NUEVAS TECNOLOGÍAS DIGITALESPARA PAMPA AZUL

Gestión de los datos en océanos y mares

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AUTORIDADES

Presidente de la Nación Dr. Alberto Á. FERNÁNDEZ

Ministro de Ciencia, Tecnología e Innovación

Dr. Roberto C. SALVAREZZA

Secretario de Planeamiento y Políticas en Ciencia, Tecnología e Innovación

Dr. Diego HURTADO

Subsecretario de Estudios y Prospectiva

Mag. Eduardo E. MALLO

Director Nacional de Estudios

Dr. Ing. Martín VILLANUEVA

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PRODUCIDO POR

Programa Nacional de Vigilancia Tecnológica e Inteligencia Competitiva (VINTEC)

Dirección Nacional de Estudios

Subsecretaría de Estudios y Prospectiva

Ministerio de Ciencia, Tecnología e Innovación de la Nación

AUTORES

● Nancy Pérez

● Miguel Guagliano

● Adriana Sánchez Rico

● Lidia Seratti

● Martín Villanueva

Buenos Aires, septiembre de 2020

Se permite el uso o la copia en cualquier formato siempre y cuando no se alteren los contenidos y se haga reconocimiento de autoría y edición, previa comunicación por escrito, informando el fin específico de su utilización a: estudiosyprospectiva@mincyt.gob.ar

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CONTENIDO

1. PRESENTACIÓN .................................................................................................................................................................. 6

2. AVANCES CIENTÍFICOS ..................................................................................................................................................... 6

3. AVANCES TECNOLÓGICOS ............................................................................................................................................. 10

3.1 Análisis de solicitantes líderes ........................................................................................................................................ 13

4. AVANCES CIENTÍFICOS Y TECNOLÓGICOS EN ARGENTINA .................................................................................. 19

5. PUBLICACIONES CIENTÍFICAS ....................................................................................................................................... 25

6. PROYECTOS DE I+D+i ...................................................................................................................................................... 31

7. ANEXO METODOLÓGICO ............................................................................................................................................... 32

7.1 Patentes de invención ...................................................................................................................................................... 32

7.2 Publicaciones científicas................................................................................................................................................... 33

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TECNOLOGÍAS 4.0 Y DESARROLLO SOSTENIBLE EN LOS OCÉANOS: LA ÚLTIMA FRONTERA

Ing. LIDIA SERATTI

El objetivo 14° de la Agenda 2030 para el Desarrollo Sostenible pide a los Estados miembros de la ONU que “conserven y utilicen de forma sostenible los océanos, los mares y los recursos marinos”, y establece diez metas para impulsar la acción.

La escasez de información sobre el uso y gestión de los océanos limita la comprensión de las condiciones oceánicas, el uso de recursos y los impactos. Las tecnologías de información 4.0 representan una oportunidad para que las naciones trabajen en colaboración científica para sostener el desarrollo sustentable de los mares compartidos.

Los océanos son decisivos para el clima global y los riesgos naturales relacionados. Una información más profunda sobre las ciencias oceánicas, impulsada por sistemas mejorados de observación e intercambio de datos, ayudarán a la comprensión y al modelado del planeta. La aparición de nuevas plataformas para seleccionar y combinar diversos conjuntos de datos, así como las nuevas herramientas analíticas -incluida la inteligencia artificial y el aprendizaje automático para extraer la información-, hace que esta información sea útil para los tomadores de decisiones, ya sean gobiernos, empresas, o comunidades.

A fin de allanar el camino para hacer de la Ciencia Marina una disciplina de datos, se requiere mejorar la adquisición, el manejo y la gestión de datos; incorporar sensores inteligentes; mejorar la eficiencia de la transferencia de datos y disponer de medios de análisis y toma de decisiones en tiempo real. También se debe incrementar la interoperabilidad y la accesibilidad; mejorar la compartición de datos entre industrias, gobiernos y sociedad civil; e incrementar el uso de analítica de big data, entre otros. Todo esto requiere entrenamiento especializado e incremento de la colaboración entre las partes.

La propuesta europea de un gemelo digital con todos los datos históricos y actuales del mar integraría tecnologías de observación en una red oceánica de Internet de las cosas, dentro de la cual los datos estarían disponibles con procesamiento en tiempo real utilizando inteligencia artificial y computación en la nube. (European Marine Board, 2019).

Esta emergente avalancha de información es el llamado "océano digital". Las principales áreas de acción son conocidas, como la reducción de la sobrepesca y de la contaminación, junto a la protección de hábitats y especies en un clima cambiante. La primera Conferencia de las Naciones Unidas sobre los Océanos, celebrada en junio de 2017, fue enérgica al respecto.

El proyecto Pampa Azul, creado en 2014 y relanzado en 2020, es una iniciativa interministerial argentina que promueve el conocimiento científico, el desarrollo tecnológico y la innovación sostenible en el Atlántico Sur, a la vez que promueve en la población mayor conciencia sobre el patrimonio marítimo y el uso responsable de sus recursos, facilitando con la información el ejercicio de la soberanía y la seguridad en los mares1.

Las innovaciones sostenibles representan una oportunidad para comprender y administrar mejor los recursos del mar. Es necesario manejar este potencial de innovación y gestionar sus riesgos. Los océanos son bienes públicos. No sin causa, en la gesta de sustentabilidad del planeta, el mar es la última frontera.

1 http://www.pampazul.gob.ar/se-relanzo-la-iniciativa-pampa-azul/

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1. PRESENTACIÓN

El presente Informe de “TECNOLOGÍAS APLICADAS A LA CIENCIA DE OCÉANOS”, ha sido elaborado a partir de fuentes de información secundaria en las siguientes categorías: publicaciones científicas (Scopus), patentes de invención (PatentInspirations2) y proyectos de I+D+i.

El alcance del reporte se limita a la búsqueda y al análisis de información relacionada con un conjunto de tecnologías aplicadas a la gestión de los datos en océanos y mares: artificial intelligence, machine learning, deep learning, internet of things, cloud computing, big data, data mining y data science, sensores, drones, rastreadores GPS, vehículos autónomos y no tripulados, cámaras submarinas, sistemas de identificación automática, radares de alta frecuencia, boyas inteligentes, redes de mallas inalámbricas, telemetría animal, sistemas de información geográfica (GIS), registradores de presión oceánica y de mares, hidrófonos submarinos, sonido navegación rango, oceanografía y cartografía satelital, microscopios oceánicos, reconocimientos de imágenes submarinas, monitoreo en tiempo real, estructuras de monitoreo de arrecifes artificiales y teledetección.

2. AVANCES CIENTÍFICOS

A partir de consultar la base de datos de publicaciones SCOPUS, se encontraron 5.296 registros relacionados con el objeto de la búsqueda. A continuación, se muestran algunos indicadores que describen la evolución durante el periodo 2018-2020 en materia de producción científica. En el año 2018, se publicaron 1.920 documentos, en el año 2019, 2.178, y al mes de julio del año 2020, 1.194 documentos (figura 1).

Figura 1. Evolución de investigaciones por año

Fuente: elaboración propia a partir de Scopus.

Las principales revistas científicas donde más se publicó en los últimos años son: Remote Sensing (277), Proceedings of SPIE. The international Society for Optical Engineering (168), Remote Sensing

2 El motor de búsqueda PatentInspiration, contiene documentos de patentes de la Oficina Europea de Patentes (EPO) y de la United

States Patent and Trademark Office (USPTO). https://www.patentinspiration.com/

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of Enviroment (123), Journal of Coastal Research (118) y en Frontiers in Marine Science (104) (figura 2).

Figura 2. Cantidad de publicaciones revistas científicas de Scopus

Fuente: elaboración propia a partir de Scopus.

Las instituciones con más publicaciones son Chinese Academy of Sciences (China, 276), National Oceanic and Atmospheric Adminstration (USA, 152), CNRS Centre National de la Recherche Scientifique (Francia, 149), University of Chinese Academy of Sciences (China, 113), Nasa Goddard Space Fligth Center (USA, 112) (figura 3).

