This PDF file contains the front matter associated with SPIE Proceedings Volume 11888, including the Title Page, Copyright information, and Table of Contents.

Climate change represents the biggest global threat of the 21st century. This has been widely recognized and is currently responded to by major international initiatives, summarizing the most pressing, globally relevant requirements in addressing the effects of a changing climate, such as the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement, the UN’s “2030 Agenda for Sustainable Development” and the Sendai Framework for Disaster Risk Reduction 2015–2030. The European Space Agency (ESA) is already addressing a large number of the requirements that respond to the above main drivers for climate action through being a main developer of European Earth Observation (EO) capabilities to deliver climate science and services. ESA’s satellites provide the global view, enabling the science community to detect signs of change, identify significant trends and constrain the models to predict the future. Through its role as a major provider of systematic and global climate observations ESA interacts with a number of international organisations, stakeholders and users, within the climate landscape that are working toward strengthening the scientific understanding and projection of climate and addressing the consequences of future change. One of the keystones of ESA’s climate activities is the Climate Change Initiative (CCI), which has been running for more than 10 years and is led by the ESA Climate Office. This unique scientific effort involves ca. 450 world-leading experts across ESA Member States to generate global multi-mission and multi-decadal datasets satisfying the requirements for 22 Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), on behalf of UNFCCC. These datasets have fully characterised uncertainties and are validated using independent, traceable, in-situ measurements. They provide an impartial yardstick to understand climate processes and to improve and validate climate models, thereby enhancing the quality, credibility and exploitation of model predictions. In association with Earth System Models (ESMs), CCI data also provide the observational record to study drivers, interactions and feedbacks due to climate change, as well as reservoirs, teleconnections, tipping points, global energy, water and carbon budgets and other Earth-system cycles, etc. The scientific results of the CCI programme, published in more than 900 papers to date, are a major contribution to the physical science base of IPCC Assessment Reports. The keynote will provide an assessment on the challenges and opportunities that space-borne data provide in our quest to tackle climate change, and will focus on the current and future satellite missions developed by ESA, underlying data storage and processing systems and how users will be able to integrate such data into potential scientific and commercial applications.

Sustainability in space is often discussed, but as a community we are only gradually learning what it actually means. To inform this understanding, a set of three parallel projects ran at Cranfield University (Oct 2020 to Mar 2021) to develop a scenario of sustainable use of the geostationary orbit region. The three projects were to develop mission designs for (a) a Scavenger spacecraft equipped with tools, actuators and sensors to perform rendezvous with selected satellites at their end of life, to harvest selected parts and components (i.e. solar panels, radiators, antenna reflectors), store and deliver them to the Recycler for refurbishment or recycling, (b) a Recycler space station located in GEO, capable of receiving parts and materials obtained by the Scavenger spacecraft and performing a range of inspection, recycling and repurposing operations on them, and (c) a candidate customer mission: a huge communications satellite based on the Airbus VASANT (VASt ANTenna) concept, with two antenna arrays, each 35 m square, sized to be able to communicate directly from GEO to mobile phone users at Earth's surface. Some of the features highlighted by these studies are (a) the technical challenges of reusing parts from old satellites: modularity and design-for-reuse seem to be key enablers, (b) the advanced robotics and autonomy implied by the on-orbit operations, (c) the challenge of long-term orbit control without excessive propellant consumption, and (d) although the technology is challenging, there are major non-technical challenges for the business case and for aspects such as the legal use of debris, liability for accidents, and compliance with any regulations. Sustainability is challenging, but nature leaves us no alternative.

