Overcoming Data Scarcity in Earth Science

Dear Colleagues,

Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models heavily rely on the data collected by direct field observations. However, a functional and comprehensive dataset of any environmental variable is hard to collect, mainly because of: i) the high cost of the monitoring campaigns; and ii) the low reliability in the measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to the equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited.

Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problem. More recently, machine learning techniques as clustering and classification, have been proposed to complete missing data.

This Special Issue on “Overcoming Data Scarcity in Earth Science” of the Journal Data is designed to draw attention to the body of knowledge that aims at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

Authors are encouraged to submit research articles, reviews, and short communications addressing this theme in this Special Issue.

Dr. Angela Gorgoglione
Dr. Alberto Castro Casales
Dr. Christian Chreties Ceriani
Dr. Lorena Etcheverry Venturini
Guest Editors

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• Earth science data
• Data scarcity
• Missing data
• Data quality
• Data Imputation
• Statistical Methods
• Machine learning
• Environmental modeling
• Environmental observations