Figura 3. Instituciones de investigación líderes en cantidad de publicaciones

Fuente: elaboración propia a partir de Scopus.

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Los países que lideran el ranking con mayor cantidad de investigaciones son Estados Unidos (1.501), China (1.351), Reino Unido (404), Francia (353) e Italia (304). Argentina presenta 18 publicaciones (figura 4).

Figura 4. Países líderes en investigación

Fuente: elaboración propia a partir de Scopus.

En la figura 5, se observa el porcentaje de publicaciones por disciplina, donde Ciencias Terrestres y Planetarias representa el 24,3%, Ingeniería 15,9%, Ciencias Computacionales 11,8%, Ciencias Ambientales 11,3 % y Ciencias Agrícolas y Biológicas 8,7%.

Figura 5. Porcentaje de publicaciones por disciplinas

Fuente: elaboración propia a partir de Scopus.

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En la figura 6, de acuerdo con lo declarado en las publicaciones, se pueden identificar las organizaciones patrocinadoras de la investigación. Las principales son: National Natural Science Foundation of China (China, 659), National Aeronautics and Space Administration (EEUU, 290), National Science Foundation (EEUU, 216), National Basic Research Program of China (China, 167) y National Oceanic and Atmospheric Administration (EEUU, 158).

Figura 6. Patrocinadores

Fuente: elaboración propia a partir de Scopus.

De acuerdo con el conteo de los conceptos claves (keywords) indexados por parte de los autores y por parte de las revistas en la base de datos SCOPUS, a continuación, se presentan los que aparecen con mayor frecuencia en el corpus de publicaciones identificado (figura 7).

Figura 7. Conceptos claves

Fuente: elaboración propia a partir de Scopus.

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3. AVANCES TECNOLÓGICOS

A partir de la búsqueda de información en la base de datos de patentes PatentInspiration, se realizó una exploración para conocer la cantidad de solicitudes y patentes concedidas hasta el mes de julio del año 2020 en relación con algunas tecnologías aplicadas a la gestión de los datos en océanos y mares (ver Presentación).

Es importante remarcar que la cantidad total de solicitudes y patentes concedidas encontradas en el período del año 2018 al mes de julio del 2020 en el motor de búsqueda PatentInspiration, limitando al objeto de búsqueda, arrojó un total de 808 documentos, los cuales se convierten en 745 al filtrarlos por familia de patentes3.

Del total de 808 documentos, el 68% se trata de solicitudes en estado de presentación y el 32% son patentes concedidas (gráfico 1).

Gráfico 1. Solicitudes de patentes vs patentes concedidas

Fuente: elaboración propia a partir de PatentInspiration.

A continuación, se muestran algunos indicadores claves que describen la evolución de la producción tecnológica durante el periodo 2018-2020. El nivel de publicaciones de patentes en el periodo de búsqueda ha sido bastante irregular, marcando en algunos meses picos de crecimiento y en otros, de decrecimiento. Se observa que en el año 2019 hubo mayor cantidad de picos de crecimiento en términos de solicitudes y patentes concedidas (figura 8). Los valores correspondientes al año 2020 se muestran de forma parcial hasta el mes de julio inclusive, fecha de cierre del presente informe.

3 Una familia de patentes está conformada por las solicitudes presentadas en distintos países con una misma fecha de prioridad.

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Figura 8. Tendencia mensual de solicitudes y patentes concedidas a nivel mundial

Fuente: elaboración propia a partir de PatentInspiration.

Para conocer los países en donde se presentaron la mayor cantidad de solicitudes de patentes, se observaron las familias de solicitudes a nivel mundial.

Entre los países con mayor cantidad de solicitudes y patentes concedidas se destacan: China (571), Corea del Sur (80), Estados Unidos (64), Rusia (17), Japón (8), Reino Unido (5), Canadá (3), Noruega (3), Australia (3), Malasia (2), Francia (2) y Dinamarca (2). A nivel latinoamericano, solo aparece México con un solo registro (figura 9). Por otra parte, dentro de las solicitudes presentadas a nivel mundial, hay 30 solicitudes que han sido presentadas vía Tratado de Cooperación de Patentes Internacional (PCT) y 14 solicitudes presentadas en la Oficina Europea de Patentes.

Figura 9. Solicitudes y patentes concedidas presentadas por país

Fuente: elaboración propia a partir de PatentInspiration.

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Los cinco solicitantes líderes (universidades, centros de investigación, empresas, personas, otros) son: Ocean University of China – China (29), Harbin Engineering University – China (23), Zhejiang University – China (15), Wuhan University of Technology - China (15) y Navico Holding AS – Noruega (9).

Figura 10. Nube de palabras: solicitantes de patentes

Fuente: elaboración propia a partir de PatentInspiration.

Entre el listado de solicitantes, si se observa solamente a las compañías que más solicitudes han presentado, se encuentran: Navico Holding AS – Noruega (9), Fairfield Industries Inc. – EE.UU. (8), Buffalo Automation Group INC. – EE.UU. (5), Magseis FF LLC – Noruega (5) y Cytroniq CO LTD. - Corea del Sur (4).

Los cinco campos tecnológicos con los cuales se relacionan la mayor cantidad de documentos de patentes son: sistemas de boyas (109), sistemas de posicionamiento por radiobaliza por satélite (84), topografía especialmente adaptada a aguas abiertas (66), métodos para leer o reconocer caracteres impresos o escritos o para reconocer patrones (62) y control de posición (55).

Figura 11. Principales campos tecnológicos relacionados a las solicitudes de patentes

Fuente: elaboración propia a partir de PatentInspiration.

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3.1 Análisis de solicitantes líderes

A continuación, se muestran los títulos de las solicitudes presentadas por los solicitantes líderes identificados en el análisis de los documentos de patentes.

Solicitante: OCEAN UNIVERSITY OF CHINA (29 REGISTROS)

TÍTULO FECHA DE

PUBLICACIÓN ESTADO EVOLUCIÓN

Unmanned aerial vehicle active radar hyperspectral detection system and method for marine environmental monitoring

09/01/2020 SOLICITUD

Storm surge calculation method based on artificial intelligence and numerical model

02/12/2019 SOLICITUD

Ocean in-situ pCO2 sensor 28/11/2019 SOLICITUD

Ocean drifting buoy device for measuring temperature and air pressure

28/10/2019 CONCEDIDA

High-sensitivity lever mechanism for profile buoy system and buoy system

17/10/2019 CONCEDIDA

System for large-scale marine equipment recyclying and networking method and recyclying method thereof

15/08/2019 SOLICITUD

Method for identifying saury fishing boat by using remote sensing night light data

08/07/2019 SOLICITUD

A marine observation big data visualization analysis method based on a complex network

27/06/2019 SOLICITUD

A log storage method and system for a marine data cluster

13/06/2019 SOLICITUD

Marine big data sharing distribution risk control model and method

10/06/2019 SOLICITUD

Split-type marine boundary layer observation device and method

30/05/2019 SOLICITUD

Marine big data sensitivity evaluation system and a marine big data sensitivity prevention method for confidentiality requirements

23/05/2019 SOLICITUD

Autonomous underwater vehicle 16/05/2019 SOLICITUD

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navigation data analysis method based on complex network construction

Split load rejection type marine boundary layer observation device and method

09/05/2019 CONCEDIDA

Fuzzy PID motion control method of underwater robot based on ESO

22/04/2019 SOLICITUD

Marine drifting buoy device capable of measuring temperature and air pressure

18/04/2019 SOLICITUD

Deep-water intelligent navigation method based on water velocity assisted inertial navigation

04/04/2019 SOLICITUD

Underwater vehicle navigation system and navigation method

14/03/2019 SOLICITUD

Lever mechanism for wave-energy profile buoy system and buoy system

28/02/2019 SOLICITUD

Intelligent buoy networking simulation method and system for ocean phenomena

15/11/2018 SOLICITUD

Ocean current energy generation integrated synchronous tracking monitor

16/08/2018 SOLICITUD

Recovery of supporting many taiwan straits ocean beacon machine to retrieve is quick –witted

