Several spaceborne X-band synthetic aperture radars (SARs) are nowadays operational, representing a well-established tool for Earth remote sensing at very high spatial resolution (order of meters up to tens of meters). Consolidated civil applications include flood area detection and monitoring, earthquake analysis, digital elevation modelling, land use monitoring and classification. Until now, the exploitation of SAR for hydrological cycle modelling and numerical weather forecast has not been pursued, mainly due to the use of relatively low microwave frequency bands, such as L, S and X. However, there are scientific and experimental evidences that: i) atmospheric precipitation in liquid and ice phase can affect SAR imagery at X band and beyond and its intensity can be retrieved (e.g., Marzano et al., 2010; Marzano et al., 2012; Pulvirenti et al., 2014), ii) snow areal extent and mass (water-equivalent) can be detected and estimated by using SAR measurements at X band and beyond (e.g., Rott et al., 2010; Montomoli et al., 2016; Cui et al., 2016). In this respect, spaceborne SARs cannot be always considered “all weather” sensors and the use of higher frequencies can be also exploited to retrieve atmospheric water content both within clouds and near the surface.
KydroSAT mission concept foresees a high-performance miniaturised SAR at Ku and Ka band, named KydroSAR, specifically devoted to mapping, detecting and estimating atmospheric precipitation and surface snow. The KydroSAR instrument is a dual-frequency lightweight fully-digital SAR whose baseline includes dual-polarization capability, large variable swath (between 50 and 150 km), high orbit duty cycle (>75%) and flexible ground resolution (between 5 and 150 m), depending on the scan/observation mode. The overall KydroSAT mission concept is based on the formation of two near-polar low-Earth-orbit minisatellites (less than 400 kg), named KydroSats and both carrying a KydroSAR payload, in order to extend the swath up to 150-300 km. These 2 KydroSats are part of an along-track convoy with X-band SAR COSMO-SkyMed and L-bad SAR SAOCOM platforms. In this way, the same Earth scene would be observed at L, X, Ku and Ka bands, representing a unique opportunity to improve high-resolution atmospheric and cryospheric products, but also useful for other applications.
In order to demonstrate the validity of the KydroSAT concept, in this work we will simulate the SAR response at L, X, Ku and Ka bands of a same scene, using the SAR forward model described in Mori et al. (2017). Both realistic 3D scenes, generated by high-resolution Cloud Resolving Models (e.g., SAM) and simplified scenarios, easier to be interpreted, will be considered. Numerical results will be discussed in order to exploit the synergic approach of the KydroSAT convoy approach and to evaluate new quantitative retrievals of rain cloud parameters, such as columnar water content and precipitation rate. Using cryospheric synthetic scenarios (Montomoli et al., 2016), numerical simulations will be also used to show the capabilities of KydroSAT for snow areal extent and mass (water equivalent) monitoring.

In the last couple of years, small satellites have paved the ground for new opportunities aiming to understand the Earth’s surface processes and bring new services to EO community.

In the beginning of 2018, the Finnish Earth Observation company ICEYE successfully demonstrated feasibility of commercial microsatellite SAR with successful stripmap-imaging results from its first satellite, ICEYE X1.

Over the next 3 years, ICEYE’s goal is to build the world’s first and largest commercial constellation of SAR microsatellites, providing access to timely and reliable radar satellite images up to tens of accesses per day at any location on Earth. High temporal resolution data will assist in understanding better Earth’s surface processes, introduce novel approaches and services, and complementing the already existing well-established methods. The present work aims to showcase how ICEYE constellation can contribute to the field of geohazard monitoring and prediction.

Since the early 1990’s, a powerful methodology based on SAR data, Radar Interferometry (InSAR), has shown its capabilities used in a wide-range of applications for ground motion monitoring. This technique is extremely useful especially for hazardous regions and monitoring geohazards. However, more frequent systematic monitoring is needed for prediction, management and response of such events.

A characteristic example is volcano monitoring where conditions prevent or limit the use of ground-based tools. InSAR can be utilized to monitor volcanoes remotely producing a deformation map, covering large scale areas with centimeter accuracy. Based on literature, scientists have shown that already existing SAR satellites can help in monitoring volcanoes - however, more real-time data and therefore rapid InSAR are needed in order to provide critical early warning and monitoring active events for guiding disaster response. Another characteristic geohazard which needs systematic and/or real-time data is accelerated slope deformation. Scientists suggest that SAR data with very short revisiting time could be used to monitor unstable areas. Currently, only ground-based tools can measure systematic and accurate high-frequency displacement maps for understanding surface processes and forecasting catastrophic slope failure events. While the capabilities of ground-based sensors are extremely useful, an individual sensor can often cover only a single slope, and the installation and maintenance of sensor network with comprehensive coverage can become unmanageable.

