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NASA NTRSDate not supplied

Addressing User Needs through the Stakeholder Engagement Program

Every two years, the Satellite Needs Working Group (SNWG), an initiative of the U.S. Group on Earth Observations (USGEO), surveys federal agencies to pinpoint their satellite Earth observation needs. For each expressed need, NASA-led assessment teams coordinate with the agencies to devise solutions. Solutions can include existing or modified data products as well as the construction of new data products and technologies, such as the Harmonized Landsat Sentinel-2 (HLS) product and the Catalog of Archived Sub-Orbital Earth Science Investigations (CASEI). To facilitate adoption of new data products and technologies, the SNWG Management Office’s Stakeholder Engagement Program (SEP) was established. The program’s primary goals are to respond to training and capacity building needs expressed by agencies and to encourage engagement from stakeholders as SNWG solutions are developed. To serve these needs, SEP has developed the following: an SNWG Solutions Earthdata webpage, an SEP Earthdata webpage, and an Earthdata Search Portal for SNWG products. These avenues provide assistance to users from all backgrounds and levels of expertise as well as publicize the ongoing efforts of SNWG solutions. In addition, the SEP is also collaborating with NASA’s Short-term Prediction Research and Transition (SPoRT) Center to develop user-driven applications for SNWG products leveraging stakeholder input. This presentation will provide an overview of the SEP, highlight the resources currently available to users, and describe ongoing efforts to address the needs of users, so SNWG products can be better implemented into scientific workflows.

Jenny Wood↗

NASA NTRS2017

Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring (Invited)

Many land applications require more frequent observations than can be obtained from a single 'Landsat class' sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a '30-meter MODIS' global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for approximately 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018. This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations.

Reflectance↗

DOE OSTI2021

Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery

As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Here, our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Promoting Collaborative Open Science through the Stakeholder Engagement Program

Since 2016, the Satellite Needs Working Group (SNWG), an initiative of the U.S. Group on Earth Observations (USGEO), has surveyed federal agencies biennially to identify their satellite Earth observation needs. Coordinating with the agencies, NASA-led assessment teams work to identify solutions for each expressed need. Solutions to a need can involve existing data products or even the formation of new data products and technologies, such as the Harmonized Landsat Sentinel-2 (HLS) product and the Catalog of Archived Sub-Orbital Earth Science Investigations (CASEI). To enhance community engagement with these new data products and technologies, the SNWG Management Office’s Stakeholder Engagement Program (SEP) was established. The SEP within NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) at Marshall Space Flight Center has three goals: to increase awareness of the SNWG survey and its outcomes, to ensure training coordination and outreach efforts supporting integration and use of SNWG solutions, and to enhance coordination among solution development teams, NASA’s Distributed Active Archive Centers (DAACs), SNWG stakeholders, and end user communities. These goals align with NASA Earth Science Data System’s (ESDS) Open Science initiative by establishing a collaborative community focused on enhancing scientific research at diverse levels of understanding. This presentation will highlight SEP’s facilitation of these open science goals through its support in SNWG’s past and current survey cycles as well as its plans for future involvement.

Jenny Wood↗

NASA NTRSDate not supplied

Artemis III Sleep Scheduling with HYPNOS Mission Planning Tool HLS Yawn Prevention N Outer Space (HYPNOS)

HYPNOS ingests critical event data, such as burns and docking, and automatically shifts the crew’s sleep schedule to support these events where possible, while still being compliant with medical restrictions. HYPNOS analyzes the crew’s timeline in 3 states: 1) Awake, meaning the crew is active for work hours 2) Pre- and Post-Sleep, awake periods of limited activity as the crew gets ready to sleep or is waking up 3) Sleep, where the crew is inactive as they rest

