With the increasing volume of information from satellites observing Earth, the technical and methodological prerequisites of users in science and applications are becoming more demanding and complex for generating demand-driven products while exploiting the full potential of large Earth observation (EO) data archives. Since 2014, the European Space Agency (ESA) is addressing this challenge with the concept of Thematic Exploitation Platforms (TEPs), aiming to create an ecosystem of interconnected platforms providing thematic EO-based data and services for currently seven thematic sectors.

The built-environment and urban sector is addressed with the Urban Thematic Exploitation Platform (UrbanTEP; urban-tep.eu), acknowledging that urbanization and sustainable settlement growth are key global challenges. The linkages to socio-economic development, health, environment, greenhouse gas emissions, climate change and other sectors are deep and multi-faceted. EO based services and resulting information products and other spatial datasets have successfully found their way into planning and decision-making processes that address the urban ecosystem. While a range of downstream services are based on solitary and effortful processing and visualization solutions, the platform-based approach has proven to be a game changing technology, being capable of revolutionizing service provision, workflows and information products.

UrbanTEP is a collaborative system, which focuses on EO data provision, processing and other spatial products for delivering multi-source information on trans-sectoral urban challenges on various scales. It is developed to provide end-to-end and ready-to-use solutions for a wide spectrum of users in the public and private sector. The core system components are an open, web-based portal connected to distributed and scalable high-level computing infrastructures and providing key functionalities for:

• high-performance data access and processing (IaaS – Infrastructure as a Service),
• modular and generic state-of-the art pre-processing, analysis, and visualization tools and algorithms (SaaS – Software as a Service),

• customized development and sharing of algorithms, products and services (PaaS – Platform as a Service), and
• networking and communication.

The facilitation of EO service acceptance and uptake by the urban community, as well as the onboarding of third-party service providers are essential to PaaS solutions. UrbanTEP is therefore in the process of expanding the range of service solutions and the interconnection with other service providers. The concept of “City Data Cubes” is introduced for urban use cases and algorithm hosting capabilities (“algo-as-as-service” functionalities) are improved by adopting the OGC Common Architecture standard. In addition, the data analytics and visualization capabilities of UrbanTEP provide functionalities for a user-driven derivation of key urban indicators based on the above-mentioned multi-source data collections. The provision of premium urban information products, like the World Settlement Footprint (WSF) outlining built-up areas globally, allows users and service providers to derive customized demand-driven EO-based products.

How to cite: Bachofer, F., Boettcher, M., Boissier, E., Brandt, G., Brockmann, C., Esch, T., Feuerstein, S., Goncalves, P., Marconcini, M., Opletal, M., Pacini, F., Paganini, M., Soukup, T., and Svaton, V.: UrbanTEP – Earth Observation Based Services for the Urban Community, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-873, https://doi.org/10.5194/egusphere-egu22-873, 2022.

Increasing agricultural production is inevitable in the future since population growth and climate change have led to significant pressure on global food security. One of the ways is to intensify the existing cropland by multi-cropping practice, allowing multiple uses of a single field during one year.  This research aims to identify and map double-cropping land using multi-temporal Sentinel 2 imagery from 2021 and advanced machine learning models. The case study focus is on Bačka and Srem, regions located in the Autonomous Province of Vojvodina, Republic of Serbia. These regions are characterized by fertile land and widespread agriculture production. However, there is a low presence of double-cropping practice due to usually dry summers, but with a tendency to change as the number of irrigation systems increase.

Considering the small amount of double-cropping fields, there is a need for direct ground truth data collection. For that reason, the first step was to reduce the area of interest to get insight into the locations of potential double-cropping land. This result was obtained by using the threshold method based on the phenology of crops during the year. The NDVI (Normalized Difference Vegetation Index) time series was utilized to define appropriate thresholds for feature two peak values to discriminate double-cropping within each pixel. The identification of the results was used on-site for collecting ground truth data. Based on the collected data and the analyzed NDVI time series, besides double-crop, three more classes of arable land were distincted and included in the classification: single winter crops, single summer crops and clover. The collected data contained 46 parcels of double crops, 43 single winter crops, 55 single summer crops and 27 parcels of clover. We used time-series images to create a dataset for training the pixel-based Random Forest classification. The results showed a very high overall accuracy of 99% and an  F-score higher than 0.9 for each of the classes.

This methodology is a suitable approach for detecting double-cropping systems, with further potential to identify exact crop types and the main practice of combining crops. The findings of this study showed that only about 2% of the study area was under this production. Except for positive economic outcomes, utilizing these systems brings significant environmental benefits and rational use of the soil without expanding physical cropland but with the same advantages. Therefore, the resulting geospatial datasets of double cropping croplands could help solve important questions relevant to food security, irrigation and climate change.

How to cite: Marković, M., Lugonja, P., Brdar, S., Živaljević, B., and Crnojević, V.: Detection of Double-Cropping Systems Using Machine Learning and Sentinel 2 Imagery - A Case Study of Bačka and Srem Regions, Serbia     , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6225, https://doi.org/10.5194/egusphere-egu22-6225, 2022.