Novel technological advances in the form of near-real-time data provided by ICEYE’s future constellations and combined with InSAR methodologies will lead to possibility of continuous high-revisit monitoring of geohazards for wide areas, accelerating our understanding of surface processes and hence support decision-makers to plan and respond faster.

More research is still required into optimal constellation arrangement and data product requirements for different types of geohazard modeling and prediction. ICEYE invites the scientific community to actively participate in further studies and demonstration campaigns with the growing constellation.

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In recent years, due to groundwater over-exploitation in Iran, land subsidence has been a problematic issue in this country. Iran is located in arid and semi-arid areas that is why, most of agriculture and drinking water demands is supplied by groundwater resources. As a consequence, annual decreasing of groundwater level is observed in most regions of Iran. The negative groundwater budget in last two decades in several aquifers in this country, has caused the land subsidence in some areas. Qazvin plain selected as study area, which is one of the industrial and agricultural provinces of Iran that 76% of water consumption comes from groundwater. The main goal of this paper is to assess the land subsidence related to aquifer characteristics. Due to expensive cost and difficulty of satellite data, this research used Interferometric Synthetic Aperture Radar (InSAR) technique. InSAR is a powerful tool for measuring ground surface deformation, displacement, topography and etc by combination of radar images in different times. In this method, an interferogram has been calculated by subtracting the phase of two radar images contains different phase contribution mainly due to topography. The purpose of this paper is calculating the land subsidence and finding its relationship with the aquifers parameters such as water level, storage coefficient, transmissivity, aquifer thickness in Qazvin plain using InSAR technique. The results of this paper show that there is a direct relationship between groundwater characteristics and land subsidence

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Reliable precipitation data and information are essential for understanding the hydrological cycles at both regional and global scales. However, accurate observation of precipitation is challenging in many remote regions of the world, due to sparse gauge networks, topography and high spatio-temporal variability in precipitation especially in arid and semi-arid region such as Iran. Satellite precipitation remote sensing such as GPM present the variability of precipitation spatially and temporally for large area such as Lorestan Province located in West of Iran.
The Global Precipitation Measurement (GPM) mission provides precipitation measurements from space at a spatial resolution of 0.1° × 0.1° and a half-hourly temporal resolution. The performance of GPM is cross-evaluated using daily precipitation measured by the synoptic rain-gauges over Lorestan Province, located in Iran for the 2016. Statistical analyses of shows appreciably good correlations between rainfall estimate by GMP and measured by rain gauges. Overall, this early study highlights that the GPM product can adequately estimate the amount of precipitation both statistically and hydrologically.

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Most optical satellite systems are set in a sun-synchronous orbit. Because of such similar configurations, orbits-of-opportunity arise. These are combinations of satellites that sense near-simultaneously. Consequently, more information can be generated from a single satellite mission when it is orchestrated with another. Hence, one tactic is to let a small-satellite piggyback with a monitoring program.

In this study we demonstrate such a strategy where we make use of the PROBA-V small-satellite in combination with Sentinel-2. The inclination of both satellites are very similar, as well as, their overpass time. This makes large scale extraction of displacements possible. One such application that can make use of this recording sequence is the monitoring of river ice break-up. In spring the melting season sets in and Arctic rivers increase in flow discharge. Eventually the water is able to break the ice cover, which will then float along. However, congestion of this ice results in jamming, associated flooding and ice run, causing damage to infrastructure and river banks. The long stretch of rivers make monitoring difficult, thus satellite remote sensing can be a means for identifying bottlenecks.

The resolution of Proba-V is 100 meters, but the time separation results in drift of the floating ice in the order of 1 km. Hence, from data of this satellite pair we are able to generate a dense velocity field over the Lena River in Russia, which stretches more than 300 km. This study demonstrates the potential of the Sentinels as an umbrella for other smaller missions. This near-simultaneous configuration has not been envisioned as a mission objective, but is coincidence. However for a future small-satellite design through clever orbit configurations can easily enhance a satellites product portfolio.