18/06/2018 CONCEDIDA

Ocean communication base station buoy based on iridium satellite and beidou satellite communication

28/06/2018 CONCEDIDA

Under acting on, inlays cyclic loading rock steel -pipe pile loading test device

26/03/2018 CONCEDIDA

Analysis technology for time-continuous flow field structure based on objective Euler coherent structure

08/03/2018 SOLICITUD

Rossby wave identification and extraction technology based on global marine remote sensing data

08/03/2018 SOLICITUD

Recycling machine and method capable of recycling multiple marine beacons

05/03/2018 SOLICITUD

Marine observation machine people 01/03/2018 CONCEDIDA

Improved automatic extracting technology of parabola-type Lagrangian coherent structure

04/01/2018 SOLICITUD

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Solicitante: HARBIN ENGINEERING UNIVERSITY (23 REGISTROS)

TÍTULO FECHA DE

PUBLICACIÓN ESTADO EVOLUCIÓN

Flying airfoil towfish driven by ocean currents

13/01/2020 SOLICITUD

Underwater beacon positioning method of underwater vehicle

02/01/2020 SOLICITUD

Moon pool array float wave energy conversion device self-adapted to sea conditions

18/11/2019 SOLICITUD

Device and method for assessing disturbances, on underwater robots, of sea waves

26/09/2019 SOLICITUD

Marine ship time sequence data analysis method based on deep learning

08/09/2019 SOLICITUD

Ship replenishment method based on Beidou relative positioning and unmanned aerial vehicle control technologies

13/06/2019 SOLICITUD

Wave buoy main wave direction computation method based on vertical-horizontal dip angle measurement outlier correction

13/06/2019 SOLICITUD

Wave buoy main wave direction calculation method based on big wave screening strategy

13/06/2019 SOLICITUD

Wavelet buoy main wave direction calculation method based on wave height weighting and vector averaging

30/05/2019 SOLICITUD

Hybrid energy green ocean detector 09/05/2019 SOLICITUD

Compensating type transverse mooring very-low frequency vector acoustic horizontal submersible buoy

06/05/2019 SOLICITUD

Wave direction buoy main wave direction calculation method based on counting proportion weighted average

15/04/2019 SOLICITUD

Calculating method for main wave direction of wave direction buoy based on optimization classification and vector average

11/04/2019 SOLICITUD

Blind deconvolution method for underwater acoustic channel based on

01/04/2019 SOLICITUD

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convex optimization

Energy-saving A satellite route planning method considering ocean current and unmanned surface vehicle dynamic influence

18/03/2019 SOLICITUD

Sea cucumber detection and binocular visual positioning method based on deep learning

22/11/2018 SOLICITUD

Maritime search and rescue positioning method based on multi-sensor network

10/09/2018 SOLICITUD

Unmanned vessel ant colony energy consumption optimal global path planning method based on electronic nautical chart

16/08/2018 SOLICITUD

Method for recognizing underwater acoustic communication modulation mode based on deep learning technique

14/05/2018 SOLICITUD

Unmanned aerial vehicle remote sensing-based wave and floating body movement forecasting system

18/01/2018 CONCEDIDA

Ship marine operation assistant decision support system based on mobile terminal

11/01/2018 CONCEDIDA

Navigation simulation system of underwater-operation-level ROV and control method thereof

11/01/2018 SOLICITUD

Real-time forecasting system of ocean floating body motion based on ocean wave image remote sensing and artificial intelligence

04/01/2018 SOLICITUD

Solicitante: ZHEJIANG UNIVERSITY (15 REGISTROS)

TÍTULO FECHA DE PUBLICACIÓN

ESTADO EVOLUCIÓN

Visual underwater box type sampler and control method thereof

04/11/2019 SOLICITUD

Method for predicting vegetation wave absorption based on machine learning

31/10/2019 SOLICITUD

Static ocean water color satellite data reconstruction method based on empirical orthogonal function decomposition method

24/10/2019 SOLICITUD

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Multi-node data synchronous acquisition system and method for real-time monitoring of underwater surface deformation

23/10/2019 SOLICITUD

Oceanic earthquake and tsunami real-time monitoring system

10/10/2019 SOLICITUD

Ocean hydrometeorological information monitoring system based on Beidou short messages and unmanned ships

29/08/2019 SOLICITUD

Method for detecting density distribution of micro-plastics in soft tissues of marine organisms

17/01/2019 CONCEDIDA

SAR radar marine ship target identification system

03/12/2018 SOLICITUD

Space-air-ground marine emergency network communication system

01/11/2018 SOLICITUD

SIO (Swarm Intelligence Optimization) SAR marine ship object recognition system

08/10/2018 SOLICITUD

Intelligent SAR radar marine ship target identification system

03/09/2018 SOLICITUD

Electrically floating mark is sent out to ocean wave energy based on piezo electric effect

20/08/2018 CONCEDIDA

Near-ultraviolet image processing based detection and early-warning method for hazardous chemical substances in off-shore marine site

05/07/2018 SOLICITUD

Formula of independently ging forward section subsurface buoy

11/06/2018 CONCEDIDA

Independent-advancing cross-section subsurface buoy

31/05/2018 SOLICITUD

Solicitante: WUHAN UNIVERSITY OF TECHNOLOGY (15 REGISTROS)

TÍTULO FECHA DE

PUBLICACIÓN ESTADO EVOLUCIÓN

Multiple-ROV (remotely operated vehicle) cooperation-based underwater unknown motion target detecting system

04/11/2019 SOLICITUD

Maritime personnel in distress search system capable of being carried on

17/10/2019 SOLICITUD

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unmanned aerial vehicle and target recognition method

Maritime unmanned aerial vehicle video image defogging method based on deep learning

04/07/2019 SOLICITUD

Unmanned aerial vehicle berthing platform system and method based on universal gimbal ring

20/06/2019 SOLICITUD

A ship domestic sewage discharge monitoring device and an application method thereof

27/12/2018 SOLICITUD

Multifunctional buoy based on multisource energy supply coupled self power generation

06/12/2018 SOLICITUD

Marine frozen products insulated container monitoring system

01/11/2018 CONCEDIDA

Intelligent controller and control method of marine solar sail

18/10/2018 SOLICITUD

Unmanned aerial vehicle automatic grabbing and dropping device for maritime search and rescue

17/09/2018 SOLICITUD

System and method for fault prediction and on-line evaluation of health state of marine diesel generator

10/09/2018 CONCEDIDA

Combined system and working method of offshore floating power generation platform and unmanned boat cluster

03/09/2018 SOLICITUD

Maritime oil contamination monitoring system and method based on unmanned aerial vehicle and BP neural network

06/08/2018 SOLICITUD

Maritime affair radar image processing method based on deep learning

21/06/2018 SOLICITUD

Boats and ships with towed earthquake surveying device

09/04/2018 CONCEDIDA

Marine long -range automatic ground tackle

08/02/2018 CONCEDIDA

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Solicitante: NAVICO HOLDING AS (9 REGISTROS)

TÍTULO FECHA DE

PUBLICACIÓN ESTADO EVOLUCIÓN

Marine electronic device for presentment of sonar images

05/02/2020 SOLICITUD

Marine electronic device for presentment of nautical charts and sonar images

20/11/2019 SOLICITUD

Wireless trolling motor assembly 23/10/2019 SOLICITUD

Base station for marine display 25/09/2019 SOLICITUD

Sonar transducer having geometric elements

28/08/2019 SOLICITUD

Sonar transducer performance optimization

17/04/2019 SOLICITUD

Alert zones for a marine environment 02/01/2019 SOLICITUD

Systems and Associated Methods for Generating a Fish Activity Report Based on Aggregated Marine Data

28/02/2018 SOLICITUD

Trip Replay Experience 28/02/2018 CONCEDIDA

4. AVANCES CIENTÍFICOS Y TECNOLÓGICOS EN ARGENTINA Con el propósito de visibilizar el aporte de la investigación argentina, se identificaron las siguientes 15 publicaciones que cuentan con participación nacional.

