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A User-Driven Approach to Determining Critical Earth Observation Priorities for Societal Benefit

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4 Author(s)
Erica Zell ; Battelle Memorial Institute, Arlington ; Amy K. Huff ; Adam T. Carpenter ; Lawrence A. Friedl

In order to help data providers make informed decisions regarding the availability and accessibility of Earth observations for societal benefit, a cross-sectoral meta-analysis of observation priorities was conducted from the perspective of users. The analysis was organized around the Group on Earth Observations' (GEOs) societal benefit areas (SBAs): Agriculture, Biodiversity, Climate, Disasters, Ecosystems, Energy, Health, Water, and Weather. Users' needs for Earth observations were prioritized using a two-stage process. In the first stage, critical observations for each individual SBA were determined based on observation needs expressed in publicly available documents, such as scientific journal articles, scientific reports, and workshop summaries. In the second stage, an ensemble of four statistically robust methods was used to prioritize the observations identified in the individual SBA analyses, based on their criticality and commonality to multiple SBAs. The result of the meta-analysis is a ranking of 152 critical Earth observation priorities; the highest priority Earth observations are those that are considered critical for the largest number of SBAs. The 10 highest ranked observations are: (1) Precipitation, (2) Soil Moisture, (3) Surface Air Temperature, (4) Land Cover, (5) Surface Wind Speed (6) Vegetation Cover, (7) Surface Humidity, (8) Urbanization, (9) Vegetation Type, and (10) Surface Wind Direction and Sea Surface Temperature (tie). This study represents one viable method to assess the priority of Earth observations from the perspective of users. The list of critical Earth observation priorities from this study is a foundation for engagement between data providers and users in regard to observation priorities.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:5 ,  Issue: 6 )