Satellites have become essential tools for providing information for weather forecasts, monitoring agriculture and studying climate. In recent years satellite lidar (laser ranging) has come of age, with three missions launched in 2018. These spaceborne lidars are collecting ground-breaking data. They are the only current in-orbit technology capable of directly measuring bare Earth topography under vegetation and of making non-saturating measurements of forest biomass, height and cover. However, the energy requirements of a lidar lead to very sparse coverage. NASA's GEDI mission, the densest sampling lidar yet, is expected to directly sample between 2-4% of the Earth's surface, leading to sampling errors and preventing its use in applications that require continuous coverage without interpolation. The coverage of a lidar satellite is controlled by how much laser power they can emit, collect and make a usable measurement from. This is controlled by the payload power, telescope collecting area, energy per shot needed for an accurate measurement, laser efficiency and detector efficiency. Initial calculations using currently in-orbit technology suggest that global, continuous coverage could be achieved within 5 years at 30 m resolution using ten ICESat-2 class lidar satellites. This would be prohibitively expensive. The Global Altimeter MISSion: GLAMIS project aimed to see whether recent developments in photonics, deployable optics and small-satellites could make continuous coverage lidar more cost-effective. All current lidar satellites use a solid-state laser. These produce short, powerful bursts of light but are only ~5% efficient. Tapered laser diodes are 25% efficient, but cannot emit the same amount of energy in as short a burst as solid state lasers can. Diode laser would need to spread the energy over a longer time, using pulse compressed lidar (PCL) to allow measurements, which would lead to very different noise behaviour to sold state lasers, possibly preventing their use in spaceborne lidar. A satellite lidar simulator was used to determine that PCL is suitable for satellite lidar over a range of biomes. The mirror size and cost for fixed and deployable telescopes were estimated for three different satellite size classes; a 12U satellite, a 150 kg satellite and a 500 kg satellite. The electric power and cost of each of these satellites was also estimated and used, along with the laser efficiencies, to calculate the lidar coverage each could achieve. Orbital constellation simulations were used to determine how many satellites of each configuration would be needed to achieve global coverage within a given timeframe, accounting for loss due to clouds. It was found that deployable optics would allow a more cost effective coverage than fixed optics. It was also found that the larger satellites would be a more cost-effective solution than smaller satellites. In conclusion, using tapered laser diodes in pulse compressed mode and deployable optics are promising technologies to increase the coverage of satellite lidar. For a future constellation of lidar satellites capable of covering the whole Earth, the optimum configuration was found to be a constellation of eleven 150 kg (roughly microwave sized) satellites fitted with foldable mirrors.

The danger posed by human-created, orbital debris has been well documented and there is a broad consensus that, if unchecked, such debris poses a significant threat to the future of space activity. Besides, there has been much discussion on the difficulties inherent in trying to remove large, non-functioning satellites from Earth orbit. However, while the physical aspects of the space domain make this a unique technical challenge and never before has society had to face the problem of decommissioning redundant infrastructures on this scale, the issues faced by the space community in tackling the economic and legal difficulties of this environmental threat are not without terrestrial parallels. The nuclear industry is also well versed in dealing with the decommissioning of potentially harmful assets that have reached the end of their operational lifespan, and has developed considerably in the last decades across disciplines and approaches. Nevertheless, only very recently there have been some attempts to investigate decommissioning and develop lessons learned across sectors. In this paper, we address this topic, and we highlight the similarities that characterise these sectors, also presenting the magnitude of the nuclear and space decommissioning challenges. Moreover, we investigate to what extent the nuclear and outer space decommissioning industries can learn from each other. Results include considerations for the future such as: • the importance of promoting decommissioning as an exciting and socio-environmentally responsible industry for new generations; • the need for “design for decommissioning” to be implemented since the early stage(s) of a project; • policies specific to the end-of-life of assets and infrastructure that focus on financial obligations of the entities responsible for decommissioning.

Space, Satellites, and Sustainability: Keynote Presentation III

Grasslands are the most widespread terrestrial ecosystem in the United Kingdom (UK). They represent a critical carbon (C) reservoir and provide forage and fodder to millions of livestock. Quantifying how management and climate affect grassland C dynamics is key to achieving climate-resilient farming, to shaping and monitoring data-informed policies, and to transitioning to a zero-C agri-food sector. To this end, remote sensing systems provide information on grassland vegetation in near-real time, across large domains and at high resolution. Biogeochemical models use our continuously-developing knowledge on ecosystem functioning to describe ecosystem C dynamics and the effects of weather and human management on them. Field measurements of C pools and fluxes provide direct ground observations of grassland C losses and gains. The combination of earth and ground observations with modelling represents a robust way for quantifying, monitoring and verifying grassland ecosystem C stocks. This presentation demonstrates our current capabilities in regards to this. We have developed and tested a model-data fusion (MDF) framework that uses earth observation data (Proba-V and Sentinel-2) on vegetation canopy (leaf area index) to infer vegetation management (grazing, cutting) and inform a validated process-based model of field-scale C dynamics (DALEC-Grass). The framework was applied for 2017-2018 at 1855 grassland fields that were sampled from across the UK. The MDF-predicted livestock density per area and the corresponding agricultural census-based data had a correlation coefficient (r) of 0.68. The MDF-predicted annual yield (harvested and cut biomass) was within the range of relevant measured data and reflected the variation of grassland management intensity across the UK. On average, the simulated grasslands were C sinks in 2017 and 2018 but the 2018 European summer heatwave resulted in a 9-fold increase in the number of simulated fields that were C sources in 2018 compared to 2017. We argue that earth observation data can be used in a MDF framework to monitor grassland vegetation management and to simulate its impacts on the C balance of any UK grassland field as well as to attribute changes in annual C balance to human activities and weather anomalies.

