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| 1 | Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)显示文摘Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube. | Gregory Giuliani Bruno Chatenoux Andrea De Bono Denisa Rodila Jean-Philippe Richard Karin Allenbach Hy Dao Pascal Peduzzi | 2017 | Big Earth Data2017，1，1： | 7 |
| 2 | Towards a knowledge base to support global change policy goals显示文摘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. | Stefano Nativi Mattia Santoro Gregory Giuliani Paolo Mazzetti | 2020 | International Journal of Digital Earth2020，13，2： | 5 |
| 3 | Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15-exploring the potential of data cube显示文摘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. | Gregory Giuliani Bruno Chatenoux Antonio Benvenuti Pierre Lacroix Mattia Santoro Paolo Mazzetti | 2020 | Big Earth Data2020，4，1： | 4 |
| 4 | Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production显示文摘Monitoring changes in Annual Net Primary Productivity(ANPP)is required for reporting on UN Sustainable Development Goal(SDG)Indicator 15.3.1:the proportion of land that is degraded over the total land area.Calibrating time-series observations of ANPP to derive Water Use Efficiency(WUE;a measure of ANPP per unit of evapotranspiration)can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes.Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation,which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification(UNCCD).This paper presents an algorithm for the Google Earth Engine(freely and openly available upon request-http://gffzzd3cc09b8251d45dfhqvcxufbkcbfn6wcb.ffgz.tsg.suse.edu.cn/10.5281/zenodo.4429773)to calculate and compare ANPP and WUE time series for Santa Cruz,Bolivia,which has recently experienced an intensification in its land use.This code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator 15.3.1.We use the MODIS 16-day average,250 m resolution to demonstrate that the Enhanced Vegetation Index(EVI)responds faster to changes in water avail-ability than the Normalized Difference Vegetation Index(NDVI).We also consider the relationships between ANPP and WUE.Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative.The sign and significance of the correlation between ANPP and WUE may also diverge over time.With further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience. | Andrea Markos Neil Sims Gregory Giuliani | 2023 | Big Earth Data2023，7，1： | 3 |
| 5 | Towards integrated essential variables for sustainability显示文摘Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions.These data allow characterizing any location on Earth,assessing the status of the environment at various scales(e.g.national,regional,global),understanding interactions between different systems(e.g.atmosphere,hydrosphere,biosphere,geosphere),and modeling future changes.The Group on Earth Observations(GEO)was established in 2005 in response to the need for coordinated,comprehensive,and sustained observations related to the state of the Earth.GEO’s global engagement priorities include supporting the UN 2030 Agenda for Sustainable Development,the Paris Agreement on Climate,and the Sendai Framework for Disaster Risk Reduction.A proposition is made for generalizing and integrating the concept of EVs across the Societal Benefit Areas of GEO and across the border between SocioEconomic and Earth systems EVs.The contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are introduced.Finally,the main aim of the 10 papers of the special issue is shortly presented and mapped according to the proposed typology of SBA-related EV classes. | Anthony Lehmann Joan Masò Stefano Nativi Gregory Giuliani | 2020 | International Journal of Digital Earth2020，13，2： | 3 |
| 6 | Digital earth:yesterday,today,and tomorrow显示文摘The concept of Digital Earth(DE)was formalized by Al Gore in 1998.At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary.Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society.This creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its formulation.By understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation. | Alessandro Annoni Stefano Nativi ArzuÇöltekin Cheryl Desha Eugene Eremchenko Caroline M.Gevaert Gregory Giuliani Min Chen Luis Perez-Mora Joseph Strobl Stephanie Tumampos | 2023 | International Journal of Digital Earth2023，16，1： | 2 |
| 7 | Drying conditions in Switzerland-indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs显示文摘Exacerbated by climate change,Europe has experienced series of hot and dry summer since the beginning of the 21st century.The importance of land conditions became an international concern with a dedicated sustainable development goal(SDG),the SDG 15.It calls for developing and finding innovative solu-tions to follow and evaluate impacts of changing land condi-tions induced by various driving forces.In Switzerland,drought risk will significantly increase in the coming decades with severe consequences on agriculture,energy production and vegeta-tion.In this paper,we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index(NDWI)to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions.Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of−0.22%/year for the 23%significant pixels across Switzerland.This tendency is mostly visible below 2000 m above sea level(m.a.s.l.)and in all biogeographical regions.The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend(−0.39%/year)over the last 35 years.Moreover,NDWI values are mostly a func-tion of temperature at elevations below the tree line rather than precipitation.Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales,but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability.Satellite Earth Observations data can provide valuable complementary obser-vations for national statistics on the ecological state of vegeta-tion to support SDG 15 to monitor land affected by drying conditions. | Charlotte Poussin Alexandrine Massot Christian Ginzler Dominique Weber Bruno Chatenoux Pierre Lacroix Thomas Piller Liliane Nguyen Gregory Giuliani | 2021 | Big Earth Data2021，5，4： | 1 |