Nº AUTORES TÍTULO AFILIACIONES AÑO FUENTE

1

Renosh P.R., Doxaran D., De Keukelaere L., Gossn J.I.

Evaluation of atmospheric correction algorithms for sentinel-2-MSI and sentinel-3-OLCI in highly turbid estuarine waters

Laboratoire d'Océanographie de Villefranche, UMR7093, CNRS-Sorbonne Université, Villefranche-sur-Mer, France; Flemish Institute for Technological Research (VITO), Belgium; Instituto de Astronomía y Física del Espacio (IAFE), CONICET-Universidad de Buenos Aires, Argentina

2020 Remote Sensing

20

2

Torregroza-Espinosa A.C., Restrepo J.C., Correa-Metrio A., Hoyos N., Escobar J., Pierini J., Martínez J.-M.

Fluvial and oceanographic influences on suspended sediment dispersal in the Magdalena River Estuary

Departamento de Gestión Industrial, Agroindustrial y de Operaciones, Corporación Universidad de la Costa CUC, Barranquilla, Colombia; Departamento de Física y Geociencias, Universidad del Norte, Barranquilla, Colombia; Instituto de Geología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico; Departamento de Historia y Ciencias Sociales, Universidad del Norte, Barranquilla, Colombia; Departamento de Ingeniería Civil y Ambiental, Universidad del Norte, Barranquilla, Colombia; Center for Tropical Paleoecology and Archaeology, Smithsonian Tropical Research Institute, Panama City, Panama; Comisión de Investigaciones Científicas (CIC) — IADO-CONICET, Bahía Blanca, Argentina; GET, UMR5563, CNRS/IRD/Université Toulouse 3, Toulouse, France

2020 Journal of Marine Systems

3 Raya Rey A., Huettmann F.

Telecoupling analysis of the Patagonian Shelf: A new approach to study global seabird-fisheries interactions to achieve sustainability

Centro Austral de Investigaciones Científicas y Técnicas, Consejo Nacional de Investigaciones Científicas y Técnicas (CADIC-CONICET), Tierra del Fuego, Ushuaia, Argentina; Instituto de Ciencias Polares, Ambiente y Recursos Naturales, Universidad Nacional de Tierra del Fuego, Tierra del Fuego, Ushuaia, Argentina; Wildlife Conservation Society, Buenos Aires, Argentina; EWHALE Lab- Institute of Arctic Biology, Biology & Wildlife Department, University of Alaska Fairbanks (UAF), Fairbanks, AK, United States

2020 Journal for Nature Conservation

4

Ozafrain S., Roncagliolo P.A., Muravchik C.H.

Likelihood map waveform tracking performance for gnss-r ocean altimetry

Sistemas Electrónicos de Navegación y Telecomunicaciones, Facultad de Ingeniería, Universidad Nacional de la Plata, La Plata, Argentina; Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales (LEICI; UNLP-CONICET) and CIC-PBA, La Plata, Argentina

2019

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

5

Christiansen F., Sironi M., Moore M.J., Di Martino M., Ricciardi M., Warick H.A., Irschick D.J., Gutierrez R., Uhart M.M.

Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics

Aarhus Institute of Advanced Studies, Aarhus C, Denmark; Zoophysiology, Department of Bioscience, Aarhus University, Aarhus C, Denmark; Harry Butler Institute, Murdoch University, Murdoch, WA, Australia; Southern Right Whale Health Monitoring Program, Puerto Madryn, Argentina; Instituto de Conservación de Ballenas, Buenos Aires, Argentina; Diversidad Animal II, Universidad Nacional de Córdoba, Córdoba, Argentina; Biology Department, Woods Hole Oceanographic Institution, Wood Hole, MA, United States; Department of Biology, University of Massachusetts, GTZ Designs, Nashville, TN, United States; School of Veterinary Medicine, University of California Davis, Davis, CA, United States

2019 Methods in Ecology and Evolution

6

Wang S., Kinnison D., Montzka S.A., Apel E.C.,

Ocean Biogeochemistry Control on the Marine

Advanced Study Program, National Center for Atmospheric Research, Boulder, CO, United States; Atmospheric Chemistry Observations and Modeling Laboratory, National Center for

2019

Journal of Geophysical Research: Atmospheres

21

Hornbrook R.S., Hills A.J., Blake D.R., Barletta B., Meinardi S., Sweeney C., Moore F., Long M., Saiz-Lopez A., Fernandez R.P., Tilmes S., Emmons L.K., Lamarque J.-F.

Emissions of Brominated Very Short-Lived Ozone-Depleting Substances: A Machine-Learning Approach

Atmospheric Research, Boulder, CO, United States; Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, United States; Department of Chemistry, University of California, Irvine, CA, United States; Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, United States; Department of Atmospheric Chemistry and Climate, Institute of Physical Chemistry Rocasolano, CSIC, Madrid, Spain; National Research Council (CONICET), FCEN-UNCuyo, UNT-FRM, Mendoza, Argentina

7

Menna B.V., Villar S.A., Acosta Lazo G.G.

Navigation System for MACÁBOT an Autonomous Surface Vehicles Using GPS Aided Strapdown Inertial Navigation System

INTELYMEC-CIFICEN-CONICET, Departamento de Ingeniería Electromecánica, Facultad de Ingeniería, Universidad Nacional Del Centro de la Provincia de Buenos Aires, Olavarría, Argentina

2019 IEEE Latin America Transactions

8

Camiolo M.D., Cozzolino E., Dogliotti A.I., Simionato C.G., Lasta C.A.

An empirical remote sensing algorithm for retrieving total suspended matter in a large estuarine region [Algoritmo empírico de sensoramiento remoto para recuperar material particulado en suspensión en una gran región estuarial]

Universidad Provincial del Sudoeste (UPSO), Subsede Coronel Pringles, Buenos Aires, Argentina; Subprograma de Sensoramiento Remoto, Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Instituto de Astronomía y Física del Espacio (IAFE), CONICET, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Buenos Aires, Argentina; Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET/UBA), Buenos Aires, Argentina; Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI/IFAECI/CNRS/CONICET/UBA), Buenos Aires, Argentina; Departamento de Ciencias de la Atmósfera y los Océanos, FCEN, Universidad de Buenos Aires, Argentina

2019 Scientia Marina

9

Roemmich D., Alford M.H., Claustre H., Johnson K.S., King B., Moum J., Oke P.R., Owens W.B., Pouliquen S., Purkey S., Scanderbeg M., Suga T., Wijffels S.E., Zilberman N.,

On the future of Argo: A global, full-depth, multi-disciplinary array

Scripps Institution of Oceanography, University of California, San Diego, United States;, Laboratoire d'océanographie de Villefranche (LOV), France; Monterey Bay Aquarium Research Institute (MBARI), United States; National Oceanography Centre, University of Southampton, United Kingdom; Oregon State University, United States; CSIRO Oceans and Atmopshere, Australia; Woods Hole Oceanographic Institution, United States; Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), France; Tohoku University, Japan; University of East Anglia, United Kingdom; Atlantic Oceanographic and Meteorological Laboratory

2019 Frontiers in Marine Science

22

Bakker D., Baringer M.O., Belbeoch M., Bittig H.C., Boss E., Calil P., Carse F., Carval T., Chai F., Conchubhair D.O., D'Ortenzio F., Dall'Olmo G., Desbruyères D., Fennel K., Fer I., Ferrari R., Forget G., Freeland H., Fujiki T., Gehlen M., Greenan B., Hallberg R., Hibiya T., Hosoda S., Jayne S., Jochum M., Johnson G.C., Kang K.-R., Kolodziejczyk N., Koertzinger A., Le Traon P.Y., Lenn Y.-D., Maze G., Mork K.A., Morris T., Nagai T., Nash J., Garabato A.N., Olsen A., Pattabhi R.R., Prakash S., Riser S., Schmechtig C., Shroyer E., Sterl A., Sutton P., Talley L., Tanhua T., Thierry V., Thomalla S., Toole J., Troisi A., Trull T., Turton J.D., Velez-Belchi P.J., Walczowski W., Wang H., Wanninkhof R., Waterhouse A., Watson A., Wilson C.,

