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NASA NTRS2019

Image Labeler: A Web Interface to Catalog Earth Science Events

Advances in machine learning (ML) have made it possible to automatically detect Earth science phenomena from satellite imagery. While useful, ML algorithms typically require an extensive dataset containing labeled images for training. Systematic labeling and management of such datasets is quite cumbersome. With this in mind, we present the Image Labeler. Image Labeler is a fast and scalable cloud-based tool that facilitates the rapid development of Earth science event databases, in order to aid automated ML-based image classification.

Case Study↗
NASA NTRS2002

Earth Science Markup Language: Transitioning From Design to Application

The primary objective of the proposed Earth Science Markup Language (ESML) research is to transition from design to application. The resulting schema and prototype software will foster community acceptance for the "define once, use anywhere" concept central to ESML. Supporting goals include: 1. Refinement of the ESML schema and software libraries in cooperation with the user community. 2. Application of the ESML schema and software libraries to a variety of Earth science data sets and analysis tools. 3. Development of supporting prototype software for enhanced ease of use. 4. Cooperation with standards bodies in order to assure ESML is aligned with related metadata standards as appropriate. 5. Widespread publication of the ESML approach, schema, and software.

Moe, Karen↗
NASA NTRS2019

New Observing Strategy (NOS) for Future Earth Science Missions

One of the new thrusts of the Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program is the New Observing Strategy (NOS) thrust. Its goal is to provide a framework for identifying technology advances needed to exploit newly available observational capabilities, particularly to enable the development of the information technologies needed to support planning, evaluating, implementing, and operating dynamic, multi-element sets of observing assets. In this paper, we will introduce relevant NOS terminology and some key concepts before describing the objectives, driving factors and technology goals of this new thrust.

Advanced Information Systems↗
NASA NTRS2019

Federated Cloud Challenges in NASA's Earth Science Data Systems (Why So Difficult?)

NASA is presented with a number of opportunities and challenges in federating its Earth Science Data Systems in the burgeoning world of cloud computing. Cloud hosting of Earth Science data provides a new way of bringing data together, at least from a virtual location sense, and is one of the main motives for NASA to host data there. However, NASA is also faced with a Big Data Variety challenge, brought on by the variety of the EO datasets in its archives. This diversity requires many diverse science archives to service the different science communities. As a result, nearly every major function in the Earth Observing System Data and Information System (EOSDIS) must also be federated across its data centers. This pattern is repeated with many of the outside agencies and organizations that EOSDIS federates with, such as the Committee for Earth Observing Satellites, leading to pioneering work on "deep federation" in a joint project with the European Space Agency to develop a Multi-Mission Algorithm and Analysis Platform.

Lynnes, Christopher↗
NASA NTRSDate not supplied

BERT-E: An Earth Science Specific Language Model for Domain-Specific Downstream Tasks

Language models are fast approaching human-like understanding of natural language. They have been shown to perform equally, if not better than humans in a myriad of language tasks such as next sentence prediction, question answering, entity extraction etc. Part of the success of the models are owed to the fact that they have been trained on varied natural language text over the internet. By virtue of this, the models do not contain the semantic information present in Earth science literature. Hence, there is a lot of room for improvement when using these models for earth science specific tasks. In this work, we showcase our approach on developing Earth science specific language models. Furthermore, we justify the need for such a model by using the embeddings generated by the model to perform a domain specific downstream task that performs better than a generic model.

Prasanna Koirala↗
NASA NTRS2020

The Role of NASA Engineering & Safety Center (NESC) in Advancing NASA's Earth Science Missions (Past, Present, and Future)

The NASA Engineering & Safety Center (NESC) was established in 2003 to provide an independent technical resource for the resolution of challenging technical problems (through the use of studies, analysis, tests, etc.). Since its inception, NESC has completed nearly 1000 technical assessments for NASA’s Human Exploration and Operation Mission Directorate (HEOMD), Science Mission Directorate (SMD), Space Technology Mission Directorate (STMD), and Aeronautics Research Mission Directorate (ARMD). Of the SMD related assessments, several were for the resolution of technical problems, analysis, or studies related to NASA’s Earth science missions in various phases of the project from design to operation. Some of the recent examples of NESC technical support for NASA (or NOAA) Earth science missions have been for: Soil Moisture Active Passive (SMAP), Deep Space Climate Observatory (DSCOVR), Cyclone Global Navigation Satellite System (CYGNSS), Ice, Cloud, and Land Elevation Satellite (ICESat-II), Joint Polar Satellite System (JPSS), and the soon to be launched collaboration mission with India, NASA-ISRO Synthetic Aperture Radar (NISAR). In this paper, we outline some of the technical challenges faced by these Earth science missions and describe how NESC contributed to their resolution. The case studies cover a wide range of disciplines involving space lidars, radars, electronics, attitude control systems, as well as Micrometeoroid Orbital Debris (MMOD) risk assessment impact to NASA missions. The efforts include strategies for risk mitigation, technical resolution of challenging problems, and failure root cause investigations combined with lessons learned reports to advance discipline knowledge, enhance NASA capabilities, and avoid future problems.