Expansion of forests and woodlands are key elements of global strategies to capture carbon from the atmosphere and therefore mitigate climate change. Fundamental to the successful planning and management of woodland conservation and restoration is the ability to map accurately the spatial extent and character of woodland ecosystems. Satellite remote sensing increasingly provides a powerful tool to facilitate these monitoring efforts at scale. However, woodland cover in Scotland is highly fragmented, with marked differences in structure between the remnant native woodlands and more common commercial plantation systems. In addition, the landscape is topographically complex and frequently shrouded in cloud. These factors pose challenges for satellite remote sensing. Therefore, the capacity to characterise fragmented woodland cover accurately in such landscapes remains uncertain. In this contribution, we assess the extent to which trees can be mapped at 10m spatial resolution using a combination of openly available Sentinel 1 C-band radar backscatter data and Sentinel 2 multi-spectral imagery in fragmented woodland landscapes in NW Scotland. We assess the predictive accuracy of the resultant classifiers using a spatially rigorous site-level cross-validation across six sites of varied woodland cover, and explore the role of canopy characteristics and topography in modulating the accuracy with which trees are detected. We find that the accuracy with which we can detect trees is strongly dependent on the stand structure. Trees are mapped more accurately in dense woodland (≥50% tree cover) than more open woodlands (≥20% tree cover), and especially compared to isolated trees. Accuracy also varied with topography, with highest accuracies in flat terrain and reduced accuracies on steeper slopes. These results demonstrate clear potential for integrating Sentinel satellite monitoring systems within woodland management frameworks, while highlighting the importance of reporting context-dependent accuracy statistics with remotely sensed maps of tree or forest cover.

Farmers are under increasing pressure to manage agricultural resources in a more sustainable and efficient manner. Information on crop nitrogen (N) status can be used to support variable rate fertiliser applications. Furthermore, yield forecasts can aid the logistical planning of harvest operations and ameliorate any negative economic impacts on food supply chains. Complex crop models simulating crop N dynamics and yields often require extensive model inputs that are seldom available. By combining observations of leaf area index (LAI) with a process-based crop model, this research presents a novel and scalable analytical solution for generating robust daily estimate of wheat N and yield, which can be applied at the sub-field scale. The crop model, DALEC-Crop, is a carbon cycle model that simulates the key processes involved in crop growth and development in response to daily meteorological observations. The model was first calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha^-1 for two consecutive growing seasons. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 11%). Using these developments at the plot scale, the model yield estimates had a high agreement with observations (mean R² = 0.7 and NRMSE = 7%) when applied at the sub-field scale across field sites under the constraints of Sentinel-2 data. Although additional field sites and seasons are required for further testing, the modelling approach could be feasibly applied to estimate yields across large areas with only minimal inputs.

The purpose of this work is to solve an important issue: the light scattering problem for ice crystals of cirrus clouds less than 10 μm and matching the obtained solution with the existing solution obtained within the physical optics approximation. The article presents a solution to the problem of light scattering by hexagonal ice particles of cirrus clouds with sizes from 0.05 to 5.17 μm for a wavelength 0.532 μm, obtained within the discrete dipole approximation. It is found that the obtained solution is in good agreement with the physical optics approximation in the vicinity of scattering angles of 0–10° (the vicinity of forward direction scattering). However, to solve the problem of light scattering in the vicinity of the backward scattering direction, which is important for the interpretation of lidar data, it is necessary to continue the calculations to sizes of the order of 20 μm. The results obtained are necessary for constructing algorithms for the interpretation of lidar data obtained by sounding cirrus clouds.

Ursa Space Systems (Ursa Space) has developed an approach to effective pre- and post-storm monitoring utilizing its extensive virtual constellation of synthetic aperture radar (SAR) satellites and its advanced analytics platform. SAR provides a reliable day or night earth observation data source that can cut through cloud cover for a clear view of an area with very limited atmospheric effects on image collection. To begin the process of hurricane monitoring, Ursa Space provides an initial understanding of a vulnerable region by examining a time series of low-resolution SAR data and generating historical change maps, which we then analyze to detect trends and infer significant change events. Based on these preliminary results, we task high-resolution SAR data for further investigation. Ahead of the hurricane, we strategically task imagery using expected storm tracks and other weather-forecasting data to provide a baseline for postevent flood mapping. After the storm passes, we continue to collect data to monitor the region, generating fused data layers describing storm damage and standing water with additional land-use land-cover (LULC) context. With specially tasked imagery, we can also examine soil moisture and other detailed ground disturbance through interferometric SAR (InSAR). The feasibility and significance of Ursa Space’s approach to pre- and post-storm monitoring is demonstrated through retrospective analysis of two storms, Hurricane Irma and Hurricane Dorian.