(NOAA), United States; JCOMMOPS, France; University of Maine, United States; Fundação Universidade Federal do Rio Grande, Brazil; Met Office, United Kingdom; State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), China; Second Institute of Oceanography, State Oceanic Administration, China; Marine Institute, Ireland; Plymouth Marine Laboratory, United Kingdom; Dalhousie University, Canada; University of Bergen, Norway; Massachusetts Institute of Technology, United States; Department of Fisheries and Oceans (Canada), Canada; Japan Agency for Marine-Earth Science and Technology, Japan; UMR8212 Laboratoire des Sciences du Climat et de l'Environnement (LSCE), France; Fisheries and Oceans Canada, Bedford Institute of Oceanography (BIO), Canada; Geophysical Fluid Dynamics Laboratory (GFDL), United States; The University of Tokyo, Japan; Niels Bohr Institute, Faculty of Natural and Life Sciences, University of Copenhagen, Denmark; Pacific Marine Environmental Laboratory (NOAA), United States; Korea Meteorological Administration, South Korea; Université de Bretagne Occidentale, France; GEOMAR Helmholtz Center for Ocean Research Kiel, Germany; Mercator Ocean (France), France; Bangor University, United Kingdom; Norwegian Institute of Marine Research (IMR), Norway; South African Weather Service, Marine Research Institute, Faculty of Science, University of Cape Town, South Africa; Tokyo University of Marine Science and Technology, Japan; Indian National Centre for Ocean Information Services, India; University of Washington, United States; OSU Ecce Terra, Centre National de la Recherche Scientifique (CNRS), France; Royal Netherlands Meteorological Institute, Netherlands; National Institute of Water and Atmospheric Research (NIWA), New Zealand; Southern Ocean Carbon and Climate Observatory (SOCCO), South Africa; Naval Hydrography Service, Argentina; Spanish Institute of Oceanography, Oceanographic Center of Vigo, Spain; Polish Academy of Sciences, Poland; Xiamen University, China; University of Exeter, United Kingdom; National Marine Fisheries Service (NOAA), United States; School of Oceanography, University of Washington, United States; State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), China; Atmosphere and Ocean Research Institute, University of Tokyo, Japan

23

Wong A.P., Xu J., Yasuda I.

10

Newman L., Heil P., Trebilco R., Katsumata K., Constable A.J., Wijk E., Assmann K., Beja J., Bricher P., Coleman R., Costa D., Diggs S., Farneti R., Fawcett S., Gille S.T., Hendry K.R., Henley S.F., Hofmann E., Maksym T., Mazloff M., Meijers A.J., Meredith M.P., Moreau S., Ozsoy B., Robertson R., Schloss I.R., Schofield O., Shi J., Sikes E.L., Smith I.J., Swart S., Wahlin A., Williams G., Williams M.J., Herraiz-Borreguero L., Kern S., Lieser J., Massom R., Melbourne-Thomas J., Miloslavich P., Spreen G.

Delivering sustained, coordinated and integrated observations of the Southern Ocean for global impact

Southern Ocean Observing System, Institute for Marine and Antarctic Studies, College of Sciences and Engineering, University of Tasmania, Australia; Australian Antarctic Division, Australia; Antarctic Climate and Ecosystems Cooperative Research Centre, Australia; Research and Development Centre for Global Change, Japan Agency for Marine-Earth Science and Technology, Japan; Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; Department of Marine Sciences, University of Gothenburg, Sweden; British Oceanographic Data Centre (BODC), United Kingdom; Institute for Marine and Antarctic Studies, College of Sciences and Engineering, University of Tasmania, Australia; University of California, Santa Cruz, United States; Scripps Institution of Oceanography, University of California, San Diego, United States; International Centre for Theoretical Physics, Italy; Department of Oceanography, Faculty of Science, University of Cape Town, South Africa; University of Bristol, United Kingdom; School of Geosciences, University of Edinburgh, United Kingdom; Old Dominion University, United States; Woods Hole Oceanographic Institution, United States; British Antarctic Survey (BAS), United Kingdom; Norwegian Polar Institute, Norway; Istanbul Technical University, Turkey; Xiamen University, Malaysia; CONICET Centro Austral de Investigaciones Científicas (CADIC), Argentina; Universidad Nacional de Tierra del Fuego, Argentina; Department of Marine and Coastal Sciences, School of Environmental and Biological Studies, Rutgers State University of New Jersey, United States; Physical Oceanography Laboratory, Ocean University of China, China; Department of Physics, Division of Sciences, University of Otago, New Zealand; National Institute of Water and Atmospheric Research (NIWA), New Zealand; Centre for Southern Hemisphere Oceans Research (CSIRO), Australia; Integrated Climate Data Center, University of Hamburg, Germany; Departamento de Estudios Ambientales, Universidad Simón Bolívar, Venezuela; Institute for Environmental Physics, University of Bremen, Germany

2019 Frontiers in Marine Science

11

Williams G.N., Solís M.E., Esteves J.L.

Satellite-measured phytoplankton and environmental

Laboratorio de Oceanografía Química y Contaminación de Aguas (LOQyCA), Centro para el Estudio de Sistemas Marinos (CESIMAR), Consejo Nacional de Investigaciones Científicas y Técnicas

2018

Plankton Ecology of the Southwestern Atlantic: From the

24

factors in north Patagonian Gulfs

(CONICET), Puerto Madryn, Chubut, Argentina Subtropical to the Subantarctic Realm

12

Cabrerizo M.J., Carrillo P., Villafañe V.E., Medina-Sánchez J.M., Helbling E.W.

Increased nutrients from aeolian-dust and riverine origin decrease the CO2-sink capacity of coastal South Atlantic waters under UVR exposure

Estación de Fotobiología Playa Unión, Rawson, Chubut, Argentina; Facultad de Ciencias, Departamento de Ecología, Universidad de Granada, Granada, Spain; Instituto Universitario de Investigación del Agua, Universidad de Granada, Granada, Spain; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

2018 Limnology and Oceanography

13

Pickering C., Rossi S.D., Hernando A., Barros A.

Current knowledge and future research directions for the monitoring and management of visitors in recreational and protected areas

Environment Futures Research Institute, Griffith University, Australia; Instituto Argentino de Nivología y Glaciología y Ciencias Ambientales (IANIGLA), Centro Científico Tecnológico (CCT) CONICET, Mendoza, Argentina

2018

Journal of Outdoor Recreation and Tourism

14

Bouman H.A., Platt T., Doblin M., Figueiras F.G., Gudmundsson K., Gudfinnsson H.G., Huang B., Hickman A., Hiscock M., Jackson T., Lutz V.A., Mélin F., Rey F., Pepin P., Segura V., Tilstone G.H., Van Dongen-Vogels V., Sathyendranath S.

Photosynthesis-irradiance parameters of marine phytoplankton: Synthesis of a global data set

Department of Earth Sciences, University of Oxford, Oxford, OX1 3AN, United Kingdom; Plymouth Marine Laboratory, Prospect Place, The Hoe, PL1 3DH, United Kingdom; Plant Functional Biology and Climate Change Cluster, Faculty of Science, University of Technology Sydney, Australia; Instituto de Investigaciones Marinas (CSIC), Spain; Marine Research Institute, Reykjavík, 121, Iceland; State Key Lab. of Marine Environmental Science/Key Laboratory of Coastal and Wetland Ecosystems, Ministry of Education, Xiamen University, Xiamen, Fujian, 361005, China; Ocean and Earth Science, University of Southampton, National Oceanography Centre Southampton, European Way, Southampton, United Kingdom; United States Environmental Protection Agency, Ariel Rios Building, 1200 Pennsylvania Avenue, Washington, DC 20460, United States; Instituto Nacional de Investigacion y Desarrollo Pesquero, Mar del Plata, Argentina; European Commission, Joint Research Centre, Ispra, 21027, Italy; Institute of Marine Research, C/o Department of Biological Sciences, University of Oslo, P.O. Box 1066, Oslo, 0316, Norway; Fisheries and Oceans Canada, Northwest Atlantic Fisheries Centre, P.O. Box 5667, St John's, NL A1C 5X1, Canada; National Centre for Earth Observation, Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, United Kingdom

2018 Earth System Science Data

25

15

Carreto J.I., Carignan M.O., Montoya N.G., Cozzolino E., Akselman R.