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Knowledge of water-surface velocities in rivers is useful for understanding a wide range of processes. In cold regions, river-ice break up and the associated downstream transport of ice debris is often the most important hydrological event of the year, producing exceptional flood levels and dramatic consequences for river infrastructure and ecology. Also river freeze-up causes an array of impacts on the river system. Quantification of river surface velocity from remote sensing rarely covers entire river reaches, and accurate and complete surface-velocity fields on rivers have rarely been produced. In this study, we track river-ice debris over entire river reaches and over a time period of about one and a half minutes, which is the typical time lapse between the image ground swaths of subsequent cubesats of the Planet Dove constellation. This constellation consists of more than 150 free-flying micro-satellites, mostly in near-polar orbit. The instruments carry frame cameras that image the Earth surface in nadir direction with resolutions of around 3 m, and about 25-30 km swath width. The constellation is able to image the entire land surface at least once per day. This is accomplished by letting the satellites follow each other in the same orbit with a time delay so that Earth rotation causes latitudinal displacement between the subsequent ground swaths to cover the complete Earth surface, and leading to the near-simultaneous cross-track overlap exploited in this study. Here, we demonstrate and evaluate the potential of the Planet cubesat constellation to measure river surface velocities systematically and over large areas. We assess the accuracy of the velocity fields obtained, and exemplify results for different river conditions and application scenarios. Finally, we explore the possibility of combining such-derived horizontal water surface velocities with repeat satellite-altimetric river stage measurements in order to estimate river discharge from space.

