Global Earth Observation System Of Systems Research Articles

Publishing Eurac Research data on the GEOSS Platform

ABSTRACT 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

ABSTRACT 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 the findings, 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 China satellite data on the GEOSS Platform

ABSTRACT 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.

Learning invention using satellite observations to support sustainable development goals (SDG): A use case on disaster risk reduction in Sei Serelo Indonesia

The morphological pattern of the Sei Serelo was investigated to infer the impact of land use and climate change. Two sets of areal Landsat (1990 and 2019) identified the morphological changes to reduce the disaster risk and ideally reverse this prevailing situation. This paper presents a scalable and flexible approach to monitoring land-use change at the local level using various components of the Global Earth Observation System of Systems (GEOSS) platform. Increasing mining area has contributed to land-use change and the loss of agricultural land in many rural areas. In many cases, it worsens the poverty levels of smallholder farmers who depend on subsistence farming – an issue that Sustainable Development Goals number one seeks to address. A multi-criteria evaluation is applied using morphometric indicators, geology, and contours to identify the areas vulnerable to drainage and relief conditions. This learning invention has developed decision tools to apply GIS utilization to support disaster risk reduction. The devices are iterative and can be updated as new events occur to maximize GIS benefit, reducing disaster risk reduction and their potential consequences.

Developing an Open Data Portal for the ESA Climate Change Initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal ( http://cci.esa.int/data/ ). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence.

Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation

Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Landmark Assessment Report highlighted that human activities are considerably degrading land and threating the well-being of approximately 3.2 billion people.In order to reduce and ideally reverse this prevailing situation, national capacities should be strengthened to enable effective assessments and mapping of their degraded lands as recommended by the United Nations Sustainable Development Goals (SDGs). The indicator 15.3.1 (“proportion of land that is degraded over total land area”) requires regular data production by countries to inform and assess it through space and time. Earth Observations (EO) can play an important role both for generating the indicator in countries where it is missing, as well complementing or enhancing national official data sources.In response to this issue, this paper presents an innovative, scalable and flexible approach to monitor land degradation at various scales (e.g., national, regional, global) using various components of the Global Earth Observation System of Systems (GEOSS) platform to leverage EO resources for informing SDG 15.3.1. The proposed approach follows the Data-Information-Knowledge pattern using the Trends.Earth model (http://trends.earth) and various data sources to generate the indicator. It also implements additional components for model execution and orchestration, knowledge management, and visualization.The proposed approach has been successfully applied at global, regional and national scales and advances the vision of (1) establishing data analytics platforms that can potentially support countries to discover, access and use the necessary datasets to assess land degradation; and (2) developing new capacities to effectively and efficiently use EO-based resources.

Ocean Climate Monitoring

Measuring ocean physics and atmospheric conditions at the sea-surface has been taking place for decades in our world’s oceans. Enhancing RD we discuss a “Hybrid” air-sea interaction deep-sea monitoring system that has been developed in the private sector to mirror ocean-climate community data streams and has been successfully deployed on three basin-scaled programs in the Indian Ocean (RAMA, First Institute of Oceanography, FIO, China), the Andaman Sea (MOMSEI, Monsoon Onset Monitoring, FIO) and the Pacific Ocean (China’s Institute of Oceanology, Academy of Sciences research in the western tropical Pacific). This application is a base to build upon as new sensors are developed and increased sampling at higher resolutions is required. A multidisciplinary tool to add new biological and biogeochemical sensors to study interaction with the physical processes of our oceans. This application can also be used at FLUX sites to enhance the Argo Program, telemetry docking stations for autonomous vehicles such as sail-drones, gliders and wave riders for enhancement and contribution to the Global Tropical Moored Buoy Array (GTMBA), Global Ocean Observing System (GOOS), Global Climate Observing System (GCOS), and the Global Earth Observing System of Systems (GEOS).