Mycosporine-like amino acids and xanthophyll-cycle pigments favour a massive spring bloom development of the dinoflagellate Prorocentrum minimum in Grande Bay (Argentina), an ozone hole affected area

Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Argentina 2018

Journal of Marine Systems

En el caso de las patentes de invención, con la búsqueda realizada en PatentInspiration, no se identificaron registros en Argentina.

5. PUBLICACIONES CIENTÍFICAS

PERSISTENT DATA GAP IN OCEAN COLOR OBSERVATIONS OVER THE EAST CHINA SEA IN WINTER: CAUSES AND RECONSTRUCTIONS - (PERSISTENTE BRECHA DE DATOS EN LAS OBSERVACIONES DEL COLOR DEL OCÉANO SOBRE EL MAR DE CHINA ORIENTAL EN INVIERNO: CAUSAS Y RECONSTRUCCIONES)

Lin, T., He, Q., Zhan, W., Zhan, H - Remote Sensing Letters - 2020

Abstract: The instrument used for ocean colour remote-sensing works in the visible wavelengths, and the presence of clouds frequently lead to invalid observations. The formation of clouds is known to be influenced by mesoscale oceanic processes (e.g., eddies and temperature fronts), but these influences are often overlooked in missing data reconstructions. By analysing more than 10 years of chlorophyll-a (chl-a) images from satellite observations, we found an area with persistent data missing in the Eastern China Sea during winter. This area stretches from 123° E and 28° N to 126° E and 34° N, with a width of approximately 100 km. The data gap is closely related to the wintertime sea surface temperature fronts, with higher cloud coverage and thus more data missing on their warm sides. This implies that the chl-a values in the persistent data gap may be consistently lower than those on the cold side, so the spatial-based interpolation has a tendency to overestimate them. We applied three data interpolation methods to fill the data gap. Two methods that count temporal relevancies, i.e., the temporal linear interpolation and the data interpolating empirical orthogonal functions (DINEOF) interpolation, perform better than the Kriging interpolation which only considers spatial autocorrelation. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

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CONTRASTING CHLOROPHYLL-A SEASONAL PATTERNS BETWEEN NEARSHORE AND OFFSHORE WATERS IN THE BOHAI AND YELLOW SEAS, CHINA: A NEW ANALYSIS USING IMPROVED SATELLITE DATA – (CONTRASTE DE LA CLOROFILA: UN PATRÓN ESTACIONAL ENTRE LAS

26

AGUAS CERCANAS A LA COSTA Y LAS DE MAR ADENTRO EN LOS MARES DE BOHAI Y AMARILLO, CHINA: UN NUEVO ANÁLISIS UTILIZANDO DATOS DE SATÉLITE MEJORADOS)

Wang, Y., Gao, Z. - Continental Shelf Research - 2020

Abstract: The seasonal cycles of sea surface Chlorophyll-a (Chl-a) concentration in the Bohai and Yellow Seas (BYS) were examined using new remotely sensed Chl-a products derived using a regional algorithm. Based on objective clustering analysis, two contrasting Chl-a seasonal patterns were delineated: 1) A nearshore pattern with a long-lasting Chl-a peak from spring to autumn, coinciding with sufficient nutrient supply and high light availability, and lower Chl-a values in winter due to the light-limitation as a consequence of low solar irradiance and high diffuse attenuation coefficient of light for the water column; 2) An offshore pattern with a strong spring bloom triggered by alleviated light limitation and abundant nutrients coinciding with high water column stability. A weak autumn peak induced by new nutrient replenishment from bottom re-suspension and horizontal transportation, a low Chl-a trough in winter mainly attributed to light-limitation, and a Chl-a trough in summer due to nutrient scarcity were observed. The linkages between the province-specific Chl-a seasonal patterns and the seasonal cycles of the corresponding environmental variables as well as other potential influencing factors are comprehensively delineated and discussed. © 2020 Elsevier Ltd

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DEEP NEURAL NETWORKS FOR AUTOMATED DETECTION OF MARINE MAMMAL SPECIES – (REDES NEURONALES PROFUNDAS PARA LA DETECCIÓN AUTOMATIZADA DE ESPECIES DE MAMÍFEROS MARINOS)

Shiu, Y., Palmer, K.J., Roch, M.A., (...), Gillespie, D., Klinck, H. - Scientific Reports - 2020

Abstract: Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species. © 2020, The Author(s).

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USING A SKILLFUL STATISTICAL MODEL TO PREDICT SEPTEMBER SEA ICE COVERING ARCTIC SHIPPING ROUTES – (USANDO UN HÁBIL MODELO ESTADÍSTICO PARA PREDECIR EL HIELO DEL MAR DE SEPTIEMBRE QUE CUBRE LAS RUTAS DE NAVEGACIÓN DEL ÁRTICO)

Li, S., Wang, M., Huang, W., (...), Wang, B., Bai, Y.- Acta Oceanologica Sinica- 2020

Abstract: The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the mid-latitudes but also brings more opportunities for Arctic shipping routes

27

and polar resource exploration, both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of sea-ice cover in the future. Here, a novel data-driven method, the causal effect networks algorithm, is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction. The whole study area was also divided into two parts: the northern region covered by multiyear ice and the southern region covered by seasonal ice. The forecast models of September sea-ice extent in the whole study area (TSIE) and southern region (SSIE) at lead times of 1–4 months can explain over 65% and 79% of the variances, respectively, but the forecast skill of sea-ice extent in the northern region (NSIE) is limited at a lead time of 1 month. At lead times of 1–4 months, local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors. When the lead time is more than 4 months, the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE. We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models. © 2020, Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature.

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SATELLITE OBSERVATIONS OF PHYTOPLANKTON ENRICHMENTS AROUND SEAMOUNTS IN THE SOUTH WEST INDIAN OCEAN WITH A SPECIAL FOCUS ON THE WALTERS SHOAL – (OBSERVACIONES POR SATÉLITE DEL ENRIQUECIMIENTO DE FITOPLANCTON EN TORNO A LOS MONTES SUBMARINOS DEL OCÉANO ÍNDICO SUDOCCIDENTAL, CON ESPECIAL ATENCIÓN AL BANCO DE WALTERS)

Demarcq, H., Noyon, M., Roberts, M.J.- Deep-Sea Research Part II: Topical Studies in Oceanography - 2020

Abstract: Spatial anomalies associated with seamounts and islands have been detected in the South West Indian Ocean using a new surface chlorophyll-a based enrichment index (EI). Calculated from daily L3 4-km MODIS chl-a data from 2003 to 2018, the EI was used to quantify recurrent (seasonal) surface enrichment associated with these features. Most notable were the shallow Walters Shoal (18 m) and nearby deeper WS-2 seamount (480 m), both located on the southern Madagascar Ridge, which showed high EI values of 40% and 15% local enhancement. Tromelin Island, east of Madagascar, exhibited more moderate values of 15% local increase. Other shallower seamounts including La Pérouse (60 m) and MAD-Ridge (240 m) exhibited sporadic or no measurable surface maxima. Regions of strong mesoscale activity such as south of Madagascar where the South East Madagascar Current detaches from the continent also revealed high EI values. A marked seasonality of the EI was observed over the Walters Shoal, with higher values during the oligotrophic season than in the austral winter. The seasonal variability of the MLD in combination with the seamount bathymetry appears to induce the thermal and biological anomalies observed there, possibly favoured by the presence of a Taylor cap at the summit. Ship-collected in situ measurements showed the satellite observed enrichment to be associated with shallowing of the deep chlorophyll maximum, as well as a shift of phytoplankton groups towards diatoms and small flagellates. No net increase of integrated chl-a was measured but a potential doubling in primary productivity is expected. The EI developed in this study, designed to reveal sporadic, small localised chl-a maxima, is likely applicable to other areas in the ocean where there is local enrichments. © 2020 Elsevier Ltd.