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The dictum “small is the new big”, synonym of “when bigger isn't better”, increasingly applies to the traditional Earth observation (EO) industry whose business model is under pressure by an emerging EO small-scale (< 500 Kg in weight) satellite constellation technology, aiming at sub-meter spatial resolution and sub-daily temporal resolution in a “seamless innovation chain” required by Space 4.0, whose ambitious objectives range from the business applications market to grand societal challenges, e.g., the United Nations Development Programme (UNDP) Sustainable Developments Goals (SDGs), such as migration, food security, climate change, terrorism, urbanisation and poverty. In spite of an increasing interest on EO small satellite constellations, according to some EO experts “the small satellite market is a failed business model in the short term at least. It is hype. It is here to hit established players (just like Uber – an excellent player with an innovative business model, to shake up the existing setup), but making huge losses in the process. It is quite obvious that we have already reached a point with the EO industry that the amount of EO data collected is way beyond than the need. So, the next time you get excited about 80 odd satellites launched by someone, just question, when and how will they make money?” To further investigate pros and cons of existing EO small satellite constellations, our analytic and pragmatic analysis starts from first principles proposed by the intergovernmental Group on Earth Observations (GEO), whose visionary goal of a Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015, unaccomplished to date, is systematic transformation of multi-sensor, multi-angular and multi-temporal EO big data cubes into timely, comprehensive and operational EO value-adding information products and services (VAPS). According to the joint GEO-Committee on Earth Observation Satellites (CEOS) Quality Accuracy Framework for EO (QA4EO) Calibration/Validation (Cal/Val) guidelines, necessary-but-not-sufficient pre-conditions for GEOSS “to allow the access to the Right Information, in the Right Format, at the Right Time, to the Right People, to Make the Right Decisions” are the four key principles of Availability/Accessibility and Suitability/Reliability applied to data and information products. Related to the four GEO-CEOS key principles are the foundational principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) applied to data, products and processes to enhance the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. Indeed, existing EO small satellite constellations, such as the PlanetScope constellation of 130+ Dove nano-satellites (standardized 3U CubeSat technology, 5 kg in weight, 3 m multi-spectral (MS) spatial resolution, time resolution > 10X Daily), and Planet’s SkySat constellation of 13 micro-satellites (125 kg in weight, 1 m MS spatial resolution, time resolution 2X Daily), guarantee augmented Suitability (in terms of very high spatial and temporal resolutions) and enhanced Availability/Accessibility (expected to be near real-time). On the other hand, the key principle of Reliability/Interoperability requires the remote sensing (RS) community to enforce mandatory GEO-CEOS QA4EO Cal/Val principles within and across small satellite constellations. Radiometric Cal is the transformation of dimensionless digital numbers (DNs) into a radiometric unit of measure to be community-agreed upon at increasing levels of information quality, specifically, top-of-atmosphere (TOA) radiance (TOARD) in range [0, +infty), TOA reflectance (TOARF) in range [0, 1], surface reflectance (SURF) in range [0, 1], corrected for atmospheric topographic and/or adjacency effects, and surface albedo in range [0, 1], corrected for bidirectional reflectance distribution function (BRDF) effects. On theory, EO data Cal is a well-known “prerequisite for physical model-based analysis of airborne and satellite sensor measurements in the optical domain”. For example, EO data Cal is considered mandatory by the GEO-CEOS QA4EO Cal/Val guidelines. It guarantees interoperability (harmonization) of EO data acquired across time, space and sensors, in agreement with the FAIR criteria. On a daily basis, all sensory data we deal with are provided with a community-agreed physical unit of measure, e.g., hour, meter, kg, etc. Whereas physical variables can be investigated by physical model-based, statistical model-based or hybrid (combined deductive/ top-down/ physical model-based and inductive/ bottom-up/ statistical model-based) inference systems, uncalibrated sensory data provided with no physical meaning can be investigated by statistical data models exclusively. Although statistical models do not require physical variables as input, they can benefit from data Cal in terms of augmented robustness to changes in the input data set acquired across time, space and sensors. Irrespective of these unquestionable true-facts, the GEO-CEOS QA4EO Cal requirement remains largely neglected in the RS common practice. For example, in the large majority of papers published in the RS literature the word “radiometric calibration” is absent, which means these works cope with uncalibrated EO data provided with no physical unit of radiometric measure. One consequence is that, to date, statistical model-based and inductive learning-from-data EO image understanding (EO-IU) systems dominate the RS literature as well as commercial EO image processing software toolboxes, consisting of overly complicated collections of inductive learning-from-data algorithms to choose from based on heuristics. In EO small-scale (< 500 Kg in weight) satellite constellations, the lack of an on-board radiometric Cal sub-system, to be compensated by vicarious Cal strategies, is expected to have an impact on intra- and inter-constellation Reliability/Interoperability requirements. For example, a Planet Surface Reflectance (SR) Product is systematically generated by Planet from the PlanetScope constellation. Based on the Planet SR product documentation, the PlanetScope radiometric Cal metadata file misses a per-band offset (bias) Cal coefficient. As a consequence, TOARD, TOARF and SURF values estimated in sequence are affected by an unknown bias (additive error term), either positive, null or negative. Consisting of SURF values corrected for atmospheric effects, the PlanetScope SR Product is expected to ensure consistency across localized atmospheric conditions, minimizing uncertainty in spectral response across time and location. No Planet SR product is available for the Planet SkySat micro-satellite constellation. According to the Planet SkySat Imagery Product specification, SkySat DNs are radiometrically calibrated into absolute (at-sensor, TOA) radiance (TOARD) units, derived using vicarious Cal methods. This means that, first, PlanetScope and SkySat radiometrically calibrated products are not comparable in physical terms. Second, the PlanetScope SR Product is not comparable in physical terms and “difficult” to be compared (e.g., fused) in statistical terms with other EO data-derived products radiometrically calibrated into SURF values corrected for atmospheric, topographic and adjacency effects, such as the Sentinel-2 Correction Prototype Processor (Sen2Cor), developed and distributed free-of-cost by the European Space Agency (ESA) to be run on user side. In compliance with the GEO-CEOS QA4EO Cal/Val guidelines, the goal of the present work is to validate by independent means the absolute radiometric quality (in comparison with a “ground truth” or reference baseline) and robustness to changes in input data, acquired across time, space and sensors within and across constellations, of the PlanetScope SR Product, consisting of SURF values corrected for atmospheric effects for augmented Reliability/Interoperability. To validate the radiometric Cal of the PlanetScope SR Product, an off-the-shelf Satellite Image Automatic Mapper™ (SIAM™) lightweight computer program was adopted. Presented in recent years in the RS literature, an off-the-shelf SIAM lightweight computer program in operating mode accomplishes automated near real-time MS reflectance (either TOARF, SURF or surface albedo) space hyperpolyhedralization into multi-level vocabularies of prior knowledge-based color names, superpixel detection and per-pixel vector quantization (VQ) quality assessment. Methodological recommendations and experimental conclusions of the present work are summarized as follows. Either TOARF, SURF or surface albedo values, whose physical domain of change is 0.0-1.0, can be rescaled to range 0-255 according to an unsigned char data coding formulation, byte(SURF / 255. + 0.5), which is affected by a quantization (discretization) error equal to (Max – Min) / number of bins / 2 (due to rounding to the closest integer, either above or below) = (1.0 – 0.) / 255. / 2. = 0.002 = 0.2%, to be considered negligible. For comparison purposes, approximation of the sun-Earth distance to 1 irrespective of the image acquisition time typically causes TOARF estimation errors of about 3-5%. Our first original conclusion, to be considered straightforward, which does not mean either obvious or trivial, is that the Planet SR Product, coded as a 16-bit unsigned short integer, can be transcoded into an 8-bit unsigned char, affected by a quantization error as low as 0.2%, with a 50% save in memory storage. Second, in line with theoretical expectations due to a lack of offset Cal parameters, in our experiments, the PlanetScope SR Product was typically affected by a positive bias in the near infra-red (NIR) band, to be counterbalanced by dark-object subtraction in band NIR = 15 digital units in range 0-255, where SURF values in range [0.0, 1.0] were byte-coded into range 0-255 according to equation byte(SURF / 255. + 0.5).

