Our surface waters are under increasing pressures from global megatrends including rapid urbanisation, population growth, land use change and intensification[1,2]. The effects of these pressures are compounded by climate driven hydroclimatic events (floods and droughts)[3] resulting in an increasing frequency of spills into our river systems from wastewater networks through combined sewar overflows (CSOs) and numerous unregulated sources. This complex interaction of climate extremes with changes in the catchment result in multiple non-linear interactions impacting water quality, ecosystem health, biodiversity declines and carbon losses. The detection of unregulated spills is beyond the capability of conventional statutory river sampling campaigns and requires an entirely new approach in monitoring.
The last ten years has seen a new paradigm in the use of satellites for catchment scale monitoring. State-of-the-art Earth observation (EO) capability includes optical remote sensing of lakes for water quality[4,5,6,7] and synthetic aperture radar (SAR) at catchments scales for water quantity[8,9,10]. Lakes, reservoirs, estuaries and coastal environments are now largely observable from space. Lakes and reservoirs integrate the impacts of human activity within their catchments including nutrient inputs, pollution, and hydrological and hydromorphic modifications. However, river systems remain largely unobserved by the state-of-the-art Copernicus Earth observation water related services, due to the limitations in spatial resolving capabilities of existing Sentinel platforms. This omission hinders our ability to identify the sources of regulated and unregulated pollutants acting on these systems and our ability to strives towards our net zero goals and addresses the climate and biodiversity emergencies.
Next generation satellites now provide significant improvements in spatial resolution and temporal coverage, transforming their efficacy for monitoring smaller river systems. Planet’s SuperDove series now provide unprecedented optical imaging capability for water quality determination in river systems. Nevertheless, key challenges remain: i) many pollutants discharged into river systems, unless associated with changes in colour, are not optically observable, e.g. nutrient inputs (fertiliser runoff, animal wastes and human sewage), or other pollutants (heavy metals, plastics and organics); ii) the optical depth of river water may be greater than the physical depth of the river and thus bottom reflectance effects may remain difficult to resolve; and iii) light scattering from nearby land pixels into river pixels (the adjacency effect) may remain difficult to resolve in smaller river systems. However, a new generation of thermal imaging from space by SatVu will transform our ability to identify discharges and mixing of pollution incidents across the water continuum including our riverine environments.
The first of SatVu’s satellites HotSat-1 was launched in June 2023, with the second due in 2024. The final constellation of 7 satellites will offer up to 12 revisits per day at 3.5 m pixel resolution offering unprecedent time series intelligence on the complex thermal dynamics of waterbodies across the engineered and natural environments and the detection of unregulated discharges into river systems.
This PhD is the first to explore the use of HotSat capability in understanding complex hydrological interactions across the water continuum and the detection of unregulated discharges by: (1) developing robust atmospheric correction routines and emissivity coefficients to recover accurate thermal water temperature products[11]; and 2) exploit the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) to identify and distinguish unregulated discharges entering riverine environments from natural phenomena that will influence the observed surface water thermal signatures.
Methodology
This PhD will bring together next generation satellite Earth observation science from SatVu with cutting edge data analytics to develop new observation capabilities to facilitate the detection of unregulated discharge events. The PhD builds on a number of recent and existing research programmes between the Universities of Stirling and Glasgow, including: GloboLakes (NE/J024279/1, ÂŁ2.9M); NERC Hydroscape (NE/N006437/1, ÂŁ2.9M) and NERC MOT4Rivers (NE/X01620X/1; ÂŁ2M). The student will also benefit from the sensor deployment across the Forth Environmental Resilience Array (Forth-ERA), a regional scale digital observatory of the water continuum with near real-time data flows to support climate adaptation and mitigate the extreme effects of climate (drought and flood). Application to new test sites is available through the Pan European Research Infrastructure H-Europe funded DANUBIUS-RI.
Satellite based thermal imaging: Whilst surface water temperature detection has been extensively developed through the Sea and Land Surface Temperature Radiometers (SLSTR) on Sentinel 3[12] and MODIS[13], AVHRR [14] and Landsat 8[15], the opportunity to exploit HotSat’s capability at high temporal and spatial resolution will deliver a new paradigm in hydrological monitoring. Critical to the accurate retrieval of water temperature products is the atmospheric correction[11].
AI and ML in feature and event detection: The nature of the challenge here is the detection of anomalous signals (i.e., if different in character to the natural baseline). Given the high volumes of data being collected, manual detection of anomalies will not be possible, hence robust methods for automated anomaly detection (AD) are required[16]. A priori definition of the nature of the anomalies may be possible, but increasingly AI/ML methods are being developed which require no apriori definitions. The spatio-temporal nature of the satellite images will also require such methods to be trained accounting for the high spatial and temporal autocorrelations.
Project Timeline
Year 1
Development of the research proposal review and science plan. Deployment of sensors within the Forth-ERA use case study sites for algorithm development, calibration and validation. Cross comparison with existing platforms, e.g. Landsat 8 (TIRS) and Sentinel 3 SLSTR, over larger target areas. Training in data analytics including AI and ML techniques. Short placements with SatVu and SEPA.
Year 2
Atmospheric correction validation. Operational data processing with SatVu. Data interpretation through site visits, comparison with in-situ sensor data and other satellite data (e.g. Planet). Application of data analytics for event detection and placements with SEPA.
Year 3
Testing thermal products and event detection through new use cases, with opportunities to expand these internationally through DANUBIUS-RI. Development of technology implementation framework (with SatVu) for testing regulatory compliance and the detection of unregulated discharge events (with SEPA).
