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The demands on global agriculture to support human and animal life while minimizing the environmental impacts of excess nutrient runoff makes the refinement of nutrient management protocols of paramount importance. A key factor in such protocols is developing and adopting effective and accurate soil testing methods. As new soil testing technologies emerge it becomes challenging for local and national agencies to determine whether to incorporate these technologies into their existing procedures for assessing soil quality.We describe a proposed new standards project for developing a comprehensive protocol and policy proposal to be used in validating innovative soil testing methodologies. We emphasize the importance of using an open consensus-based standards development process, sponsored by the The Geoscience and Remote Sensing Society and overseen by the (Institute of Electrical and Electronics Engineers) IEEE Standards Association. By emphasizing the need for collaboration, transparency, and adherence to state-approved methodologies we are creating an optimal pathway for its acceptance by users and state regulatory bodies and an overall acceptance on an international level.

Abstract. Improvements in process representation in numerical weather prediction (NWP) models requires informed collaboration between scientists making research-grade observations and scientist developing state-of-the-art NWP models. As a result, progress in model quality relies heavily on the ability to efficiently evaluate and reliably reconcile these two sources of information. To facilitate such progress, with focus on enhanced model skill in polar regions, the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) community defined the Merged Data File (MDF) format. The file format is designed for high temporal and spatial resolution data for direct comparison between observations and model output to assess parameterized processes under various conditions. A broad overview of the MDF format is provided along with supporting use-cases defined by the research community, and present a set of free, open-source, computational tools for creating and utilizing this standardized format. Two free open source Python packages are discussed: 1) “The MDF toolkit", a data processing library for the creation of standardized datasets, and 2) "MDF visualization", a set of Python codes in notebook format that accelerate model evaluation and climate process research utilizing the MDF format. The benefits of such tools that may help unite diverse groups of researchers through a common data-format language are also discussed.

Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, prompting the need for automated deep learning-based classification approaches. However, training these algorithms is challenging because expert-generated ice charts, commonly used as training data, do not map single ice types but instead map polygons with multiple ice types. Moreover, the distribution of various ice types in these charts is frequently imbalanced, resulting in a performance bias toward the dominant class. In this article, we present a novel GeoAI approach to training sea ice classification by formalizing it as a partial label learning task with explicit confidence scores to address multiple labels and class imbalance. We treat the polygon-level labels as candidate partial labels, assign the corresponding ice concentrations as confidence scores to each candidate label, and integrate them with focal loss to train a convolutional neural network. Our proposed approach leads to enhanced performance for sea ice classification in Sentinel-1 dual-polarized SAR images, improving classification accuracy (from 87% to 92%) and weighted average F-1 score (from 90% to 93%) compared to the conventional training approach of using one-hot encoded labels and categorical cross-entropy loss. It also improves the F-1 score in four out of the six sea ice classes.

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed.

A large and ever-growing body of geophysical information is measured in campaigns and at specialized observatories as a part of scientific expeditions and experiments. These collections of observed data include many essential climate variables (as defined by the Global Climate Observing System) but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment but also the drivers that contribute to that state. These field data are used not only to further understand environmental processes through observation-based studies but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation–model research, the Merged Observatory Data File (MODF) concept has been developed. A MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel the development of corresponding Merged Model Data Files (MMDFs). Using the MODF and MMDF protocols will facilitate the evolution of model intercomparison projects into model intercomparison and improvement projects by putting observation and model data “on the same page” in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus, this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during that first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can be considered a MODF creation manual.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). *Retired Corresponding author: Thomas Jung, thomas.jung@awi.de

IEEE GRSS is the premier engineering society addressing remote sensing for the geosciences. A vast amount of expertise is available through GRSS publications, conferences, and the activities of its committees. The GRSS Standards Committee was established to create open consensus standards based on GRSS expertise. Standards are the pinnacle artifact of the knowledge base that supports technology development. To plan the development of standards, the Standards Committee is developing an enterprise model of the GRSS scope. The Enterprise Model will be a framework for standards development. The model will be used to identify existing GRSS expertise, existing standards, and new standards that need to be developed.

The rapid development of new remote sensing technologies and the increased need for access to, and ready utilization of remote sensing products are motivating the development of new technical standards. Standardization benefits research by improving interoperability, helps accelerate the adoption of new methods and technologies, and is vital for the commercialization of new sensors. The GRSS Standards Committee aims to promote a coordinated suite of standards that builds on uniqueness of GRSS expertise. We provide an overview of the GRSS Standards Committee activities and the impacts that the standards we develop are expected to have on science and society.

Data is the lifeblood of the geosciences. Furthermore, the acquisition, processing and interpretation of data all depend on established specifications describing the systems and procedures that were used in producing, describing and distributing that data. It can be said that technical standards underpin the entire scientific endeavour. This is becoming ever truer in the era of Big Data and Open, Transdisciplinary Science. It takes the dedicated efforts of many individuals to create a viable standard. This presentation will describe the experiences and status of standards development activities related to geoscience remote sensing technologies which are being carried out under the auspices of the IEEE Geoscience and Remote Sensing Society (GRSS).A Standards Development Organization (SDO) exists to provide the environment, rules and governance necessary to facilitate the fair and equitable development of standards, and to assist in the distribution and maintenance of the resulting standards. The GRSS sponsors projects with the IEEE Standards Association (IEEE-SA), which, like other SDOs such as ISO and OGC, has well-defined policies and procedures that help ensure the openness and integrity of the standards development process. Each participant in a standards working group typically brings specific interests as a producer, consumer or regulator of a product, process or service. Creating an environment that makes it possible to find consensus among competing interests is a primary role of an SDO. I will share some of the insights gained from the six standards projects that the GRSS has initiated which involve hyperspectral imagers, the spectroscopy of soils, synthetic aperture radar, microwave radiometers, GNSS reflectometry, and radio frequency interference in protected geoscience bands.