Habitat mapping is key to meeting land management and conservation objectives, from supporting estimation of natural capital in a landscape to monitoring habitat change over time. Current land management challenges, including conserving rare species and habitats, illustrate a growing need for spatially extensive and rapidly updatable biodiversity information; a need that can best be met through remotely sensed imagery combined with the remarkable data processing potential of machine learning. We assess the potential for optical satellite data to classify complex upland habitat types in the Scottish Highlands, following two UK national habitat classification frameworks. We explore how the differences in spatial and spectral resolution of satellite sensors affects the accuracy of derived habitat maps. Specifically, we contrast the performance of open-source Sentinel-2 data (20 m spatial resolution) against higher spatial-resolution data from the commercial Worldview-2 satellite (0.5 m resolution). We then compare the results produced with these satellite datasets against equivalent results obtained with high-resolution (25 cm) colour airborne photographs, to better inform users on the utility of available optical data before subsequent analysis, e.g. natural capital assessments, in comparable settings. We demonstrate that high-fidelity habitat maps (93% overall accuracy) can be produced using high resolution optical satellite data (Worldview-2). This level of accuracy exceeded that of maps derived from airborne surveys (~75%) and is deemed sufficient to be useful to ecologists in-situ. In contrast, the capacity of Sentinel-2 data was considerably more limited (~50% overall accuracy). This highlights the importance of spatial resolution for characterising habitat mosaics at scale, especially in settings such as upland Scotland where shifts in habitat and species composition of importance to land managers may occur at relatively fine length scales (<10m). Provided high spatial resolution optical data is available, the framework developed is scalable to a national scale, therefore, facilitating effective land management strategies

The estimation of the Earth's surface temperature (LST) from the infrared thermal radiation (TIR) emitted by the Earth detected by means of remote sensing has allowed a giant leap in climate analysis. The specialized literature has highlighted the singular importance of the LST in the generation of the Urban Heat Island (UHI), especially at night. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems. On the one hand, the scarcity of meteorological stations that allow obtaining the air temperature (Ta) with an adequate degree of spatial resolution. And, on the other, due to the limited temporal availability of medium / high resolution nocturnal satellite images that make it possible to know the LST at night. Traditional methods for obtaining nocturnal UHI have been directed either to extrapolation of data from weather stations, or obtaining Ta through urban transects. In the first case, the lack of weather stations in urban landscapes makes it extremely difficult to obtain data to extrapolate and propose models at a detailed resolution scale. In the second case, there is a manifest difficulty in obtaining data simultaneously and significantly representative of urban and rural zones. Remote sensing images are another methodology used to measure nighttime UHI, but the greatest limitation of this method is the scarcity of high-resolution images that allow rigorous nighttime LST to be obtained. Only MODIS, or Sentinel 3, offer free mid-resolution nighttime thermal imaging for LST and UHI analysis. Furthermore, the integration of LST (obtained from remote sensing imagery) with Ta (obtained from weather stations) continues to be a pending challenge. The right estimation of the temperature of the air at ≈ 2-m height above ground (Ta) from LST is possible but complex. The vertical lapse rate to be applied is function of the surface energy balance, which varies in function of the nature of the surface and of the instant of the day. The objective of this paper is to integrate the information derived from the thermal band of satellite images (LST) with the "in situ" measurements of the Ta obtained at meteorological stations. The methodology used consists, first, in developing a model by means of multi-regression analysis of the night air temperatures, using as explanatory variables, in addition to the physical characteristics of the territory (longitude, latitude, altitude, distance to the sea, slope, orientation), the characteristics derived from vegetation (NDVI), urbanization (NDBI) as well as the LST obtained by means of MODIS. And, secondly, downscaling the previous model, using the information obtained from Landsat 8. In this way, a set of models is obtained at different resolutions that allow estimating the nighttime temperature at a detailed level. The case study is the Metropolitan Area of Barcelona (636 km², 3.3 million inhabitants).