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Space is currently experiencing an innovation-driven paradigm shift, both from within and outside of the space domain. Space used to be a domain for science and public authorities but industry is gearing up and will be in the driving seat to create an economy based on space assets and technology.

Especially the global bio-economy can sharpen up their business with Remote Sensing. The entire value chain: seed/plant breeders, seed/plant multipliers, operational & commercial farming, food processing industry will benefit from the objective measurements that Remote Sensing can bring. While public available data ( e.g. Sentinels & Landsat ) boost the uptake of earth observation, they do not always provide the necessary revisit time. This information is a crucial element for companies making a switch from product to services.

In this paper we want to present two developments which we are presently undertaking where both the miniaturisation of remote sensing instrumentation in combination with the recent revolution in small satellite development allows to build a constellation of satellites within reasonable cost which will provide the necessary information at high revisit time needed by society and industry.

A first track is the development -within an ESA-GSTP project- of a compact hyperspectral VISNIR imager called CHIEM. Multitemporal spaceborne hyperspectral imaging will especially be beneficial for vegetation monitoring and is essential for precision agriculture and forestry. The CHIEM instrument is designed to fit within a 12 U CubeSat. On a satellite mission operating at 500km, it offers a swath of 80km and a GSD of 20m. The imager can also accommodate alternative operational scenarios, including airborne applications. The hyperspectral instrument is formed by Fabry-Perot type interference filters in varying thickness, each transmitting a narrow wavelength band with a spectral resolution (FWHM) between 5 and 10nm. The filters are directly deposited on a 2D 12 Mpx CMOS sensor array in a linewise arrangement, providing spectral information along one axis and spatial information along the other axis. Full hyperspectral information is built up by capturing images in rapid succession while scanning over an area. This makes it operationally similar to a pushbroom camera, making it ideally suited for use on satellite systems. The individual acquisitions however have the advantage that each contains a meaningful 2D view of the area. The CHIEM hyperspectral sensor will also provide a “smart spectral solution” allowing to extract information from a selection of spectral bands through on chip & onboard processing. At present our consortium is developing a proto-flight model instrument with the aim to have an In Orbit Demonstration mission in 2020. This IOD mission acts as a precursor for a constellation of 10-15 hyperspectral satellites which would then give a global 2-3 days revisit time. With a spatial resolution of 20m it matches well the spatial resolution of the Sentinel-2 mission and can provide higher spectral resolution information at a short revisit time.

In parallel we have a development for a small satellites mission with an imager operating in the Thermal InfraRed wavelength regime. For agriculture the TIR observations are essential to measure the evapotranspiration through monitoring of the land surface temperature. Amongst other applications we mention here the interest for volcanology and fire detection. As was mentioned in the hyperspectral case, the high revisit time is crucial for several of the applications and a constellation of small satellites can provide this. In preparation of such a constellation of small satellites we are presently preparing an IOD mission with a TIR camera utilising uncooled microbolometer pixels array (640x480 pixels, 17 µm pitch). The planned GSD is 100 m and a swath of 48 km for a 600 km altitude orbit. For the future constellation a larger array of 1024x768 pixels is foreseen to provide a larger swath.

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In recent years, the proliferation of small satellites for remote sensing has made it possible to monitor daily change with high resolution imagery. Cubesats are often launched as constellations intended to operate as a unified whole, providing seamless coverage through space and time; however, imperfect normalization between sensors in a constellation can limit the interpretability of this data. One well-known provider of cubesat data is the San Francisco-based company Planet, which operates several constellations of remote sensing satellites. Their largest constellation, called “PlanetScope”, includes nearly 200 satellites capable of producing georeferenced 4-band multispectral imagery with daily global coverage. Using images from the PlanetScope constellation, we developed a generic method for normalizing variable time-series images using high quality reference imagery. This process proceeds in two main steps. First, no-change pixels between the target and reference images are identified. Second, a linear regression generates a transformation from the target image’s radiometry to that of the reference image. Optionally, the georeferencing of each image may be updated if displacement or distortion is suspected. These steps are applied to each image in the time-series. Once this set of normalized images is generated, a second process prepares the time-series for change analysis at either a pixel or polygon level. To simplify interpretation of the time-series, spectral indices are calculated for each image. The value of these spectral indices through time is despiked to remove the effects of undetected clouds and smoke. A smoothing filter is then applied to minimize changes such as lighting effects that may not have been eliminated by the normalization process. The result is a smooth trend of the spectral indices through time that captures significant and persistent changes at the pixel or polygon level. This method was tested in multiple sites around British Columbia and the Northwest Territories that experienced wildfires in 2017. Time-series of over 100 PlanetScope images per area were normalized using publically available multispectral satellite imagery as reference data. Changes due to fires were easy to detect in the normalized data, whereas they were obscured due to noise in the original PlanetScope time-series. While these test cases focused on detecting burned areas in PlanetScope data, it is important to note that this process is satellite- and index-agnostic; it may be used for any input images, any reference images, and any spectral index that may be calculated from the available bands. This method could be a cost-effective tool to normalize noisy cubesat time-series and prepare data for change detection applications. It may also help fuse the high spatial and temporal resolution of cubesat constellations with the consistency and calibration of traditional remote sensing satellites.

