unique design: the coverage and performance requirements at P-band imply the use of a large aperture antenna. This is realized by an offset-fed reflector antenna system consisting of the instrument feed array and a deployable reflector with a 12 m projected aperture.

The Biomass mission relies on a calibration transponder which is designed and developed specifically for Biomass mission needs. The Biomass Calibration Transponder (BCT) is a fully polarimetric active transponder working in P-band. It is used primarily during the commissioning phase for three applications:
1/ the Biomass satellite end-to-end antenna pattern characterisation, 2/ radiometric and polarimetric calibration and 3/ performance verification.
The BCT is provided by C-Core, Canada. It will be located at ESA’s New Norcia antenna site in Australia.

The antenna pattern knowledge is a necessary input for the on-ground data processing.
Due to the large size of the satellite in reflector-deployed configuration, it is not possible to characterise the end-to-end antenna pattern on-ground with sufficient accuracy with the available test facilities.
Only in-flight antenna pattern measurements can provide the required information with the sufficient accuracy.

To measure the antenna pattern in flight, the satellite is placed in a dedicated commissioning orbit with a defined orbit drift and a repeat cycle of 3 days. The orbit drift allows an antenna pattern characterisation at different azimuth cuts. In total, two months are dedicated to the antenna pattern characterisation which allows sufficient transponder overpasses providing doublet patterns measurements at different elevations.

Once the antenna pattern has been measured, the commissioning phase will continue with the radiometric, geometric and polarimetric calibration and performance verification activities.
As for the antenna characterisation, the calibration and verification activities will rely on the BCT. In addition, the potential of natural targets to support the cal/val activities is currently investigated.
The following system key performance requirements are driving the in-flight performance verification activities:

• Channel Imbalance and Cross Talk: for the accurate estimation of the channel imbalance and cross-talk, all four polarimetric scattering responses are required from a location with the same Faraday rotation.
• Radiometric Bias: the largest contribution to the radiometric bias budget is the external calibration bias of the reference target.
• Radiometric stability: the radiometric stability drives the number of observations of reference targets, and the stability of those targets.
• Residual Phase over pulse travel time: the residual phase stability requirement drives the short term phase stability requirements of the reference target.