Selection of the Key Earth Observation Sensors and Platforms Focusing on Applications for Polar Regions in the Scope of Copernicus System 2020–2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consump...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Estefany Lancheros, Adriano Camps, Hyuk Park, Pedro Rodriguez, Stefania Tonetti, Judith Cote, Stephane Pierotti
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
SAR
ice
Q
Online Access:https://doi.org/10.3390/rs11020175
https://doaj.org/article/086920428399498a8689affdcc8825e1
Description
Summary:An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.