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Citizen Observatories and the New Earth Observation Science
Open AccessEditorial

The Role of Citizen Science in Earth Observation

Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria
Department of Mathematics, University of Coimbra, 3001-501 Coimbra, Portugal
Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), 3030-290 Coimbra, Portugal
Author to whom correspondence should be addressed.
Academic Editor: Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 357;
Received: 29 March 2017 / Revised: 29 March 2017 / Accepted: 29 March 2017 / Published: 11 April 2017
(This article belongs to the Special Issue Citizen Science and Earth Observation)
Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS projects that could provide valuable calibration and validation data for EO, yet are not integrated into operational monitoring systems. A special issue on the role of CS in EO has revealed continued trends in applications, covering a diverse set of fields from disaster response to environmental monitoring (land cover, forests, biodiversity and phenology). These papers touch upon many key challenges of CS including data quality and citizen engagement as well as the added value of CS including lower costs, higher temporal frequency and use of the data for calibration and validation of remotely-sensed imagery. Although still in the early stages of development, CS for EO clearly has a promising role to play in the future. View Full-Text
Keywords: earth observation; citizen science; crowdsourcing; citizen observatories; data quality; citizen engagement earth observation; citizen science; crowdsourcing; citizen observatories; data quality; citizen engagement
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Fritz, S.; Fonte, C.C.; See, L. The Role of Citizen Science in Earth Observation. Remote Sens. 2017, 9, 357.

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