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

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.
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.
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.

Carrea, L; Cretaux, J-F; Liu, X; Wu, Y; Calmettes, B; Duguay, CR; Merchant, CJ; Selmes, N; Simis, SGH; Warren, M; Yesou, H; Müller, D; Jiang, D; Embury, O; Bergé-Nguyen, M; Albergel, C; 2023. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies . Scientific Data.
Courtecuisse, E; Marchetti, E; Oxborough, K; Hunter, PD; Spyrakos, E; Tilstone, GH; Simis, SGH; 2023. Optimising Multispectral Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae . Sensors.
Zolfaghari, K; Pahlevan, N; Simis, SGH; O'Shea, RE; Duguay, CR; 2022. Sensitivity of remotely sensed pigment concentration via Mixture Density Networks (MDNs) to uncertainties from atmospheric correction . Journal of Great Lakes Research.
Valente, A; Sathyendranath, S; Brotas, V; Groom, SB; Grant, MG; Jackson, T; Chuprin, A; Taberner, M; Airs, RL; Antoine, D; Arnone, R; Balch, WM; Barker, K; Barlow, R; Bélanger, S; Berthon, J-F; Beşiktepe, Ş; Borsheim, Y; Bracher, A; Brando, V; Brewin, RJW; Canuti, E; Chavez, FP; Cianca, A; Claustre, H; Clementson, LA; Crout, R; Ferreira, AS; Freeman, S; Frouin, R; Garcia-Soto, C; Gibb, SW; Goericke, R; Gould, R; Guillocheau, N; Hooker, SB; Hu, C; Kahru, M; Kampel, M; Klein, H; Kratzer, S; Kudela, R; Ledesma, J; Lohrenz, S; Loisel, H; Mannino, A; Martinez-Vicente, V; Matrai, P; McKee, D; Mitchell, BG; Moisan, T; Montes, E; Muller-Karger, F; Neeley, AR; Novak, M; O'Dowd, L; Ondrusek, M; Platt, T; Poulton, AJ; Repecaud, M; Röttgers, R; Schroeder, T; Smyth, TJ; Smythe-Wright, D; Sosik, HM; Thomas, C; Thomas, R; Tilstone, GH; Tracana, A; Twardowski, M; Vellucci, V; Voss, K; Werdell, J; Wernand, M; Wojtasiewicz, B; Wright, S; Zibordi, G; 2022. A compilation of global bio-optical in situ data for ocean colour satellite applications – version three . Earth System Science Data.
Sun, X; Brewin, RJW; Sathyendranath, S; Dall’Olmo, G; Airs, RL; Barlow, RG; Bracher, A; Brotas, V; Kheireddine, M; Lamont, T; Maranon, E; Moran, XAG; Raitsos, DE; Shen, F; Tilstone, GH; 2022. Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure . Remote Sensing of Environment.
Findlay, HS; Kurekin, A; Selmes, N; 2022. Assessment of the environmental, ecosystem, and human activities in coastal Vietnam and Cambodia gathered from satelitte remote sensing . PML Publishing.
Barlow, R; Lamont, T; Viljoen, J; Airs, RL; Brewin, RJW; Tilstone, GH; Aiken, J; Woodward, EMS; Harris, C; 2022. Latitudinal variability and adaptation of phytoplankton in the Atlantic Ocean . Journal of Marine Systems.