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DEEP CONVOLUTIONAL NEURAL NETWORKS TO MONITOR CORALLIGENOUS REEFS: OPERATIONALIZING BIODIVERSITY AND ECOLOGICAL ASSESSMENT – (REDES NEURONALES CONVOLUCIONALES PROFUNDAS PARA VIGILAR LOS ARRECIFES DE CORAL: PONER EN FUNCIONAMIENTO LA EVALUACIÓN DE LA BIODIVERSIDAD Y LA ECOLOGÍA)

Marre, G., De Almeida Braga, C., Ienco, D., (...), Holon, F., Deter, J. - Ecological Informatics - 2020

Abstract: Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification models an increasingly viable tool for monitoring marine habitats. Coralligenous reefs are an underwater habitat of particular importance, found in the Mediterranean. This habitat is of a similar biocomplexity to coral reefs. They have been monitored in French waters since 2010 using manually annotated photo quadrats (RECOR monitoring network). Based on the large database of annotations accumulated therein, we have trained convolutional neural networks to automatically recognise coralligenous species using the data gathered from photo quadrats. Previous studies conducted on similar habitats performed well, but were only able to consider a limited number of classes, resulting in a very coarse description of these often-complex habitats. We therefore designed a custom network based on off-the-shelf architectures which is able to discriminate between 61 classes with 72.59% accuracy. Our results showed that confusion errors were for the most part taxonomically coherent, showing accuracy performances of 84.47% when the task was simplified to 15 major categories, thereby outperforming the human accuracy previously recorded in a similar study. In light of this, we built a semi-automated tool to reject unsure results and reduce error risk, for when a higher level of accuracy is required. Finally, we used our model to assess the biodiversity and ecological status of coralligenous reefs with the Coralligenous Assemblage Index (CAI) and the Shannon Index. Our results showed that whilst the prediction of the CAI was only moderately accurate (pearson correlation between observed and predicted CAI = 0.61), the prediction of Shannon Index was more accurate (pearson correlation = 0.74). In conclusion, it will be argued that the approach outlined by this study offers a cost and time-effective tool for the analysis of coralligenous assemblages which is suitable for integration into a large-scale monitoring network of this habitat. © 2020 Elsevier B.V.

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SATELLITE-BASED REMOTE SENSING DATA SET OF GLOBAL SURFACE WATER STORAGE CHANGE FROM 1992 TO 2018 – (CONJUNTO DE DATOS DE TELEDETECCIÓN POR SATÉLITE SOBRE EL CAMBIO EN EL ALMACENAMIENTO MUNDIAL DE AGUAS SUPERFICIALES DE 1992 A 2018)

Tortini, R., Noujdina, N., Yeo, S., (...), Marlier, M.E., Lettenmaier, D.P. - Earth System Science Data - 2020

Abstract: The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring at a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goals are to promote the use of satellite-derived products for the study of large inland water bodies and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (1V ) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions

29

(TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and Envisat) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage relationships between elevation and surface area (i.e., hypsometry) to produce estimates of 1V even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of 1V as well as of WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992-2018 period. The data sets presented and their respective algorithm theoretical basis documents are publicly available and distributed via the Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/, last access: 13 May 2020) of NASA s Jet Propulsion Laboratory. Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the 1V data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX/Poseidon in 1992 (beginning of the satellite altimetry era) to the near present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as in the data set presented here is of fundamental importance to baseline future SWOT products. © 2020 BioMed Central Ltd. All rights reserved.

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CURRENT AND FUTURE REMOTE SENSING OF HARMFUL ALGAL BLOOMS IN THE CHESAPEAKE BAY TO SUPPORT THE SHELLFISH INDUSTRY – (TELEDETECCIÓN ACTUAL Y FUTURA DE LAS FLORACIONES DE ALGAS NOCIVAS EN LA BAHÍA DE CHESAPEAKE PARA APOYAR LA INDUSTRIA DE LOS MARISCOS)

Wolny, J.L., Tomlinson, M.C., Schollaert Uz, S., (...), Scott, G.P., Stumpf, R.P. - Frontiers in Marine Science - 2020

Abstract: Harmful algal bloom (HAB) species in the Chesapeake Bay can negatively impact fish, shellfish, and human health via the production of toxins and the degradation of water quality. Due to the deleterious effects of HAB species on economically and environmentally important resources, such as oyster reef systems, Bay area resource managers are seeking ways to monitor HABs and water quality at large spatial and fine temporal scales. The use of satellite ocean color imagery has proven to be a beneficial tool for resource management in other locations around the world where high-biomass, nearly monospecific HABs occur. However, remotely monitoring HABs in the Chesapeake Bay is complicated by the presence of multiple, often co-occurring, species and optically complex waters. Here we present a summary of common marine and estuarine HAB species found in the Chesapeake Bay, Alexandrium monilatum, Karlodinium veneficum, Margalefidinium polykrikoides, and Prorocentrum minimum, that have been detected from space using multispectral data products from the Ocean and Land Colour Imager (OLCI) sensor on the Sentinel-3 satellites and identified based on in situ phytoplankton data and ecological associations. We review how future hyperspectral instruments will improve discrimination of potentially harmful species from other phytoplankton communities and present a framework in which satellite data products could aid Chesapeake Bay resource managers with monitoring water quality and protecting shellfish resources. © Copyright © 2020 Wolny, Tomlinson, Schollaert Uz, Egerton, McKay, Meredith, Reece, Scott and Stumpf.

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ARCTIC MULTIYEAR SEA ICE VARIABILITY OBSERVED FROM SATELLITES: A REVIEW – (VARIABILIDAD PLURIANUAL DEL HIELO MARINO DEL ÁRTICO OBSERVADA DESDE LOS SATÉLITES: UNA REVISIÓN)

Bi, H., Liang, Y., Wang, Y., (...), Kong, M., Huang, H. - Journal of Oceanology and Limnology - 2020

Abstract: In comparison with seasonal sea ice (first-year ice, FY ice), multiyear (MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons. Therefore, the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer. As a response, the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover. Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice. During the recent decades, many active and passive sensors onboard a variety of satellites (QuikSCAT, ASCAT, SSMIS, ICESat, CryoSat-2, etc.) have been used to monitor the dramatic loss of Arctic MY ice. The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations. We summarize the primary satellite data sources that are used to identify MY ice. The methodology to classify MY ice and derive MY ice concentration is reviewed. The interannual variability and trends in the MY ice time series in terms of coverage, thickness, volume, and age composition are evaluated. The potential causes associated with the observed Arctic MY ice loss are outlined, which are primarily related to the export and melting mechanisms. In addition, the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion, as well as the roles of synoptic weather, are analyzed. The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed. Moreover, some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed. © 2020, Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature.