NASA↗
NASA NTRS2002

The National Aeronautics and Space Administration's Earth Science Applications Program: Exploring Partnerships to Enhance Decision Making in Public Health Practice

The National Aeronautics and Space Administration (NASA), Earth Science Enterprise is engaged in applications of NASA Earth science and remote sensing technologies for public health. Efforts are focused on establishing partnerships with those agencies and organizations that have responsibility for protecting the Nation's Health. The program's goal is the integration of NASA's advanced data and technology for enhanced decision support in the areas of disease surveillance and environmental health. A focused applications program, based on understanding partner issues and requirements, has the potential to significantly contribute to more informed decision making in public health practice. This paper intends to provide background information on NASA's investment in public health and is a call for partnership with the larger practice community.

Vann, Timi S.↗
NASA NTRS2001

ECHO Responds to NASA's Earth Science User Community

Over the past decade NASA has designed, built, evolved, and operated the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) in order to provide user access to NASA's Earth Science data holdings. During this time revolutionary advances in technology have driven changes in NASA's approach to providing an IMS service. This paper will describe NASA's strategic planning and approach to build and evolve the EOSDIS IMS and to serve the evolving needs of NASA's Earth Science community. It discusses the original strategic plan and how lessons learned help to form a new plan, a new approach and a new system. It discusses the original technologies and how they have evolved to today.

Pfister, Robin↗
NASA NTRSDate not supplied

A Quantitative Analysis On the Use Of Supervised Machine Learning in Earth Science

Several recent papers have investigated different challenges in applying machine learning (ML) techniques to Earth science problems. The challenges listed range from interpretability of the results to computational demand to data issues. In this paper, we focus on specific challenges listed in the review papers that are centered around training data, as the size of training data is important in applying deep learning (DL) techniques. We are in the process of conducting a literature survey to better understand these challenges as well as to understand any trends. As part of this survey, our review has encompassed Earth science papers from AGU, AMS, IEEE and SPIE journals covering the last ten years and focused on papers that utilize supervised ML techniques.

Katrina S Virts↗
NASA NTRS2004

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Teng, William↗
NASA NTRS2004

Proceedings of the 12th JPL Airborne Earth Science Workshop

Participants at the 12th Airborne Earth Science Workshop reported science research and applications results with spectral images measured by the NASA Airborne Invisible/lnfrared Imaging Spectrometer (AVIRIS). The workshop was held in Pasadena, California, from February, 2003.

Green, Robert O.↗
NASA NTRS2011

Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Projects Data and Services at the GES DISC

NASA's Earth Science Program is dedicated to advancing Earth remote sensing and pioneering the scientific use of satellite measurements to improve human understanding of our home planet. Through the MEaSUREs Program, NASA is continuing its commitment to expand understanding of the Earth system using consistent data records. Emphasis is on linking together multiple data sources to form coherent time-series, and facilitating the use of extensive data in the development of comprehensive Earth system models. A primary focus of the MEaSUREs Program is the creation of Earth System Data Records (ESDRs). An ESDR is defined as a unified and coherent set of observations of a given parameter of the Earth system, which is optimized to meet specific requirements for addressing science questions. These records are critical for understanding Earth System processes; for the assessment of variability, long-term trends, and change in the Earth System; and for providing input and validation means to modeling efforts. Seven MEaSUREs projects will be archived and distributed through services at the Goddard Earth Sciences Data and Information Services Center (GES DISC).

Vollmer, Bruce E.↗
NASA NTRS2018

Earth Science Deep Learning: Applications and Lessons Learned

Deep Learning: A subfield of machine learning; Algorithms inspired by function of the brain; Scales with amount of training data; Powerful tool without the need for feature engineering; Suitable for Earth Science applications. Deep Learning for Earth science at MSFC (Marshall Space Flight Center): Phenomena identification; Hurricane intensity (wind speed) estimation; Severe storm (hailstorm) detection; Transverse bands detection; Entity extraction for knowledge graph creation; Ephemeral water detection.

Labeled Data↗
NASA NTRSDate not supplied

Improving Earth Science Dataset Search with Publication

The NASA Goddard Earth Sciences Data and Information Services Center (GESDISC) archives a large number of Earth observational datasets. Thousands of the publications are created each year based on these datasets. The content of these publications can be used for discovery of the datasets based on the characteristics of applicational research. We leverage the content of these publications to retrieve the information about phenomena and domains where measurements from the datasets were utilized through linking these publications and dataset in Knowledge Graph. We retrieve phenomena and domain information using SWEET ontology and produce the set of keywords that are linked to the datasets. Further, we evaluate this link strength according to the frequency of dataset usage in the papers mentioning these keywords. We demonstrate how this linkage can improve dataset search by comparing the search results obtained from Common Metadata Repository (CMR) search and the publications based data.

Kristina Stoyanova↗
NASA NTRS2012

Policy for Robust Space-based Earth Science, Technology and Applications

Over the past six decades, satellite remote sensing technology has contributed to the transformation of using earth science not only to advance science, but to improve quality of life. With satellite missions launched almost every year, new types of earth science data are being incorporated into science, models and decision-making systems in a broad array of organizations. A challenge for space agencies has been ensuring that satellite missions serve both the scientific community and the applied community of decision makers without the missions becoming unfocused and overly expensive. By understanding and considering the needs of the environmental data and applied research user community early on in the mission-design process, agencies can ensure that satellites meet the needs of multiple constituencies. This paper describes the mission development process in the European Space Agency and the National Aeronautics and Space Administration and compares and contrasts the successes of and challenges faced by these agencies in balancing science and applications within their missions.

Brown, Molly E.↗