Levi Gosdin↗

DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗

NASA NTRS2021

Cosmology with the Roman Space Telescope - Multiprobe Strategies

We simulate the scientific performance of the Nancy Grace Roman Space Telescope High Latitude Survey (HLS) on dark energy and modified gravity. The 1.6-yr HLS Reference survey is currently envisioned to image 2000 deg2 in multiple bands to a depthof∼26.5 in Y, J, H and to cover the same area with slit-less spectroscopy beyond z=3. The combination of deep, multiband photometry and deep spectroscopy will allow scientists to measure the growth and geometry of the Universe through a variety of cosmological probes (e.g. weak lensing, galaxy clusters, galaxy clustering, BAO, Type Ia supernova) and, equally, it will allow an exquisite control of observational and astrophysical systematic effects. In this paper, we explore multiprobe strategies that can be implemented, given the telescope’s instrument capabilities. We model cosmological probes individually and jointly and account for correlated systematics and statistical uncertainties due to the higher order moments of the density field. We explore different levels of observational systematics for the HLS survey (photo-z and shear calibration) and ultimately run a joint likelihood analysis in N-dim parameter space. We find that the HLS reference survey alone can achieve a standard dark energy FoM of>300 when including all probes. This assumes no information from external data sets, we assume a flat universe however, and includes realistic assumptions for systematics. Our study of the HLS reference survey should be seen as part of a future community-driven effort to simulate and optimize the science return of the Roman Space Telescope.

Cosmological parameters↗

NASA NTRS2019

The Use of Landsat 8 and Sentinel-2 Data and Meterological Observations for Winter Wheat Yield Assessment

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2.

Skakun, S.↗

NASA NTRSDate not supplied

Moving Beyond Apollo: Vacuum Ground Testing to Reduce Plume-Surface Interaction Risks to Lunar Landers

NASA’s Artemis Program will return humans to the surface of the Moon for the first time since Apollo using the Human Landing System (HLS). Plume-surface interactions (PSI) pose a potential hazard to all propulsive landing vehicles and future nearby assets that will be part of a sustained lunar architecture. Risks due to uncertainty in PSI predictions have challenged lunar landers since the 1960s, and understanding these phenomena further remains critical to enabling NASA’s lunar exploration goals. To this end, the HLS Program has funded a risk reduction ground test to obtain data relevant for application to environments produced by large landing systems. New data are needed to understand PSI and effects with the potential to differ from those experienced by the Apollo landers. This presentation will discuss the test concept, facility, research goals, methods, and planned data products.

Moon↗

NASA NTRSDate not supplied

AI Foundation Models for Science: An Open Collaborative Initiative

Foundation Models (FMs), AI models designed to replace task-specific models, are increasingly being recognized for their versatility across numerous downstream applications. These models, trained using self-supervised techniques on any type of sequence data, circumvent the need for large annotated datasets, a major bottleneck in traditional AI model development. FMs can be applied to downstream tasks using few-shot learning and fine-tuning, significantly reducing the need for large labeled training datasets and computational resources. However, the development of FMs requires substantial resources, including access to data and compute power, expertise in the latest models, and specialized scientific knowledge for systematic evaluation. It is challenging for a single group to possess all these capabilities. To address this, NASA IMPACT has initiated an open collaborative effort, leveraging partnerships with the private sector and other groups within and outside NASA, to jointly build FMs. The overarching goal is to develop a consistent and collaborative approach to building FMs for high-value science datasets. This initiative has fostered collaboration within NASA and with external partners, including IBM Research, Clark University, DOE’s ORNL, ESA, and USGS. The effort focuses on identifying key datasets with a wide range of downstream applications, pretraining and building FMs using modified transformer architectures, evaluating compute infrastructure needs, and sharing models, pretraining and fine-tuning code, and data with the community. Furthermore, it aims to train the Earth science community to fine-tune these models for various downstream applications. Our initial effort resulted in the creation of a 100 million parameter HLS Geospatial Model within six months, which was released on HuggingFace. We are now expanding our scope to include data from weather and climate models and investigating multimodal models. We invite those interested in participating in this effort to join us by sharing their use cases, expertise, or data.

Rahul Ramachandran↗

NASA NTRS2019

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.

phenological fitting↗

NASA NTRS2024

Alaska Ecological Conservation II: Using NASA Earth Observations to Identify Recent Changes in Vegetation Phenology and Its Impacts on Caribou Calving and Migration

Caribou are known for their long-distance migrations from wintering grounds to specified calving zones. Notably, the Western Arctic Herd (WAH) is the largest caribou herd in Northwestern Alaska that exhibits this behavior. These calving zones are highly predictable, and research suggests that calving and the availability of high-quality vegetation are correlated. We partnered with the U.S. National Park Service in Northwestern Alaska to use Terra and Aqua Moderate Resolution Imaging Spectroradiometer(MODIS)and Harmonized Landsat and Sentinel-2 (HLS) imagery to derive Normalized Difference Vegetated Index (NDVI) values and assess vegetation phenology in calving zones from 2000 –2024. Our partners seek to understand if observed changes in caribou migration patterns and calving events correlate with shifts in the availability of nutrient-rich vegetation. Our results showed spatial variations of highly vegetated zones and annual shifts in the timing and length of growing seasons. While limitations existed in the MODIS and HLS datasets, we demonstrated feasibility using National Aeronautics and Space Administration (NASA)Earth observations for NDVI analysis over the study region. Using location data, our results may further research into caribou calving behaviors.