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data and need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.

Data is the lifeblood of the geosciences.The acquisition, processing and interpretation of data all depend on established specifications describing the systems and procedures that were used in producing, describing and distributing that data.It can be said that technical standards underpin the entire scientific endeavour.This is becoming ever truer in the era of Big Data and Open, Transdisciplinary Science.It takes the dedicated efforts of many individuals to create a viable standard.This presentation will describe the experiences and status of standards development activities related to geoscience remote sensing technologies which are being carried out under the auspices of the IEEE Geoscience and Remote Sensing Society (GRSS).While the value and viability of community-developed principles and specifications have been amply demonstrated, a Standards Development Organization (SDO) exists to provide the environment, rules and governance that are needed to ensure the fair and equitable development of a standard, and to assist in the distribution and maintenance of the resulting standard.The GRSS sponsors projects with the IEEE Standards Association (IEEE-SA), which, like other SDOs such as ISO and OGC, has well-defined policies and procedures that help ensure the openness and integrity of the standards development process.Each participant in a standards working group typically brings specific interests as a producer, consumer or regulator of a product, process or service.Creating an environment that makes it possible to find consensus among competing interests is a primary role of an SDO.This presentation will include highlights and insights gained from the seven standards projects that the GRSS has initiated.These projects involve hyperspectral imagers, the spectroscopy of soils, data from synthetic aperture radars and GNSS reflectometry, calibration of microwave radiometers, and the characterization of radio frequency interference in protected geoscience bands.

The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS "Standards for Earth Observation Technical Committee" to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title "Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content." At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA.

NASA’s spaceborne laser altimeter, ICESat-2, sends 10,000 laser pulses per second towards Earth, in 6 separate beams, and records individual photons reflected back to its telescope. From these photon elevations, specialized ICESat-2 data products for land ice, sea ice, sea surface, land surface, vegetation and inland water are generated. Altogether these products total nearly 1 TB per day, which poses data management/visualization challenges for potential users. OpenAltimetry, a browser-based interactive visualization tool, was built to provide intuitive access to data from ICESat-2 and its predecessor mission (ICESat). It emphasizes ease of use and rapid access for expert and non-expert audiences alike. The initial design choices and subsequent user-informed development have led to a tool that has been enthusiastically received by the ICESat-2 Science Team, researchers from various disciplines, and the general public. This presentation will highlight the elements that led to OpenAltimetry’s success.

Earth Observation data, as one element of the rapidly expanding world of Big Data, holds the potential to assist in addressing many of the societal and environmental problems facing humanity. Standards, especially community-developed open standards, are an important part of the solution. This paper describes the activities of the IEEE GRSS Standards for Earth Observations Technical Committee in developing standards to improve accessibility and interpretability of data from several kinds of remote sensing technologies.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

The rapid development and deployment of new remote sensing instrumentation is transforming the way we acquire and interpret information on all aspects of environmental and human activity. This is driving the need for technical standards that support the processing, management, analysis, and quality assessment of data from these sources. The IEEE Geoscience and Remote Sensing Society (GRSS) Standards for Earth Observations (SEO) Technical Committee (TC) was created in 2017 to support this need. To date, it has sponsored four standards development projects, with two new projects starting soon. This article highlights the scope of each and looks at future directions.

NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a laser altimeter that fires 10,000 pulses per second towards Earth and records the travel time of individual photons to measure the elevation of the surface below. The volume of data produced by ICESat-2, nearly a TB per day, presents significant challenges for users wishing to efficiently explore the dataset. NASA's National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which is responsible for archiving and distributing ICESat-2 data, provides search and subsetting services on mission data products, but providing interactive data discovery and visualization tools needed to assess data coverage and quality in a given area of interest is outside of NSIDC's mandate. The OpenAltimetry project, a NASA-funded collaboration between NSIDC, UNAVCO and the University of California San Diego, has developed a web-based cyberinfrastructure platform that allows users to locate, visualize, and download ICESat-2 surface elevation data and photon clouds for any location on Earth, on demand. OpenAltimetry also provides access to elevations and waveforms for ICESat (the predecessor mission to ICESat-2). In addition, OpenAltimetry enables data access via APIs, opening opportunities for rapid access, experimentation, and computation via third party applications like Jupyter notebooks. OpenAltimetry emphasizes ease-of-use for new users and rapid access to entire altimetry datasets for experts and has been successful in meeting the needs of different user groups. In this paper we describe the principles that guided the design and development of the OpenAltimetry platform and provide a high-level overview of the cyberinfrastructure components of the system.

As data repositories make more data openly available it becomes challenging for researchers to find what they need either from a repository or through web search engines. This study attempts to investigate data users' requirements and the role that data repositories can play in supporting data discoverability by meeting those requirements. We collected 79 data discovery use cases (or data search scenarios), from which we derived nine functional requirements for data repositories through qualitative analysis. We then applied usability heuristic evaluation and expert review methods to identify best practices that data repositories can implement to meet each functional requirement. We propose the following ten recommendations for data repository operators to consider for improving data discoverability and user's data search experience: 1. Provide a range of query interfaces to accommodate various data search behaviours. 2. Provide multiple access points to find data. 3. Make it easier for researchers to judge relevance, accessibility and reusability of a data collection from a search summary. 4. Make individual metadata records readable and analysable. 5. Enable sharing and downloading of bibliographic references. 6. Expose data usage statistics. 7. Strive for consistency with other repositories. 8. Identify and aggregate metadata records that describe the same data object. 9. Make metadata records easily indexed and searchable by major web search engines. 10. Follow API search standards and community adopted vocabularies for interoperability.