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NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS2020

Evaluation of Tablet-Based Methods for Assessment of Contrast Sensitivity

Some astronauts have suffered degradation of vision during long-duration space flight, suffering from a condition that has come to be known as Spaceflight Associated Neuro-ocular Syndrome (SANS). While related morphological changes can be observed with imaging technologies such as optical coherence tomography (OCT), it may be useful to have a rapid method for functional vision assessment. In this paper, we compare three tablet-based methods for rapid assessment of contrast sensitivity. First, a relatively novel method developed expressly for touch screens, in which the subject ”swipes” a frequency/contrast sweep grating to indicate the boundary between visible and invisible patterns; second, a method-of-adjustment task in which the subject adjusts the contrast of a grating patch up and down to bracket the visual threshold; and third, a traditional temporal two-alternative forced choice (2AFC) task, in which the subject is presented with a near-threshold stimulus in one of two intervals, and must report the interval containing the stimulus. The swipe method shows variability comparable to the 2AFC method, and shows good agreement in estimates of the spatial frequency of peak sensitivity. The absolute sensitivity estimated with the swipe method is higher than that of the other methods, perhaps because subjects are biased to trace outside of the visible pattern region, or perhaps due to stimulus differences.

Mulligan, Jeffrey B.↗
NASA NTRS2013

Rapid Assessment of Contrast Sensitivity with Mobile Touch-screens

The availability of low-cost high-quality touch-screen displays in modern mobile devices has created opportunities for new approaches to routine visual measurements. Here we describe a novel method in which subjects use a finger swipe to indicate the transition from visible to invisible on a grating which is swept in both contrast and frequency. Because a single image can be swiped in about a second, it is practical to use a series of images to zoom in on particular ranges of contrast or frequency, both to increase the accuracy of the measurements and to obtain an estimate of the reliability of the subject. Sensitivities to chromatic and spatio-temporal modulations are easily measured using the same method. We will demonstrate a prototype for Apple Computer's iPad-iPod-iPhone family of devices, implemented using an open-source scripting environment known as QuIP (QUick Image Processing,

mobile devices↗
NASA NTRS2016

A Method for Rapid Measurement of Contrast Sensitivity on Mobile Touch-Screens

Touch-screen displays in cell phones and tablet computers are now pervasive, making them an attractive option for vision testing outside of the laboratory or clinic. Here we de- scribe a novel method in which subjects use a finger swipe to indicate the transition from visible to invisible on a grating which is swept in both contrast and frequency. Because a single image can be swiped in about a second, it is practical to use a series of images to zoom in on particular ranges of contrast or frequency, both to increase the accuracy of the measurements and to obtain an estimate of the reliability of the subject. Sensitivities to chromatic and spatio-temporal modulations are easily measured using the same method. A proto- type has been developed for Apple Computer's iPad/iPod/iPhone family of devices, implemented using an open-source scripting environment known as QuIP (QUick Image Processing, http://hsi.arc.nasa.gov/groups/scanpath/research.php). Preliminary data show good agreement with estimates obtained from traditional psychophysical methods as well as newer rapid estimation techniques. Issues relating to device calibration are also discussed.

contrast sensitivity↗
NASA NTRS2012

Examination of Surface Residuals Obtained During Re-Lubrication of the International Space Station (ISS) Solar Alpha Rotary Joint (SARJ)

The starboard SARJ mechanism on the ISS suffered a premature lubrication failure, resulting in widespread loss of the nitride case layer on its 10.3 meter circumference, 15-5PH steel race ring [1, 2]. To restore functionality, vacuum-stable grease was applied on-orbit, first to the port SARJ mechanism to save it from the damage suffered by the starboard mechanism. After 3 years of greased operation, telemetry indicated that the port mechanism required relubrication, so part of that process included sampling each of the three race ring surfaces to evaluate any wear debris recovered and the state of the originally applied grease. Extensive microscopic examination was conducted, which directed subsequent microanalysis of particulate. Since the SARJ mechanism operates in the vacuum of space, a sampling method and tool had to be developed for use by astronauts while working in the extravehicular mobility unit (EMU). The sampling tool developed was a cotton terry-cloth mitt for the EMU glove, with samples taken by swiping each of the three port SARJ race-ring surfaces. The sample mitts for each surface were folded inward after sampling to preserve sample integrity, for return and ground analysis. The sample mitt for what is termed the outer canted surface of the SARJ race-ring is shown in Figure 1. Figure 1 also demonstrates how increasing levels of magnification were used to survey the contamination removed in sampling, specifically looking for signs of wear debris or other features which could be further evaluated using Scanning Electron Microscopy (SEM) methods. The most surprising overall result at this point in the analysis was the relatively small amounts of grease recovered during sampling. It is clear that the mechanism was not operating with surplus lubricant. Obviously, evidence of molybdenum disulfide (MoS2), a major component in the grease applied, was prevalent in the analysis conducted. But a small amount of mechanism wear debris was observed. Figure 2 shows an example of a region of concentrated wear debris. Although some MoS2 is observed, most of the contaminant in this location is nitrided 15-5PH steel, as verified by the associated chemical analysis. High oxygen content was also observed which, when associated with the apparent friable nature of the steel material, suggests that this contaminant could be quite old, perhaps even associated with the mechanism s original manufacture and acceptance testing. Additional microscopic

