Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems

Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.

Publishing Eurac Research data on the GEOSS Platform

This paper is the third of a series that introduces some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform is a brokering infrastructure that brokers more than 190 autonomous information systems and data catalogs;it was created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS). This manuscript focuses on the analysis of Eurac Research datasets and illustrates the data publishing process to enroll the Eurac Research Data Provider to the GEOSS Platform through the administrative and technical registrations. The study provides an analysis of the GEOSS user searches for Eurac Research data in order to understand the main use of datasets of an important Data Provider.

GEOSS Platform data content and use

The GEOSS Platform is a key contribution to the goal of building the Global Earth Observation System of Systems(GEOSS).It enables a harmonized discovery and access of Earth observation data,shared online by heterogeneous organizations worldwide.This work analyzes both what is made available in the GEOSS Platform by the data providers and how users are utilizing it including multiyear trends,updating a previous analysis published in 2017.The present statistics derive from a 2021 EOValue report funded by the European Commission.The offer of GEOSS Platform data has been the object of various analyses,including data provider characterization,data sharing trends,and data characterization(comprising metadata quality analysis,thematic analysis,responsible party identification,spatial–temporal coverage).GEOSS data demand has also been the object of several analyses,including data consumer characterization,utilization trends,and requested data characterization(comprising thematic analysis,spatial–temporal coverage,and popularity).Among thefindings,a large amount of shared data,mostly from satellite sources,emerges with an issue of low metadata quality and related discovery match.Moreover,the trend in usage is decreasing.Therefore,the progressive disconnection of the GEOSS platform from its data Providers and Users and other possible causes are also reported.

Publishing NextGEOSS data on the GEOSS Platform

This paper is the second of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform was created as the technological tool to implement interoperability among the Global Earth Observation System of Systems (GEOSS);it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. This paper is focused on the analysis of the NextGEOSS datasets describing the data publishing process from NextGEOSS to the GEOSS platform. In particular, both the administrative registration and the technical registration were taken into consideration. One of the most important data shared by the GEOSS Platform are the NextGEOSS datasets: the present study provides some insights in terms of GEOSS user searches for NextGEOSS data.

Publishing China satellite data on the GEOSS Platform

This paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform.The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems(GEOSS);it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems.The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration.The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform.The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.

Towards a knowledge base to support global change policy goals

In 2015,it was adopted the 2030 Agenda for Sustainable Development to end poverty,protect the planet and ensure that all people enjoy peace and prosperity.The year after,17 Sustainable Development Goals(SDGs)officially came into force.In 2015,GEO(Group on Earth Observation)declared to support the implementation of SDGs.The GEO Global Earth Observation System of Systems(GEOSS)required a change of paradigm,moving from a data-centric approach to a more knowledge-driven one.To this end,the GEO System-of-Systems(SoS)framework may refer to the well-known Data-Information-Knowledge-Wisdom(DIKW)paradigm.In the context of an Earth Observation(EO)SoS,a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data–e.g.social and economic datasets.These elements are:Essential Variables(EVs),Indicators and Indexes,Goals and Targets.Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem.This includes:collect,formalize,publish,access,use,and update knowledge.ConnectinGEO project analysed the knowledge necessary to recognize,formalize,access,and use EVs.The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.

Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15-exploring the potential of data cube

Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth.To halt and reverse the current trends in land degradation,there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands,as required by the Sustainable Development Goals(SDGs),in particular,the SDG indicator 15.3.1(“proportion of land that is degraded over total land area”).Earth Observations(EO)can play an important role both for generating this indicator as well as complementing or enhancing national official data sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat or Sentinels).In response to this issue,this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.

Exploring the depths of the global earth observation system of systems

This paper explores for the first time the contents,structure and relationships across institutions and disciplines of a global Big Earth Data cyber-infrastructure:the Global Earth Observation System of System(GEOSS).The analysis builds on 1.8 million metadata records harvested in GEOSS.Because this set includes almost all the major large data collections in GEOSS,the analysis represents more than 80%of all the data made available through this global system.We explore two major aspects:the collaborative networks and the thematic coverage in GEOSS.The first connects the contributing organisations through the more than 200,000 keywords used in the systems,and then explores who is citing whom,a proxy for of institutional thickness.The thematic coverage is analysed through neural network algorithms,first on the keywords,and then on the corpus of 653 million lemmatised lower case words built from the titles and abstracts of all 1.8 million metadata records.The findings not only give a good overview of the GEOSS data universe,but offer immediate priorities on how to increase the usability of GEOSS through improved data management,and the opportunity to augment the metadata with high level concept that synthetise well the contents of the data-set.

One decade(2011–2020)of European agricultural water stress monitoring by MSG-SEVIRI:workflow implementation on the Virtual Earth Laboratory(VLab)platform

Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired products.In this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water stress.To this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on VLab.The execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.

Developing food,water and energy nexus workflows

There is a growing recognition of the interdependencies among the supply systems that rely upon food,water and energy.Billions of people lack safe and sufficient access to these systems,coupled with a rapidly growing global demand and increasing resource constraints.Modeling frameworks are considered one of the few means available to understand the complex interrelationships among the sectors,however development of nexus related frameworks has been limited.We describe three opensource models well known in their respective domains(i.e.TerrSysMP,WOFOST and SWAT)where components of each if combined could help decision-makers address the nexus issue.We propose as a first step the development of simple workflows utilizing essential variables and addressing components of the above-mentioned models which can act as building-blocks to be used ultimately in a comprehensive nexus model framework.The outputs of the workflows and the model framework are designed to address the SDGs.