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Over the next few years, Europe will take important steps towards implementing the architecture of the Copernicus Space Component for Earth Observation (EO), fulfilling the needs of stakeholders concerned with land monitoring, marine monitoring, atmosphere monitoring, emergency management, security, and climate change.
Nowadays, constellations and distributed networks of satellites are emerging as clear development trends in the space system market to enable augmentation, enhancement, and possibilities of new applications for future EO Missions. It is of paramount importance for Europe to properly analyse these trends and assess whether or not they could provide a competitive advantage for EO systems.
The paper presents the mission analysis results of the H2020 ONION project, a European Union research activity that proposes a system concept to supplement in a progressive way the current European EO infrastructures and to serve emerging needs in an optimal fashion.
Among several use cases considered, the ONION project focussed on proposing system architectures to provide competitive revisit time, data latency and image resolution for two demanding application scenarios of interest: marine weather forecast (MWF) and agricultural hydric stress.
A set of promising system architectures has been subject of a comprehensive assessment, based on mission analysis expertise and detailed simulation for evaluating several key parameters such as revisit time and data latency of each measurement of interest, on-board memory evolution and power budget of each satellite of the constellation, ground station contacts and inter-satellite links. The architectures are built with several heterogeneous satellite nodes distributed in different orbital planes. Each platform can embark different instrument sets, which provide the required measurements for each use case.
A detailed mission analysis has then been applied to the selected architecture for the MWF use case, including refined data flow analysis to optimize system resources; refined power budget analysis; delta-V and fuel budget analysis considering all the possible phases of the mission, encompassing correction of launcher injection errors and acquisition of nominal satellite position inside the constellation, orbit maintenance to control altitude, collision avoidance to avoid collision with space debris objects and end-of-life disposal to comply with EOL guidelines.
The relevance of the system architecture selected for the MWF has been evaluated for 3 use cases of interest (Arctic sea-ice monitoring, maritime fishery pressure and aquaculture, agricultural hydric stress) to show the versatility and the feasibility of the chosen architecture to be adapted for other EO applications.

