The increasing availability and accessibility of commercial satellite data presents a unique opportunity for the Earth observation (EO) community to leverage new sources of remote sensing imagery. However, despite improved access to data and computation, conducting analyses with data from multiple platforms remains challenging due to interoperability barriers. Calibration and Validation (Cal/Val) efforts are essential to overcoming these obstacles by ensuring the quality and reliability of EO data and quantifying necessary adjustments for seamless integration. The Committee on Earth Observation Satellites (CEOS) Systems Engineering Office (SEO) has launched the CEOS Analytics Lab (CAL), an Open Data Cube (ODC)-based tool, to facilitate data processing and collaboration among researchers. In this paper, we present a pilot study using tools developed for CAL to compare commercial imagery to traditional civil government Earth observations. We demonstrate the usefulness of collaborative environments such as CAL and provide the community with tools which assist with commonly identified calibration and validation tasks necessary to perform interoperability studies. This demonstration lays a foundation for operations around the use and evaluation of commercial satellite data, supporting further developments in the CEOS and Open EO communities. We also present a set of tools, CalValToolbox, to the EO community to perform Cal/Val tasks.

Riparian and shoreline buffers are vital landscape elements of coastal and riverine shorelines, providing significant ecosystem services, habitats, and aesthetic qualities. One critical role is the filtration and uptake of non-point source pollutants, particularly in estuaries that have seen extensive land cover change, loss of wetlands, and that face sea-level rise such as the Chesapeake Bay. Combining a remote sensing and coastal ecological approach, this project presents data science tools to investigate coastal riparian and shoreline vegetation relationships with in situ water quality sampling. This project provides tools and examples for quantifying riparian buffer vegetation, calculating Total Suspended Matter (TSM) from Earth Observation (EO) data, and demonstrates the use of data science methods to explore relationships between non-point source pollutants.

In 2015, all United Nations (UN) Member States adopted the 2030 Agenda for Sustainable Development. The Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The Committee on Earth Observation Satellites (CEOS) Systems Engineering Office (SEO) team has recently developed and released a set of innovative notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. The main contributions in this paper are: (1) briefly describing the framework of the UN SDG notebooks, (2) enumerating the notebooks' salient features, and (3) discussing current limitations and proposing approaches to overcome these limitations.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.

There is an urgent need to increase the capacity of developing countries to take part in the study and monitoring of their environments through remote sensing and space-based Earth observation technologies. The Open Data Cube (ODC) provides a mechanism for efficient storage and a powerful framework for processing and analyzing satellite data. While this is ideal for scientific research, the expansive feature space can also be daunting for end-users and decision-makers who simply require a solution which provides easy exploration, analysis, and visualization of Analysis Ready Data (ARD). Utilizing innovative web-design and a modular architecture, the Committee on Earth Observation Satellites (CEOS) has created a web-based user interface (UI) which harnesses the power of the ODC yet provides a simple and familiar user experience: the CEOS Data Cube (CDC). This paper presents an overview of the CDC architecture and the salient features of the UI. In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The fully-customizable source code of the UI is available at our public repository. Interested parties can download the source and build their own UIs. The UI empowers users by providing features that assist with streamlining data preparation, data processing, data visualization, and sub-setting ARD products in order to achieve a wide variety of Earth imaging objectives through an easy to use web interface.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using Amazon Web Services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives.

In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multi-disciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources---the Argonne Leadership Computing Facility (ALCF), Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)---to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort.

In this paper, we present an architecture that encapsulates system hardware inside a software component used for job execution and status monitoring. The development of this interface has enabled system simulation, which yields a number of novel benefits, including dramatically improved debug and testing capabilities.