Martinez, J. E.↗
NASA NTRS2017

Challenges in Obtaining and Visualizing Satellite Level 2 Data in GIS

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. One way to help users better understand the satellite data is to provide data along with Images, including accurate pixel coverage area delineation, and science team recommended quality screening for individual geophysical parameters. However, there are challenges of visualizing remote sensed non-gridded products: (1) different geodetics of space-borne instruments (2) data often arranged in a long-track and a cross-track axes (3) spatially and temporally continuous data chunked into granule files: data for a portion (or all) of a satellite orbit (4) no general rule of resampling or interpolations to a grid (5) geophysical retrieval only based on pixel center location without shape information. In this presentation, we will unravel a new Goddard Earth Sciences Data and Information Services Center (GES DISC) Level 2 (L2) visualization on-demand service. The service's front end provides various visualization and data accessing capabilities, such as overlay and swipe of multiply variables and subset and download of data in different formats. The backend of the service consists of Open Geospatial Consortium (OGC) standard-compliant Web Mapping Service (WMS) and Web Coverage Service. The infrastructure allows inclusion of outside data sources served in OGC compliant protocols and allows other interoperable clients, such as ArcGIS clients, to connect to our L2 WCS/WMS.

GI↗
NASA NTRSDate not supplied

Neural Network Atmospheric Correction of Remote Sensing Imagery Over Water Using a Synthetic Dataset

Remote sensing atmospheric correction methods have primarily focused on imagery over land. However, accurate correction over water is important for monitoring and research of aquatic environments. More research in this area is ongoing, though one of the biggest challenges is enough quality data to develop and validate correction methods. This is especially true for neural network (NN) -based models which have shown promise in this area given enough quality data. To address this deficiency of data, we are leveraging a synthetic dataset produced by a model called SWIPE that uses radiative transfer modeling to simulate the atmospheric effects on water-leaving (WL) reflectance to estimate top-of-atmosphere (TOA) reflectance. This allows us to produce almost unlimited pairs of WL reflectance and corresponding TOA reflectance for model training across a variety of atmospheric conditions. We use two approaches for our atmospheric correction model. One uses a conditional variational autoencoder (VAE) to estimate a single WL reflectance value from a single TOA reflectance value. The second is based on a UNET architecture and estimates an array of WL reflectance values from an array of TOA reflectance values. The goal of the second method is to capture atmospheric effects that occur spatially between values within the array as compared to the first method.

deep learning↗
NASA NTRSDate not supplied

Visualizing Geospatial Data through ESRI Story Maps for Earth Science Education: Lessons Learned from My NASA Data

For 20 years My NASA Data (MND) has curated NASA Earth science data and provided the data to educators in engaging learner-centered resources. MND has recently featured story maps as an innovative way to engage students in NASA Earth data. A story map is a cloud-based lesson that engages the learner in interactive geospatial maps using NASA data, and other multimedia content, text, and tasks that can be seamlessly incorporated in classroom instruction. This immersive technology eliminates the need for the user to move among tricky interfaces to access and visualize Earth science data, and no special software is required to be downloaded. Each story map integrates data from different NASA satellite missions, retrieved from Distributed Active Archive Centers (DAACs). Story maps also employ data analysis tools, such as time series options and swipe tools that allow learners to view and analyze relationships between scientific variables. MND has produced 25 story map lesson plans on the topics of air quality, the urban heat island effect, Earth’s energy budget, phytoplankton distribution, hurricane formation, solar eclipses, ocean circulation patterns, sea ice extent, and volcanic eruptions. Nine of them are extended story maps and written in the 5E format, which is internationally recognized as best practice based on how children learn science. Each story map resource is developed by the MND team featuring a GIS programming specialist, a lead scientist, and educational specialist/s to ensure the context, content, and methods are scientifically and educationally sound. The MND story maps are written for middle and high school science teachers and students as they connect with the Earth Systems Science phenomena featured in the Next Generation Science Standards. Each story map includes supporting resources for smooth integration in the classroom. During Fiscal Year 2023, The My NASA Data website received over 100,000 story map engagements during. These metrics highlight the interest in story maps as an Earth Science educational resource.

Desiray Wilson↗