Applicability of KOMPSAT Image Data in Open Data Cube

Abstract. Data cube terms a multi-dimensional stack of gridded datasets aligned for analysis. Open Data Cube (ODC) is an open-source based information processing and managing platform on the viewpoint from web-based infrastructure. This open platform is for a large volume of geo-spatial information with geo-rectified coordinates, and it has been applied by non-profit international organizations such as the Committee on Earth Observation Satellites (CEOS) for an international coordination and management of space-borne missions, Global Earth Observation System of Systems (GEOSS) in the Group on Earth Observations (GEO), as an intergovernmental organization to improve the applicability, accessibility and usability of Earth observations for benefit of human society.The building of Analysis Ready Data (ARD), which means the preparation of radiometric calibration and geo-rectification, is for the data cube utilization. The platform converts large-scale satellite image data into analytic information, providing functions for time series analysis. Internationally, there has been an ever-increasing number of country-based data cube deployments with freely available satellite images, including Australia Data Cube, Vietnam Data Cube, Swiss Data Cube, and Colombia Data Cube, as a computing environment for information distribution, sharing, and analysis.However, there is no program yet to register Korea Multi-purpose satellite (KOMPSAT) optical and radar images on this platform, so this study developed the registering and ingestion script codes for KOMPSAT optical and radar image sets into ODC. Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. Thus, an ingestion process is required to add satellite data to the ODC platform and the process can be divided into three main stages in Figure 1. First, it is to define the data type in the YAML format. Then, the indexing process of datasets for metadata registration is necessary. The next step is a data ingestion process that users can be used directly data sets collected in ODC.Figure 2 shows some of the Python module results for index datasets and the process of metadata generation. The metadata YAML, which is required for indexing, has many advantages in many respects in the creation of metadata through Python modules. This is why we added Python modules to create metadata YAML. In particular, the KOMPSAT data ingestion process was designed so that all of them were possible through one module.Using script modules for these steps, the functional accuracy was tested with actual satellite data. Color composite images using RGB bands of KOMPSAT optical images were generated in the ODC environment in Figure 3. In this process, image data formats of GeoTiff and netCDF are also supported.In this study, consideration points for implementation of ODC applications are also discussed. KOMPSAT data is basically commercial-based products, unlike other freely accessible satellite images in the ODC applications. For the practical contribution for ODC-GEOSS, careful considerations for data policy are needed, because it can be applied as a reference model for other commercial satellite data for GEOSS.

A novel approach for mapping WRB soil units – A methodology for a global SOTER coverage

Traditional soil maps present soil information in the form of categorical classes of soil types classified on theappropriate level of the applied classification system corresponding to the scale. Soil complexes and associations have been used to describe polygons. This kind of data structure is useful to characterise an area by explaining its soil resources. However, it is difficult to convert these complex categorical units into a simple digital variable, the usage of this kind of data in a digital environment is limited. Users often need single properties instead of the complex classes. Additionally, the problem becomes more complicated when soil information of different origin, based on different classification systems has to be integrated into a common, harmonised database. The presented methodology is part of the efforts to develop a global SOTER (World Soil and Terrain database) coverage and contribute to the global soil observing s as part of the Global Earth Observing System of Systems (GEOSS). The aim is to determine and map the relevant soil properties, horizons and materials following the diagnostic concepts of the World Reference Base (WRB) for soil resources andderive the occurrence probability of soil classes (WRB reference soil groups) of certain spots with the application of remote sensing and digital soil mapping tools. The developed method is referred as the e-SOTER approach and is capable of producing a stack of soil diagnostic element layers with the likelihood of their occurrence within each pixel and a layer of WRB reference soil groups (RSG). This new approach may provide better input for modellers and predict the spatial continuum of the soil cover in a much better resolution than the traditional polygon based approaches. At the same time the diagnostic elements, as building blocks of the classification systems, help the correlation of the national soil classes into integrated databases and maps.

AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.

Approach and practice: integrating earth observation resources for data sharing in China GEOSS

ABSTRACTEarth observation data sharing is an essential part of the data lifecycle and plays a critical role in Earth science research. Existing industry data sharing systems are affected by restrictions in distributed resource management and tightly coupled service interoperability. These systems currently offer no support for facilitating cross-disciplinary exploration and application. The lack of a national data sharing infrastructure has led to reduced international cooperation. These barriers are common and have hindered the development of the Global Earth Observation System of Systems (GEOSS). The China GEOSS Data Sharing Network (China GEOSS DSNet) has been proposed as a part of China’s Plan for Implementing GEOSS (2016–2025) to address the above issues. In this research, we designed a national GEOSS data sharing framework, including resource integration mechanism, sharing-oriented metadata standards, and lightweight interoperability service to coordinate various Earth observation resources. So far, more than 29 million archived satellite metadata records and 200 TB of high-quality satellite datasets have been integrated under this framework. The results were demonstrated in the following applications: domestic satellite archived metadata query service, international Earth observation resource sharing service, and disaster emergency response service.

