Science Group

Earth Observation Science & Applications

Improving the understanding and management of the ocean through the development and application of cutting-edge Earth Observation (EO) techniques and the pioneering of digital innovations in areas such as artifical intelligence (AI) and machine learning.

At a time when the sustainability of the ocean is under increasing threats from unprecedented and rapid changes, the ‘Earth Observation Science & Applications’ group is committed to harnessing new ways to monitor and evidence the changes and impacts taking place.

Building on PML's world-class expertise in satellite-based ocean colour remote-sensing and in-situ marine monitoring, this includes:

driving innovations in marine science, exploiting past, existing and new generation Earth Observation (EO) data.
exploiting the opportunities afforded by machine learning and artificial intelligence (AI) to address key environmental and societal questions
improving the synergy between in-situ, airborne and satellite observations to provide cost-effective monitoring of a variety of parameters (e.g. water quality, harmful algal blooms and eutrophication)
harnessing the capabilities of the latest Sentinel mission products, open-source tools and novel autonomous in-situ technologies.

The group's areas of focus include the ocean carbon cycle; phytoplankton dynamics, community structure and function, harmful algal bloom (HAB) detection, fisheries and aquaculture, coastal and inland water quality, links between ecosystem health and human health, and data-driven decision making.

The group also leads research into, and the production of, climate data records in ocean colour, sea-surface height, sea state, and lake-water colour, with a view to assessing the status of our oceans for policy-making.

PML Project pages

Latest publications

Ford, DJ; Tilstone, GH; Shutler, JD; Kitidis, V; Sheen, KL; Dall’Olmo, G; Orselli, IBM; 2023. Mesoscale Eddies Enhance the Air‐Sea CO 2 Sink in the South Atlantic Ocean . Geophysical Research Letters.
Cayetano, CB; Creencia, LA; Sullivan, E; Clewley, D; Miller, PI; 2023. Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model . UCL Open Environment.
De Keukelaere, L; Moelans, R; Knaeps, E; Sterckx, S; Reusen, I; De Munck, D; Simis, SGH; Constantinescu, AM; Scrieciu, A; Katsouras, G; Mertens, W; Hunter, PD; Spyrakos, E; Tyler, A; 2023. Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation . Remote Sensing.

Land, PE; Findlay, HS; Shutler, JD; Piollé, J-F; Sims, RP; Green, H; Kitidis, V; Polukhin, A; Pipko, II; 2023. OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons . Earth System Science Data.
Clason, C; Rangecroft, S; Owens, PN; Łokas, E; Baccolo, G; Selmes, N; Beard, D; Kitch, J; Dextre, RM; Morera, S; Blake, W; 2023. Contribution of glaciers to water, energy and food security in mountain regions: current perspectives and future priorities . Annals of Glaciology.
Lehmann, MK; Gurlin, D; Pahlevan, N; Alikas, K; Anstee, J; Balasubramanian, SV; Barbosa, CCF; Binding, CE; Bracher, A; Bresciani, M; Burtner, A; Cao, Z; Dekker, AG; Di Vittorio, C; Drayson, N; Errera, RM; Fernandez, V; Ficek, D; Fichot, CG; Gege, P; Giardino, C; Gitelson, AA; Greb, SR.; Henderson, H; Higa, H; Rahaghi, AI; Jamet, C; Jiang, D; Jordan, TM; Kangro, K; Kravitz, JA; Kristoffersen, AS; Kudela, R; Li, L; Ligi, M; Loisel, H; Lohrenz, S; Ma, R; Maciel, DA; Malthus, TJ; Matsushita, B; Matthews, MW; Minaudo, C; Mishra, DR; Mishra, S; Moore, T; Moses, WJ; Nguyễn, H; Novo, EMLM; Novoa, S; Odermatt, D; O’Donnell, DM; Olmanson, LG; Ondrusek, M; Oppelt, N; Ouillon, S; Pereira Filho, W; Plattner, S; Verdu, AR; Salem, SI; Schalles, JF; Simis, SGH; Siswanto, E; Smith, B; Somlai-Schweiger, I; Soppa, MA; Spyrakos, E; Tessin, E; Van der Woerd, HJ; Vander Woude, A; Vandermeulen, RA; Vantrepotte, V; Wernand, M; Werther, M; Young, K; Yue, L; 2023. GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality . Scientific Data.
Phongphattarawat, S; Bouman, HA; Lomas, MW; Sathyendranath, S; Tarran, GA; Ulloa, O; Zubkov, MV; 2023. Ecophysiological basis of spatiotemporal patterns in picophytoplankton pigments in the global ocean . Frontiers in Marine Science.
Abdulaziz, A; Sathyendranath, S; Vijayakumar, SK; Menon, N; George, G; Kulk, G; Raj, D; Krishna, K; Rajamohananpillai, R; Tharakan, B; Jasmin, C; Vengalil, J; Platt, T; 2023. The Distribution of Fecal Contamination in an Urbanized Tropical Lake and Incidence of Acute Diarrheal Disease . ACS ES&T Water.
Martin, N; Clewley, D; Groom, SB; 2023. Improving the performance of National Centre for Earth Observation (NCEO) code using GPUs . .
Jiang, D; Scholze, J; Liu, X; Simis, SGH; Stelzer, K; Müller, D; Hunter, P; Tyler, A; Spyrakos, E; 2023. A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes . International Journal of Applied Earth Observation and Geoinformation.