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The center developed an optical microsatellite. The concept of satellite design is low-cost, versatile and commercially viable. While retaining the advantages of traditional optical remote sensing, the satellite is also capable of super-resolution imaging and digital domain TDI.The satellite payload is an optical remote sensing camera. Based on the technology of space optical instruments such as traditional space camera, the camera is redesigned for space-borne sensitive devices. The camera integrates the working mechanism and control methods of various general types of image sensors with the composite linear array architecture and uses a new program-controlled technology, and developed a new type of multi-modality super CMOS sensor, which can perform multiple sampling modes.
The multi-modality super CMOS sensor, MS-CMOS for short, consists of several line CMOS sensors, which is similar to an array CMOS sensor structure. The signal is outputted by line CMOS sensor and every CMOS channel is independent. The hardware structure of the MS-CMOS imaging sensor is shown in the Fig 1.
MS-CMOS is a general-purpose device, the operating mode is adjustable on the satellite, or automatically adjusted according to the requirements of the user's. The imaging model can be represented by the following function:
I=F(N,M,α,T,t)
I: MS-CMOS imaging model;
N: number of line CMOS sensors number(longitudinal);
M: pixel number of line CMOS sensor(transverse);
α: CMOS sensor rotation Angle;
T: Sampling time of each line CMOS sensor;
t: Integration time.
When N =1, MS-CMOS can realize line CMOS sensor function.
When I₁= I₂=…=I, MS-CMOS can realize array CMOS sensor function.
When the rotation Angle does not equal to zero, MS-CMOS can sample in the tilting mode. In tilting mode, we can obtain multiple images which have regular dislocation in both line array and vertical array of the CMOS. Sampling time determines the sampling frequency. Suppose the sampling frequency is p, and the tangent value of angle is the simplest fraction. The functional relation between them is as follows.
tanα=a/b;
p=b/a.
When the sensor samples in tilting mode at a specific frequency and angle, the resulting multiple images can form a super resolution grid with equidistant encryption in the area with the highest overlap. by interpolating multiple images based on the grid, Super resolution reconstruction can be completed and satellite spatial resolution can be improved. As shown in Fig 2, the selected area is the super resolution grid.
According to the application requirements of the satellite，we selected 2 line CMOS sensors to form an array CMOS sensor, the rotation angle α is set to26.56° (arctan 1/2). In a triangle, the ratio of the opposite side to the adjacent side is 1:2. Tilting mode samples as an angle of 26.56, we can get obtain two images with 0.5 pixel displacement in the direction of CMOS sensor and 1 pixel displacement in the direction of vertical array. To ensure that the resolution ratio can be increased equally in the direction and distance direction of the satellite, sampling is carried out at twice the frequency. As shown in the Fig 3.
MS-CMOS is able to getting highly overlapped and fixed dislocation images at the same area during the tilting mode sampling. Based on the image features, the situation of limited computer processing capacity and limited resources, a method, designed for satellite-using, is proposed to improve the resolution of remote sensing image.
Since the computers on satellite have limited resources to use, complex algorithms cannot be used. The tilting mode sampling can obtain the image with regular dislocation overlapping. Considering the combination of image interpolation and sub-pixel algorithm, the super resolution interpolation reconstruction formula can be derived.
Suppose the result of super resolution reconstruction is H, the first line CMOS sensor imaging is L₁, and the second line CMOS sensor imaging is L₂, the interpolation model is as follows.
H（i,2j-1）=L₁ (i,j);
H（i,2j）=L₂ (i,j).
H(i,j)、L₁ (i,j)andL₂ (i,j): Corresponding pixel gray value.
The first and second line CMOS sensor images were magnified by 2 times in the array direction, and L₂ was shifted by 1 pixel in the array direction, and super resolution reconstruction results were obtained by interpolation.
According to the sampling mode, the image is distorted. The distortion is caused by the computer's internal image storage and display mechanism. In the image obtained by rotation Angle sampling, there is a mutual position relation between pixels. However, in the computer, the sampled image is output in RAW format, and the output values of each pixel are filled in turn without recording the position relation. Therefore, it is necessary to correct the deformation and invert the super fractal reconstruction results according to the rotation angle during sampling to obtain the final super resolution image.
Assuming that the super-resolution image is SR, H is the same as above, the correction process can be represented by the following function model.
SR(x,y)=F( α,H(x,y))
SR(x,y): The position coordinates of a pixel in super resolution image;
α: Rotation Angle of sensor;
H(x,y): The position coordinates of a pixel in super resolution reconstruction result.
We performed a simulation experiment of tilting mode sampling. Taking the target image as an example, the experimental results are as follows.
Target image is used to calculate the specific increase multiple of image resolution. Assuming that x represents the minimum number of three horizontal lines that can be clearly identified in the target image, y represents the minimum number of three vertical lines that can be clearly identified in the target image, the calculation can be represented by the following formula.
target resolution=√((x²+y²)/2)
Comparing the Fig 7,Fig 8,Fig 10 and Fig 11. The target images in Fig 7 and Fig 8 can distinguish the horizontal and vertical line pairs at the resolution of 19 meters, the target resolution is calculated as 19.00m according to the formula; the target images in Fig 10 and Fig 11 can distinguish the horizontal line pairs at the resolution of 9 meters, and distinguish the vertical line pairs at the resolution of 10 meters , the target resolution is calculated as 9.51m according to the formula. It is concluded that by using MS-CMOS and the algorithm, we can improve image resolution by about 2 times compared with conventional sampling.
The experimental results show that the combination of sensor and algorithm is feasible in engineering, which can effectively improve the azimuth and range resolution of satellite data. In practice, the satellite design orbit is 500km. Under the conventional sampling mode, the resolution of remote sensing image is 1.5m. When processed by the tilting mode sampling combined with the above algorithm, the resolution of image can be increased by at least √2 times and reach sub-meter resolution. By this way, it can improve the satellite spatial resolution without increasing the economic cost at the current manufacturing level.
Due to the limited resources on satellite, complex algorithms cannot be implemented. With the improvement of technology, operations such as de-noising and de-aliasing will also be completed on satellite to get a better effect. When the data is transmitted to the ground, by combined with other super resolution algorithms, the resolution of image can be further improved and the image information can be enriched.