Pixel to Picture and Picture to Person

Remotely sensed data are related with the words pixel and resolution as the picture is formed from the pixel having the size defined by the value of resolution of the data. There are unlimited sectors where remote sensing technology can be applied to uplift the social status of the people. In order to make best use of this system, Regional Workshop on Himalayan GEOSS was organized in Nepal. The workshop developed a concept “pixel to picture and picture to person” based on the technology related to Earth observation. The concept to be used for the several sectors identified by the workshop which are also related with the Social benefit areas of the Global Earth Observation System of Systems (GEOSS) of Group on Earth Observation (GEO). Therefore, this paper tries to summarize the result of the workshop in this particular theme.

Chilean geo client application for disasters

Abstract. The global network of the Group on Earth Observation, GEO, connects all kinds of professionals from public and private institutions with data providers, sharing information to face the challenges of global changes and human development and they are creating a Global Earth Observation System of Systems (GEOSS) to connect existing data infrastructures. A GEOSS Architecture Implementation Pilot Project for Disasters in Chile (AIP-8) was created as part of a capacity building initiative and representatives of different national agencies in Chile, along with international experts, formed a GEOSS Capacity Building Working Group (Lovison et al, 2016). Consistent with the objectives of GEOSS AIP-8 Chile, we developed and implemented a prototype service based on web services, mobile applications and other communication channels, which allows connecting different sources of information, aiming to reduce population vulnerability to natural disasters such as: earthquakes, flooding, wild fires and tsunamis, which is presented here. The GEO Chile client application is a JavaScript application using GEODAB brokering services, GIS technology and disaster information provided by national and international disaster services, including public and private organizations, where cartography becomes fundamental as a tool to provide realism and ubiquity to the information. Seven hotpots are targeted: Calbuco, Copahue and Villarrica volcanoes areas, Valparaíso city, which is frequently a victim of wildfires in the zone where population meets forest and Iquique, Illapel and Talcahuano, areas frequently struck by earthquakes and tsunamis.

Interoperability challenges in river discharge modelling: A cross domain application scenario

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question.This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS).The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a “River Discharge” application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

The China GEO Data Center: Bringing order to open Earth-observation data

Numerous remote-sensing devices observe the Earth every day, generating large amounts of remotesensing images and metadata. The extensive Earth obser vation (EO) data made available on the Internet by different EO agencies in multiple countries pose challenges for integration, storage, access, and collaborative applications. The capacity to explore and utilize all these heterogeneous EO data from across the world is very limited. In this article, we introduce the China Group on Earth Observations (GEO) Data Center, which is devoted to the efficient integration of EO data on the Internet and opening China's EO data to the world. We describe the infrastructure of the China GEO Data Center and discuss the design and implementation of the China Global Earth Observation System of Systems (GEOSS). We demonstrate the use of the GEO discovery and access broker (DAB) to harvest the large amounts of open EO data brokered by the GEO from different agencies around the world. We describe a data model we have designed to express the relations among EO data and capture the implicit semantic knowledge in EO data through rules (a model implemented using the Apache Jena framework). Finally, we describe the mechanisms for opening China's EO data to the world.

SCHISTOSOMIASIS: GEOSPATIAL SURVEILLANCE AND RESPONSE SYSTEMS IN SOUTHEAST ASIA

Abstract. Geographic information system (GIS) and remote sensing (RS) from Earth-observing satellites offer opportunities for rapid assessment of areas endemic for vector-borne diseases including estimates of populations at risk and guidance to intervention strategies. This presentation deals with GIS and RS applications for the control of schistosomiasis in China and the Philippines. It includes large-scale risk mapping including identification of suitable habitats for Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Predictions of infection risk are discussed with reference to ecological transformations and the potential impact of climate change and the potential for long-term temperature increases in the North as well as the impact on rivers, lakes and water resource developments. Potential integration of geospatial mapping and modeling in schistosomiasis surveillance and response systems in Asia within Global Earth Observation System of Systems (GEOSS) guidelines in the health societal benefit area is discussed.

