In recent years the European Space Agency (ESA) supported the development of the European ecosystem of Earth Observation cloud platforms and services for Earth Observation. Having reached a level of maturity, such solutions are emerging as important tools for new scientific discovery about the Earth system and to develop stakeholder-driven, science-based applications. Looking towards a long-term sustainable approach that maximises the utilization of scientific outputs and their transition into applications, ESA is proposing EarthCODE – a FAIR Open Science Environment for earth system science as an implementation of its EO Open Science and Innovation vision. The vision of EarthCODE is to enable long-term persistence of the end-to-end scientific experiments (including data, code, workflows and documentation), looking to maximise reproducibility, reuse, and valorisation of science by the wider community. EarthCODE is promoting a flexible and scalable architecture based on existing EO Platforms (from the ESA Network of resources), leveraging interoperability and standards.

Precipitation is a key variable of earth observation systems, involved in a wide spectrum of applications, from the optimal water resources management to early warning systems for flood/drought events. During the last decade, significant research effort has been given in the development of satellite-based precipitation products (SPPs), with improved accuracy and high spatio-temporal coverage. This work presents a methodological framework that couples machine learning and explainability techniques, along with uncertainty quantification methods, to develop a new SPP for the Mediterranean region. Specifically, the methodology couples: a) ensemble (tree-based) machine learning algorithms to merge multiple satellite-based observations (i.e., both precipitation and soil moisture-based products), b) global and local explainable AI techniques to provide explanations at the model and instance level, respectively, and c) a copula-based approach to quantify the predictive uncertainty of the final integrated product. The new product fuses data from two widely known low-latency SPPs, i.e., the SM2RAIN-ASCAT and GPM Late Run dataset, while its performance is assessed against state-of-art products, i.e., the EMO5, GPM Final Run, ERA5 and PERSIANN CDR.

International collaboration among space agencies is central to the success of satellite Earth observations and data analysis, aiming at providing an accurate and timely information to decision-makers, main stakeholders and public. These partnerships foster more comprehensive measurements, robust datasets, and cost-effective missions. The European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA), and National Aeronautics and Space Administration (NASA) have combined their resources, technical knowledge, and expertise to produce the Earth Observing Dashboard [1], which strengthens our understanding of global environmental changes and other societal challenges impacting our planet [2].

With Earth Observation (EO) missions generating tens of TB of data every day, much of it available in a free and open manner, systematically observing our planet, the opportunities for society to benefit from this data are endless. There are, however, remaining challenges of reusability, reproducibility, and scalability of the scientific analysis run on EO data. This often results in interesting findings addressing the situation from a year or two ago, due to time required for validation of the data.

The Massive Open Online Course – Earth Observation Open Data Science (MOOC EOODS) teaches the concepts of data cubes, cloud platforms, and open science in the context of Earth Observation (EO). The course is designed to bridge the gap towards the recent cloud native advancements in EO by offering a MOOC as an integrated and open learning experience relying on a mixture of animated lecture content and hands-on exercises hosted on the EO themed e-learning platform EO College.

The measures to contain the Covid-19 pandemic have had worldwide impacts on our environment, societies and economies. The ‘COVID-19 Earth Observation Dashboard’ (https://eodashboard.org) jointly developed by ESA, NASA and JAXA, combines a wealth of data from the tri-agencies' Earth-observing satellites to monitor the worldwide impacts of COVID-19. Developed in an Open Science framework, the dashboard is openly available to users worldwide and allows to track changes in air and water quality, climate, economic activity and agriculture.

This paper introduces the Rapid Action for Coronavirus and Earth Observation, a joint ESA-EC initiative to showcase applications of Earth Observation data derived from the Copernicus Sentinels and Third Party Missions to generate timely information with societal interest on the changes observed during the coronavirus pandemic lockdown and post lockdown periods on the European economy, agriculture, air quality, water quality and land. All indicators are based on Sentinel data and derived by capitalizing on EO Platforms and advanced Artificial Intelligence as a key enabler and accelerator of information discovery. Pioneering flexible and rapid development and deployment of the dashboard within EuroDataCube, the https://race.esa.int product is a fully Open Source solution developed using state-of-the-art technologies in an Open Science framework. Through coding competitions and coaching of citizen contributions, innovative community indicators were integrated using an Agile and Design Thinking Product Development Methodology.

The COVID-19 pandemic has brought difficulties to daily lives and caused various changes in socio-economic activities and the social environment. Since the satellite-based Earth observation allows us to monitor the Earth's surface globally and periodically with various physical parameters, as space agencies, it is our responsibility to monitor the changes of the Earth from space, share the observation results with the public, and record them for future generations. Aiming to fulfill this obligation, JAXA has cooperated with NASA and ESA to develop the Earth Observing Dashboard, which provides information on the changes of the Earth environment before/after COVID-19 pandemic. This paper discusses the overview of the three agency's cooperation on COVID-19 dashboard.

Understanding deforestation drivers is important for climate change mitigation, forest management and effective control measures. Charcoal production is thought to be one of the main drivers of forest degradation in many Sub-Saharan Africa (SSA) regions. Due to the difficulty to detect it, the industry impact is highly uncertain. In this paper we explore means to improve the understanding of forest degradation due to charcoal production in SSA by leveraging the capacity for large area systematic observation of Copernicus Sentinel-1 and Sentinel-2 satellites. We utilise Sentinel-1 SAR (Synthetic Aperture Radar) time series data to distinguish charcoal production patterns at regional scale. We compare different statistical and Machine Learning approaches. The results indicate that Deep Learning methods give the best performance in terms of accuracy and scalability, achieving just under 90% accuracy over test and validation areas. Future work looks to generalize the analysis and extend to country level characterization of degradation.