The concept of ‘Digital Earth’ (DE), as outlined in 1999 by the former US Vice-President Al Gore, foresees a “multi-resolution, three-dimensional representation of the planet that would make it possible to find, visualise and make sense of vast amounts of geo-referenced information on physical and social environments”. The DE concept is quickly becoming reality, with a strong dynamic component provided by real time data, forecast and projections. The Copernicus programme provides a fundamental contribution to this concept. The challenge is to access and extract information from distributed data centres containing decades of global and local environmental data generated by in-situ sensors, numerical models, satellites, and individuals.

The Advanced geospatial Data Management platform (ADAM, https://adamplatform.eu/) implements the DE concept: ADAM allows accessing a large variety of multi-year global geospatial collections from satellites (Sentinels, Landsat, MODIS) model analysis and predictions (CAMS, C3S), enabling data discovery, visualization, combination, processing and download. ADAM provides datacubeless access and processing services, namely it exposes multi-dimensional (spatial, temporal, spectral …) subsetting capabilities as well as on-the-fly processing functions, so that the consumer (human or machine) gets only the piece of data wherever and whenever needed, avoiding transferring large amounts of useless bytes or massive local processing. Key feature of the ADAM concept is the standardization of the interfaces: each layer (discovery, access, processing, visualization) exposes OGC (https://www.ogc.org/)-compliant interfaces to foster federation and interoperability.

ADAM is an horizontal (generic) layer to support different vertical domains such as Agriculture, Cultural and natural heritage, marine applications, critical infrastructure monitoring, public health, education and media. This contribution focuses on two main operational applications for atmospheric sciences and climate change assessment and mitigation.

TOP (http://top-platform.eu/) is a web-based platform build on top of the ADAM data exploitation layer offering users from the atmospheric sciences domain a Virtual Research Environment (VRE) to exploit Copernicus atmospheric and climate data products, such as Sentinel-5 P data, CAMS products, European Environmental Agency in-situ measurements. Deployed on the Mund Dias, it is the first operational platform implementing the data triangle (EO, model and in-situ data) and hence creates an atmospheric multi-source data cube, stimulating a multidisciplinary scientific approach due to the availability of various collections.

One of the main effects of evolving climate is change precipitation and temperature regimes: EO provides a fundamental contribution for high resolution monitoring these variables. ADAM offers access to global datasets from Copernicus Climate Change services (C3S), ESA Climate Change initiative (ESA CCI) and the GPM program. In the framework of the ESA EO4SD Climate Resilience cluster (https://eo4sd-climate.gmv.com/), more than 30 climate variables and indicators were computed for climate screening, climate risk assessment and climate adaptation. Indicators are provided to various entities such as the World Bank Climate Change Knowledge Portal (CCKP, https://climateknowledgeportal.worldbank.org/). Another relevant example is the STRENCH project (https://www.interreg-central.eu/Content.Node/STRENCH.html) that allows managers of natural and cultural heritage sites to assess climate risk and define mitigation actions through the use of a dedicated webGIS tool fed by a large pool of climate indicators computed from models and satellite data via ADAM.

How to cite: Natali, S., Mantovani, S., Rendl, C., and Marco Figuera, R.: The ADAM federated data handling platform to enable scientific services development, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11073, https://doi.org/10.5194/egusphere-egu22-11073, 2022.

Earth Observation plays important role in Precision Agriculture. Precision agriculture, holds great promise for modernization of agriculture both in terms of environmental sustainability and economic outlook.  The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates of fertilizer, irrigation and/or pesticide are required for optimal soil productivity and minimized environmental impact. Usage of Earth Observation in Precision Agriculture is in many years subjects of intensive research, but there already exist commercial application. But full potential of EO is till now not utilised. This limits the uptake of precision agriculture technology and thus also the realization of its promised benefits. EO4Agri project in its Strategic Research Agenda identified as one from  priorities for future to support collaborative research of expert from different domains, EO, agriculture, Artificial Intelligence and also direct involvement of farmers or advisors. But till now there didn’t exist platforms, which will be able to support such collaborative research. Now The Map Whiteboard is opening new possibilities for such collaborative research in this domain.

The Map Whiteboard concept at the centre of this submission is intended to plug into the “traditional” workflow of variable rate applications and enables agricultural advisors/extension services and farmers to interact, adjust and share an understanding of the estimations made by the ‘black box’, thus increasing the trust in and improving the quality of the prediction models. The vision of the Map Whiteboard innovation was conceived out of a sequence of large-scale collaborative writing efforts using Google Docs. As opposed to traditional offline word processing tools, Google Docs allows multiple people to edit the same document]—at the same time—allowing all connected clients to see changes made to the document in real-time by synchronising all changes between all connected clients via the server. The ability to work on a shared body of text, avoiding the necessity to integrate fragments from multiple source documents and with multiple styles removed many obstacles associated with traditional document editing. The Map Whiteboard technology seeks to do the same for the traditional use of GIS tools. The overall vision for the technology is that a Map Whiteboard will be to GIS what Google Docs is to word processing. We are now introducing this technology as a tool for collaborative work farmers and advisory services offering them analysis of EO data. The Map Whiteboard is now in intensive tested  and now are integrated tools for online analysis of EO data.

How to cite: Charvat, K., Bergheim, R., Berzins, R., Langovskis, D., Zadrazil, F., and Kubickova, H.: Map WhiteBoard - New Technology in Collaborative Research in Smart FArming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12431, https://doi.org/10.5194/egusphere-egu22-12431, 2022.