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

Burggraaff, O; Werther, M; Boss, ES; Simis, SGH; Snik, F; 2022. Accuracy and Reproducibility of Above-Water Radiometry With Calibrated Smartphone Cameras Using RAW Data . Frontiers in Remote Sensing.
Kurekin, A; Miller, PI; Avillanosa, AL; Sumeldan, JDC; 2022. Monitoring of Coastal Aquaculture Sites in the Philippines through Automated Time Series Analysis of Sentinel-1 SAR Images . Remote Sensing.
Werther, M; Odermatt, D; Simis, SGH; Gurlin, D; Jorge, DSF; Loisel, H; Hunter, PD; Tyler, AN; Spyrakos, E; 2022. Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs . ISPRS Journal of Photogrammetry and Remote Sensing.

Jordan, TM; Martin, C; Brisbourne, AM; Schroeder, DM; Smith, AM; 2022. Radar Characterization of Ice Crystal Orientation Fabric and Anisotropic Viscosity Within an Antarctic Ice Stream . Journal of Geophysical Research: Earth Surface.
Jordan, TM; Simis, SGH; Grötsch, PMM; Wood, J; 2022. Incorporating a Hyperspectral Direct-Diffuse Pyranometer in an Above-Water Reflectance Algorithm . Remote Sensing.
Richter, I; Roberts, BR; Sailley, SF; Sullivan, E; Cheung, VV; Eales, J; Fortnam, M; Jontila, JB; Maharja, C; Nguyen, THa; Pahl, S; Praptiwi, RA; Sugardjito, J; Sumeldan, JDC; Syazwan, WM; Then, AY; Austen, MC; 2022. Building bridges between natural and social science disciplines: a standardized methodology to combine data on ecosystem quality trends . Philosophical Transactions of the Royal Society B: Biological Sciences.
Simis, SGH; Hunter, PD; Matthews, MW; Spyrakos, E; Tyler, A; Vaiciute, D; 2022. Improved hyperspectral inversion of aquatic reflectance under non-uniform vertical mixing . Optics Express.
Clark, DR; Rees, AP; Ferrera, CM; Al-Moosawi, L; Somerfield, PJ; Harris, C; Quartly, GD; Goult, S; Tarran, GA; Lessin, G; 2022. Nitrite regeneration in the oligotrophic Atlantic Ocean . Biogeosciences.
Pahlevan, N; Smith, B; Alikas, K; Anstee, J; Barbosa, CCF; Binding, CE; Bresciani, M; Cremella, B; Giardino, C; Gurlin, D; Fernandez, V; Jamet, C; Kangro, K; Lehmann, MK; Loisel, H; Matsushita, B; Hà, N; Olmanson, L; Potvin, G; Simis, SGH; VanderWoude, A; Vantrepotte, V; Ruiz-Verdu, A; 2022. Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 . Remote Sensing of Environment.
Huthnance, JM; Hopkins, J; Berx, B; Dale, A; Holt, J; Hosegood, P; Inall, M; Jones, S; Loveday, BR; Miller, PI; Polton, J; Porter, M; Spingys, C; 2022. Ocean shelf exchange, NW European shelf seas: Measurements, estimates and comparisons . Progress in Oceanography.