Area C - Board 295

The European Space Agency (ESA) has contracted cosine measurement systems to highly integrate Thermal Infrared (TIR) technologies into a miniaturized Visible-Near-InfraRed (VNIR) hyperspectral imager able to fit a volume of less than two liters. The combination of hyperspectral VNIR and TIR in such a compact envelope is unprecedented. The imager is named HyperScout-2 as it will use the HyperScout-1 platform, that has flight heritage, as starting point to further integrate spectral channels.
HyperScout-2 will be used as in-orbit test-bed to benchmark the performance of such a miniaturized class of systems as well as to perform hands-on investigations to forecast the benefits of combining frequent measurements in the VNIR and TIR from nanosatellites, with less frequent but very accurate measurements performed by institutional satellites such as the Copernicus fleet.
HyperScout-2 will enable experimental programs to investigate the use of Artificial Intelligence (AI) for a variety of applications in the field of object detection and data inference, potentially beneficial also to the larger class satellites the Agency is developing as part of the Copernicus expansion element.
The first enabled product is the fusion of TIR imaging with hyperspectral data fully co-registered. Additional applications that will be investigated are: 1. high resolution by data fusion between hyperspectral and thermal, 2. evapotranspiration and agriculture applications, 3. thermal inertia and soil moisture, 4. Urban Heat Island, 5. Assisting search and rescue for pyroclastic flow/surge deposits, 6. Oil Spill, 7. Fire monitoring, 8. Underground fires and 9. Water quality.
The disruptive development makes use agile project management and risk mitigation approaches in order to catch the challenging schedule dictated by the main mission program. The project is targeting a flight window in the second quarter 2019, as part of the FSSCAT mission expected to complement the Copernicus Land and Marine Environment.
This contribution reports on a general description of HyperScout-2 as well as of the fast track program in which is implemented. We will also highlight the way the space asset will be exploited especially regarding the understanding of the potential of small systems deployed within small satellites constellation if integrated into ecosystems made of small and large systems.

Area C - Board 296

The SMOS mission, which is celebrating 10 years on orbit in 2019, was the first to carry a two dimensional interferometer operating at L-band. Since 2009 it has been continuously providing brightness temperature measurements, in full polarisation and for a wide range of incidence angles.
These measurements have been used succesfully to derive estimation and monitoring of soil moisture and ocean salinity, with unprecedented accuracy.
Closely following in SMOS footsteps, Aquarius and SMAP were launched in 2011 and 2015. Based on a different technology (real aperture instead on interferometry), they have both provided most valuable results, specially benefiting from better radiometric accuracy and radio frequency interferences (RFI) mitigation capability, that was not available for SMOS.
Still, all these instruments are limited by a spatial resolution of few tens of kilometers. In order to be able to make use of these data in hydrological models, and for many other applications, like the survey of water resources at the scale of irrigated zones, a better spatial resolution must be achieved. Consolidation studies of mission requirements have shown it should be improved by at least one order of magnitude.
This is the main goal of a distant future mission, that should be able to provide brightness temperature measurements at the 4km spatial resolution, with radiometric accuracy around 1 K, and significant range of incidence angles.
But the design of that future mission needs to cope with major challenges. For real aperture instruments, the size of the reflector would make it rapidly unpractical, and for interferometers the size of the baselines and number of antennas required to achieve both spatial resolution and radiometric accuracy also ends with a serious size problem.
The possibility to use a multi satellite instrument, faces another important challenge: the possibility to operate unconnected interferometer in orbit.
To solve this issue, CNES initiated a feasibility study for a demonstration mission, relying on at least two nanosatellites flying in close range formation (around 40m between satellites) and transmitting signals acquired by similar detectors onboard all satellites to ground to allow synchronization and correlation computation. The objectives of this mission are to demonstrate our ability to make use of these measurements to compute visibilities, usable to reconstruct brightness temperature maps. It will also demonstrate our ability to cope with synchronization problems between the detectors, accurate relative localization, and RFI mitigation.
Beside a functional breadboard of the acquisition chain is being developed and used to assess the performances of the synchronization method and optimize the design of the pseudo random code to use in this respect. It will also be used to assess the capability of the RFI mitigation selected algorithm for interferometry.
The status of this mission will be presented in this paper, along with results of simulations carried out to validate all the developed methods for synchronization and RFI mitigation as well as measured assessment realized with the breadboard.

Area C - Board 297