Year 3.5
Write up, submission and publication development
Training & Skills
The candidate will develop skills in thermal imaging algorithm development including atmospheric correction for the accurate retrieval of thermal data. The candidate will also support field campaigns and sensor deployment to support calibration and validation. The candidate will acquire data analytic skills and programming in Python and R.
The student will present their findings annually within a postgraduate research symposium specific to the universities of Stirling and Glasgow and international conferences. The student’s progress will be subject to annual progress reviews. All research students are members of Stirling Graduate School and are encouraged to attend seminars (that are particularly relevant to them) in addition to the generic training skills provided by the IAPETUSII DTP. Students also take advantage of the opportunities for networking with external visitors and students from other academic areas to promote interdisciplinarity.
References & further reading
[1] Whelan M.J., Linstead C., Worrall F., Ormerod S.J., Durance I., Johnson A.C., Johnson D., Owen M., Wiik E., Howden N.J.K., Burt T.P., Boxall A., Brown C.D. Oliver D.M., Tickner D. (2022), Is water quality in British rivers “better than at any time since the end of the Industrial Revolution”? Science of the Total Environment, 843, 157014.[2] Petrie B. 2021, A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management, Environmental Science and Pollution Research. 28, 32095–32110[3] Collet L., Harrigan S., Prudhomme C., Formetta G., Beevers L. (2018), Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment, Hydrology and Earth System Sciences, 22, 5387-5401.[4] Werther M, Odermatt D, Simis SGH, Gurlin D, Lehmann MK, Kutser T, Gupana R, Varley A, Hunter PD, Tyler AN & Spyrakos E (2022) A Bayesian approach for remote sensing of chlorophyll-a and associated retrieval uncertainty in oligotrophic and mesotrophic lakes. Remote Sensing of Environment, 283, 113295.[5] Pahlevan N, Mangin A, Balasubramanian SV, Smith B, Alikas K, Arai K, Barbosa C, Bélanger S, Binding C, Bresciani M, Giardino C, Hunter P, Simis S, Spyrakos E & Tyler A (2021) ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment, 258, 112366.[6] Neil C, Spyrakos E, Hunter PD & Tyler AN (2019) A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sensing of Environment, 229, pp. 159-178.[7] Spyrakos E, O’Donnell R, Hunter P, Miller C, Scott EM, Simis S, Neil C, Barbosa C, Binding C, Bradt S, Bresciani M, Dall’Olmo G, Giardino C, Gitelson A, Kutser T, Li L, Matsushita B, Martinez-Vicente V, Matthews M, Ogashawara I, Ruiz-Verdu A, Schalles J, Tebbs E, Zhang Y & Tyler A (2018) Optical types of inland and coastal waters. Limnology and Oceanography, 63, 846-870.[8] Balenzano A., Mattia F., Satalino G., a, Lovergine F.P., Palmisano D., Peng J., Marzahn P., Wegmüller U., Cartus O., Dąbrowska-Zielińska K., Musial J.P., Davidson M.W.J., Pauwels V.R.N., Cosh M.H., McNairn H., Johnson J.T., Walker J.P., Yueh S.H., Entekhabi D., Kerr Y.H., Jackson T.J., 2021, Sentinel-1 soil moisture at 1 km resolution: a validation study, Remote Sensing of Environment, 263, 112554.[9] Landuyt L., Wesemael A van., Schumann G.J.P., Hostache R., Verhoest N.E.C., Van Coillie F.M.B (2019) Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches, IEEE Transactions on Geoscience and Remote Sensing, 57, 722-739;[10] Salamon P., McCormick N., Reimer C., Clarke T., Marschallinger B.B., Wagner W., Martinis S., ChowC., Böhnke C., Matgen P., Chini M., Hostache R., Molini L., Fiori E., Walli A. (2021) The New, Systematic Global Flood Monitoring Product of the Copernicus Emergency Management Service, IEEE International Geoscience and Remote Sensing Symposium, 1053-1056;[11] Vanhellemont Q., Brewin R.J.W., Bresnahan P.J., Cyronak, T. (2022) Validation of Landsat 8 high resolution Sea Surface Temperature using surfers. Estuarine, Coastal and Shelf Science 265, 107650[12] Maberly SC, O’Donnell RA, Woolway RI, Cutler MEJ, Gong M, Jones ID, Merchant CJ, Miller CA, Politi E, Scott EM, Thackeray SJ & Tyler AN (2020) Global lake thermal regions shift under climate change. Nature Communications, 11, 1232[13] MacCallum and Merchant (2012) Surface water temperature observations of large lakes by optimal estimation, Canadian Journal of Remote Sensing 38, 25-45[14] Brewin R.J.W., Mora L de., Billson O., Jackson T., Russel P., Brewin T.G., Shutler L.D., Miller P.I., Taylor B.H., Smyth T.J., Fishwick J.R.(2017) Evaluating operational AVHRR sea surface temperature data at the coastline using surfers, Estuarine, Coastal and Shelf Science 196, 276-289[15] Vanhellemont, Q., (2020) Automated water surface temperature retrieval from Landsat 8/TIRS, Remote Sensing of Environment, 237, 111518[16] Kermorvant C, Liquet B, Litt G, Jones JB, Mengersen K, Peterson EE, Hyndman RJ, Leigh C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters (2021). International Journal of Environmental Research and Public Health, 18(23),12803.
