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James MacKinnon

Publications and source records attributed to James MacKinnon.

12 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Automated UAS Measurements of Reflectance and Solar Induced Florescence (SIF) for Assessment of the Dynamics in Photosynthetic Function, Application for Maize (Zea Mays L.) in Greenbelt, Maryland, US

To enable monitoring of the parameters governing photosynthesis at the temporal frequency relevant to their dynamics and at a spatial scale that allows practical assessment and management there is a strong need for advancement in the UAS remote sensing methods and instruments. Currently, no single sensor can provide data at the desired high temporal, spectral and spatial resolutions. Our field measurements obtained using the integrated UAS Piccolo system during the summers of 2017 and 2018 demonstrate that science quality reflectance and solar induced fluorescence (SIF) data can be retrieved with high temporal frequency using small Unmanned Aerial Systems (UAS). The implemented approach facilitates consistent data comparisons in space and time and facilitates their integration with other spectral satellite and airborne data.

Petya Campbell↗
NASA NTRSDate not supplied

Adaptive Wavelength Scanning Lidar (AWSL) for 3D Mapping from Space

We present the design and performance of an adaptive wavelength scanning lidar (AWSL) for highly efficient mapping from low Earth orbit (LEO). Mapping is accomplished by steering a laser beam across 1,200 resolvable spots using wavelength tuning and grating dispersion. Any subset of these 1,200 spots can be selected by wavelength switching. The design is validated with an 1550-nm prototype using a fast wavelength-tuned (500-kHz) pulsed (2-ns) fiber laser with the beam dispersed by gratings for beam steering. Reflected pulses are detected with an eight-pixel detector array with single-photon sensitivity. Eight lidar returns are time-multiplexed to one output that is digitized with a single 1-GSPS-digitizer, to save power. A grating spectrometer rejects solar background noise spatially and spectrally, and images laser footprints on to the detector array. The gratings retain the fiber laser beam quality. We are developing a 1030-nm AWSL intended for a LEO SmallSat platform.

Guangning Yang↗
NASA NTRSDate not supplied

Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science

Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.

Distributed Systems↗
NASA NTRSDate not supplied

Development of Concurrent Artificially-Intelligent Spectrometry and Adaptive Lidar System (CASALS) for Swath Mapping From Space

We present the design and performance of a Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) for 3D imaging from Space. With a single fast wavelength tuning laser, CASALS accomplishes a 1,200 resolvable spots swath mapping by grating dispersion wavelength steering. Any subset of these 1,200 spots can be selected by wavelength switching. The validation operating principle was accomplished and reported in IGASS-2022. With configurable base design, we report the designs and progress of the CASALS airplane campaign with 256 contiguous ground spots. It is accomplished with a single fast tuning lase at 1040-nm, 1.152MHz tuning rate, and pulse modulated 2-ns on each wavelength. Return pulses are mapped to an eight-pixel detector array with single-photon sensitivity. The lidar returns are time-multiplexed to two outputs that are digitized with two 1-GSPS-digitizer. A grating spectrometer rejects solar background noise spatially and spectrally. We are developing a 1040-nm CASALS intended for multiple LEO orbit missions: Earth Venture Mission on ESPA Grande, STV Mission on ESPA Grande SmallSat, STV Mission on spacecraft equivalent to ICESat power (SWaP,) and splitting the laser beam requires higher laser pulse energy.

ranging lidar↗
NASA NTRSDate not supplied

The West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE): A Prototype for A PBL Mission of Missions

We present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment(WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.

Planetary Boundary Layer↗
NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