| 8 | Cloud-based storage and computing for remote sensing big data:a technical review显示文摘The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and industry.Despite the progress of computer technologies,conventional computing implementations have become technically inefficient for processing RSBD.Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years.This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science.First,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,i.e.raster storage,metadata management,data homogeneity,and computing paradigms.Second,we introduce state-of-the-art cloud-based data management technologies for RSBD storage.The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data model.Four data models are suggested,i.e.scenes,ARD,data cubes,and composite layers.Third,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations.Finally,we categorize the architectures of mainstream RSBD platforms.This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers. | Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
| 9 | Emerging trends in big Earth data management and analysis显示文摘Big Earth data are increasingly used in a variety of applications.At the same time,technological developments happen rapidly and include Earth observation data cubes,analysis-ready data(ARD),the need to access distributed systems and data to avoid replicating datasets,searching and finding datasets,or visualization of data and information in a comprehensive way. | Martin Sudmanns Gregory Giuliani Dirk Tiede Hannah Augustin | 2023 | Big Earth Data2023，7，3： | 0 |
| 10 | Think global,cube local:an Earth Observation Data Cube’s contribution to the Digital Earth vision显示文摘The technological landscape for managing big Earth observation(EO)data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations.EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or global.Several EO data cubes with a geographic focus(“local EO data cubes”)have been implemented.However,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further examined.We investigate local EO data cubes from five perspectives(science,business and industry,government and policy,education,communities and citizens)and illustrate four examples covering three continents at different geographic scales(Swiss Data Cube,semantic EO data cube for Austria,DE Africa,Virginia Data Cube).A local EO data cube can benefit many stakeholders and players but requires several technical developments.These developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data cubes.We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet. | Martin Sudmanns Hannah Augustin Brian Killough Gregory Giuliani Dirk Tiede Alex Leith Fang Yuan Adam Lewis | 2023 | Big Earth Data2023，7，3： | 0 |
| 11 | Reviewing the discoverability and accessibility to data and information products linked to Essential Climate Variables显示文摘Essential Climate Variables(ECVs)are geophysical records generated from systematic Earth Observations associated with climate variations,changes,and impacts.ECVs products support the data and information needs of international frameworks and policies such as the work of the United Nations Framework Convention on Climate Change(UNFCCC)and the Intergovernmental Panel on Climate Change(IPCC).We map the main networks and initiatives publishing ECVs,by presenting an overview of existing satellite-based ECVs,their general data creation characteristics,discoverability and accessibility methods from an end-user perspective.We investigate key initiatives providing or coordinating access to ECV data records,such as the Global Climate Observing System(GCOS),the Committee on Earth Observation Satellites(CEOS),the Coordination Group for Meteorological Satellites(CGMS),Joint Working Group on Climate(WGClimate),the Remote Sensing Systems(REMSS),and the European Space Agency Climate Change Initiative(ESA CCI).We find that ECV data discovery and access is difficult and time consuming due to the lack of common data and metadata catalogues.In addition,the selection of fit-for-purpose data records by end-users requires the implementation of interoperable standards and scalable data infrastructures to allow the generation of tailored applications and datadriven information products in support of decision-making processes. | Maria Teresa Miranda Espinosa Gregory Giuliani Nicolas Ray | 2020 | International Journal of Digital Earth2020，13，2： | 0 |
| 12 | One decade(2011–2020)of European agricultural water stress monitoring by MSG-SEVIRI:workflow implementation on the Virtual Earth Laboratory(VLab)platform显示文摘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. | Bagher Bayat Carsten Montzka Alexander Graf Gregory Giuliani Mattia Santoro Harry Vereecken | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
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