Contributing to the GEO Model Web implementation: A brokering service for business processes

In the Earth system science domain, most current digital infrastructures are able to support data access rather than provide answers to complex questions – noticeably, supporting the ability to address the what-if questions posed by users. To this purpose, integrated modeling is an indispensable element. This work follows a recognizable gap characterized by high-level steps towards model integration: the Science-to-Information Technology barrier. This manuscript introduces an innovative approach to address such a gap in the “Model Web” framework initiative promoted by GEO (Group on Earth Observation) on its GEOSS (Global Earth Observation System of Systems) program. The methodological approach consists of five technologically neutral steps to build executable workflows from abstract business processes. A high-level architecture, based on the brokering pattern, is introduced to implement the proposed approach. By extending the GEOSS brokering framework, a proof-of-concept implementation is presented, argued, and ultimately experimentations and conclusions are discussed.

MONITORING OF IN-FIELD VARIABILITY FOR SITE SPECIFIC CROP MANAGEMENT THROUGH OPEN GEOSPATIAL INFORMATION

Abstract. The agricultural sector is in a unique position due to its strategic importance around the world. It is crucial for both citizens (consumers) and the economy (both regional and global), which, ideally, should ensure that the whole sector is a network of interacting organisations. It is important to develop new tools, management methods, and applications to improve the management and logistic operations of agricultural producers (farms) and agricultural service providers. From a geospatial perspective, this involves identifying cost optimization pathways, reducing transport, reducing environmental loads, and improving the energy balance, while maintaining production levels, etc. This paper describes the benefits of, and open issues arising from, the development of the Open Farm Management Information System. Emphasis is placed on descriptions of available remote sensing and other geospatial data, and their harmonization, processing, and presentation to users. At the same time, the FOODIE platform also offers a novel approach of yield potential estimations. Validation for one farm demonstrated 70% successful rate when comparing yield results at a farm counting 1’284 hectares on one hand and results of a theoretical model of yield potential on the other hand. The presented Open Farm Management Information System has already been successfully registered under Phase 8 of the Global Earth Observation System of Systems (GEOSS) Architecture Implementation Pilot in order to support the wide variety of demands that are primarily aimed at agriculture and water pollution monitoring by means of remote sensing.

ERA-PLANET THE EUROPEAN NETWORK FOR OBSERVING OUR CHANGING PLANET

In the last decade a significant number of projects and programmes in different domains of Earth Observation and environmental monitoring have generated a substantial amount of data and knowledge on different aspects related to environmental quality and sustainability. Big data generated by in-situ or satellite platforms are being collected and archived with a plethora of systems and instruments making difficult the sharing of data and transfer of knowledge to stakeholders and policy makers to support key economic and societal sectors. The overarching goal of ERAPLANET is to strengthen the European Research Area in the domain of Earth Observation in coherence with the European participation in the Group on Earth Observation (GEO) and Copernicus. The expected impact is to strengthen European leadership within the forthcoming GEO 2015-2025 Work Plan. ERA-PLANET is designed to reinforce the interface with user communities, whose needs the Global Earth Observation System of Systems (GEOSS) intends to address. It will provide more accurate, comprehensive and authoritative information to policy and decision-makers in key societal benefit areas, such as Smart Cities and Resilient Societies; Resource efficiency and Environmental management; Global changes and Environmental treaties; Polar areas and Natural resources. ERA-PLANET will provide advanced decision-support tools and technologies aimed to better monitor our global environment and share the information and knowledge available in the different domains of Earth Observation.

ERA-PLANET THE EUROPEAN NETWORK FOR OBSERVING OUR CHANGING PLANET

Abstract. In the last decade a significant number of projects and programmes in different domains of Earth Observation and environmental monitoring have generated a substantial amount of data and knowledge on different aspects related to environmental quality and sustainability. Big data generated by in-situ or satellite platforms are being collected and archived with a plethora of systems and instruments making difficult the sharing of data and transfer of knowledge to stakeholders and policy makers to support key economic and societal sectors. The overarching goal of ERAPLANET is to strengthen the European Research Area in the domain of Earth Observation in coherence with the European participation in the Group on Earth Observation (GEO) and Copernicus. The expected impact is to strengthen European leadership within the forthcoming GEO 2015-2025 Work Plan. ERA-PLANET is designed to reinforce the interface with user communities, whose needs the Global Earth Observation System of Systems (GEOSS) intends to address. It will provide more accurate, comprehensive and authoritative information to policy and decision-makers in key societal benefit areas, such as Smart Cities and Resilient Societies; Resource efficiency and Environmental management; Global changes and Environmental treaties; Polar areas and Natural resources. ERA-PLANET will provide advanced decision-support tools and technologies aimed to better monitor our global environment and share the information and knowledge available in the different domains of Earth Observation.