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.
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. Bio-geochemical 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.
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).
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.
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