NDVI↗

NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the xEVAS and HLS contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. We describe the current iterations of a suite of tools to support EVA procedure authoring and execution, for both ISS and Artemis missions, as well as a software solution to establish and interact with mission context and data products. These tools have been developed iteratively and continue to be tested in several NASA facilities such as the Neutral Buoyancy Lab (NBL), Artemis field testing, and in present-day International Space Station (ISS) operations on orbit. Our solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support future human spaceflight missions.

EVA Mission System Software↗

NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the Exploration Extravehicular Activity Services (xEVAS) and Human Landing System (HLS) contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the flight operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. This paper describes the current iterations of a suite of tools to support EVA procedure authoring and execution, and mission context creation for both International Space Station (ISS) and Artemis missions. These tools have been developed iteratively and continue to be used in present-day ISS operations on orbit and in several NASA facilities such as the Neutral Buoyancy Lab (NBL) and Artemis field testing. These solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support both current and future human spaceflight missions.

Matthew J. Miller↗

NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the Exploration Extravehicular Activity Services (xEVAS) and Human Landing System (HLS) contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the flight operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. This paper describes the current iterations of a suite of tools to support EVA procedure authoring and execution, and mission context creation for both International Space Station (ISS) and Artemis missions. These tools have been developed iteratively and continue to be used in present-day ISS operations on orbit and in several NASA facilities such as the Neutral Buoyancy Lab (NBL) and Artemis field testing. These solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support both current and future human spaceflight missions.

Matthew Miller↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Research and development is being conducted to support data-driven design decisions for manual control and human involvement in the lunar landing task under the Human Landing System (HLS) program within the Artemis campaign. A human-in-the-loop simulator evaluation of the manual control of a lunar landing vehicle in the final approach and landing phase was conducted at NASA Langley Research Center in the Lunar Flight Deck simulator using the Altair Design and Analysis Cycle (DAC)-2 government reference vehicle. The objective was to perform a direct comparison of control law types with display aiding for various rotational control powers being considered under HLS. Ten subjects (four NASA test pilots and six current pilot astronauts) provided Cooper-Harper ratings, NASA Task Load Index workload ratings, and qualitative comments. The piloting task was to assume manual control of the vehicle (including vertical descent rate) at 150 m above the landing zone, fly to a redesignated landing target (which was up to 75 m radially from the center of the landing zone) and to touch down within a position accuracy of 5m. The data showed that the display augmentation in the form of a “hover cue” significantly improved the pilot’s ability to control translation and create satisfactory handling qualities for otherwise sluggish configurations; however, the investigation also showed that display augmentation is not a panacea. Handling qualities problems, including pilot-induced oscillations, and higher workload for the lowest control powers can still be evident.

Lynda J. Kramer↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Lynda Kramer↗

NASA NTRS2024

Navigation Performance Overview of Gateway During a Lunar Lander Mission

Starting with Artemis IV, the human lander system (HLS) missions will utilize the Gateway as a staging point in a near rectilinear halo orbit (NRHO) between cislunar space and the lunar surface. The presence of a crew, Orion, and HLS will cause significant perturbations from docking and venting, while attitude requirements on Gateway can cause structural antenna blockage. The HLS mission timeline and perturbations are simulated considering antenna blockage to ground stations. Simulated DSN tracking data is generated and processed to produce a navigation state estimate for orbit maintenance maneuver (OMM) targeting. The starting epoch is varied to adjust tracking availability, and the volume of tracking data processed is reduced. Separately, the observability of perturbations in the NRHO with two-way tracking data is analyzed. The mission is simulated with imperfect knowledge of venting perturbations, and finally the estimation errors from propagating an estimated state from data cutoff (DCO) to maneuvers are investigated.

Clark P Newman↗