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ESTIMATING TROPICAL CYCLONE INTENSITY IN THE SOUTH CHINA SEA USING THE XGBOOST MODEL AND FENGYUN SATELLITE IMAGES – (ESTIMACIÓN DE LA INTENSIDAD DE LOS CICLONES TROPICALES EN EL MAR DE CHINA MERIDIONAL UTILIZANDO EL MODELO XGBOOST Y LAS IMÁGENES DEL SATÉLITE FENGYUN)

Jin, Q., Fan, X., Liu, J., Xue, Z., Jian, H. – Atmosphere - 2020

Abstract: Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity prediction, which remains challenging, as the internal convection and formation mechanisms of such storms are not fully understood. This study investigated the relationship between remote sensing data and TC intensity to improve the accuracy of TC intensity prediction. An intensity forecast model for the South China Sea was built using the eXtreme Gradient Boosting (XGBoost) model and FengYun-2 (FY-2) satellite data, environmental data, and best track datasets from 2006 to 2017. First, correlation analysis algorithms were used to extract the TC regions in which the satellite data were best correlated, with TC intensity at lead times of 6, 12, 18, and 24 h. Then, satellite, best track, and environmental data were used as source data to develop three dierent XGBoost models for predicting TC intensity: model A1 (climatology and persistence predictors + environmental predictors), model A2 (A1 + satellite-based predictors extracted as mean values), and model A3 (A1 + satellite-based predictors extracted by our method). Finally, we analyzed

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the impact of the FY-2 satellite data on the accuracy of TC intensity prediction using the forecast skill parameter. The results revealed that the equivalent blackbody temperature (TBB) of the FY-2 data has a strong correlation with TC intensity at 6, 12, 18, and 24 h lead times. The mean absolute error (MAE) of model A3 was reduced by 0.47%, 1.79%, 1.91%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A2, respectively, and by 2.73%, 7.58%, 7.64%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A1. Furthermore, the accuracy of TC intensity prediction is improved by FY-2 satellite images, and our extraction method was found to significantly improve upon the traditional extraction method. © 2020 by the authors.

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6. PROYECTOS DE I+D+i

BÉLGICA, 2020. GRANDES DATOS Y DIGITALIZACIÓN

A la luz de los rápidos avances en la digitalización de la tecnología oceánica y la recopilación de datos, es necesario abordar la forma en que los macrodatos impactarán y ayudarán a impulsar el futuro de las ciencias marinas.

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FRANCIA, 2020. SEADATACLOUD: SEGUIR DESARROLLANDO LA INFRAESTRUCTURA PANEUROPEA PARA LA GESTIÓN DE DATOS MARINOS Y OCEÁNICOS

La infraestructura paneuropea SeaDataNet ha sido desarrollada por NODC y los principales institutos de investigación de 34 países. Más de 100 centros de datos marinos están conectados y proporcionan descubrimiento y acceso a recursos de datos para todos los investigadores europeos.

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MUNDIAL, 2020. PROYECTO DE DIGITAL OCEAN

Los proyectos le permiten organizar sus recursos de DigitalOcean (como Droplets, Spaces y balanceadores de carga) en grupos que se adaptan a su forma de trabajar. Puede crear proyectos que se alineen con las aplicaciones, entornos y clientes que aloja en DigitalOcean.

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GRECIA, 2019. BIGDATAOCEAN: EXPLOTACIÓN DE OCEAN'S OF DATA PARA APLICACIONES MARÍTIMAS

El principal objetivo de BigDataOcean es permitir escenarios de big data marítimos para empresas, organizaciones y científicos con sede en la UE, a través de una plataforma multisegmento que combinará datos de diferente velocidad, variedad y volumen bajo un motor multilingüe, confiable e interconectado para producir un depósito de datos masivos de valor y veracidad para los participantes y las comunidades locales.

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NORUEGA, 2019. VEHÍCULOS AÉREOS NO TRIPULADOS MEJORADOS PARA UNA MEJOR GESTIÓN MARINA

El impulso hacia actividades marítimas y marinas en entornos más remotos y hostiles exige nuevos enfoques y tecnologías. MarineUAS brindó a 16 jóvenes investigadores las habilidades y el conocimiento para ayudar a desarrollarlos.

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ESPAÑA, 2018. RESUMEN DEL INFORME FINAL: NEXOS (SISTEMAS DE SENSORES OCEÁNICOS MULTIFUNCIONALES, COMPACTOS, RENTABLES Y DE PRÓXIMA GENERACIÓN QUE POTENCIAN LA GESTIÓN MARINA, MARÍTIMA Y PESQUERA)

El proyecto europeo FP7 NeXOS aborda los desafíos anteriores respondiendo a los descriptores ambientales clave identificados por la Directiva Marco de la Estrategia Marina Europea (MSFD) con un conjunto de sistemas de sensores ópticos y acústicos multifuncionales, compactos, interoperables y habilitados para la Web de nueva generación, así como sensores para la ordenación pesquera.

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7. ANEXO METODOLÓGICO El presente reporte se trabajó mediante el uso de distintas fuentes de información, tal como se describió al inicio. Para el caso de información del campo científico y tecnológico se trabajó con bases de datos estructuradas. Se han utilizado las bases de datos Scopus (publicaciones científicas) y PatentInspiration (patentes de invención). A continuación, se muestran las sentencias de búsquedas elaboradas:

7.1 Patentes de invención TITLE/ABSTRACT: ((((sensors and (Autonomous or optical or "Advanced platform" or remote)) or (sensors and (camera or “Real aperture radar” or “Solid scanner” or termal or “Microwave radiometer” or laser)) or drones or (drone and (oceanic or submarine)) or drone or "gps trackers" or "gps" or (“gps” and (“water temperature” or salinity or nutrients)) or (vehicle and (underwater or autonomous or “remotely operated” or unmanned)) or “ROV” or "Rouvs" or "High Frecuency Radar" or buoys or "Autonomous Reef Monitoring Structures" or "Automatic identification systems" or "drop camera" or "Wireless Mesh networks" or "Animal Telemetry" or "Geographic Information System" or "Clod card" or "Satellite Oceanography" or "Bottom Pressure Recorder" or "Underwater hydrophone" or "Sound Navigation and Ranging" or “SONAR” or "Satellite Cartography" or microscopes or " Underwater image recognition" or "Real-time monitoring" or “Artificial Reef Monitoring Structures” or “remote sensing” or "LIDAR") AND (ocean or marine or oceanic or "océan" OR "maritimes" OR "transocéanique" or "ocean" or océan OR maritimes OR transocéanique OR Ozean or "mar" OR "marítimos" OR "marinos" OR oceánicas) AND (data or information)) OR ("artificial intelligence" or "machine learning" or "deep learning" or "cloud computing" or "big data" or "data science" or "computers science") AND (ocean or marine or oceanic or "océan" OR "maritimes" OR "transocéanique" or "ocean" or océan OR maritimes OR transocéanique OR Ozean or "mar" OR "marítimos" OR "marinos" OR oceánicas)) and not (river or lake)

Resultados: 808 documentos (745 familias). Periodo: 2018-2020 (hasta el mes de julio inclusive).

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7.2 Publicaciones científicas TITLE-ABS-KEY ( ( ( "ocean" OR "marine" OR "oceanic" OR "océan" OR "maritimes" OR "transocéanique" OR "mar" OR "marítimos" OR "marinos" OR "oceánicas" OR "oceanography" ) AND ( "data" OR "digital" ) ) AND ( "Artificial intelligence" OR "Machine learning" OR "Internet of things" OR "Deep learning" OR "Cloud computing" OR "Big Data" OR "Data Science" OR "Data mining" OR (sensor and ( "autonomous" OR "advance plataform" OR "remote" OR "optical" OR "imaging" OR "microwave" OR "laser")) OR "dron" OR "GPS trackers" OR ("Vehicles" AND ("underwater" OR "autonomous" OR "remotely operated" OR unmanned)) OR "ROUVs" OR "Drop-cameras" OR "Automatic identification systems" OR "High Frecuency Radar" OR "Wireless Mesh networks" OR "Animal Telemetry" OR "drifter oceanography" OR "GIS" OR "Clod card" OR "Bottom Pressure Recorder" OR "Underwater hydrophone" OR "Sound Navigation and Ranging" OR "Satellite Oceanography" OR "Oceanic microscopes" OR "Underwater image recognition" OR "Real-time monitoring" OR "Artificial Reef monitoring structures" OR "remote sensing") ) AND ( LIMIT-TO ( PUBYEAR , 2021 ) OR LIMIT-TO ( PUBYEAR , 2020 ) OR LIMIT-TO ( PUBYEAR , 2019 ) OR LIMIT-TO ( PUBYEAR , 2018 ))

RESULTADOS: 5.296 documentos. PERIODO: 2018-2020 (hasta el mes de julio inclusive).