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NASA NTRS2018

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept.

Kawa, S. Randolph↗

NASA NTRS2019

Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 Years of Progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Review↗

NASA NTRSDate not supplied

NASA’s NextGen Remote Sensing Instruments Have Arrived: Data Products For Studying Disease Vectors

Remote sensing can be used to measure, evaluate or estimate both the environment (state functions) and interfaces (processfunctions) defining vector habitats. The products of remote sensing can be integrated directly into the epidemiological equationsto significantly enhance our understanding of disease vector’s life cycles and habitats. The next generation of NASA’s remotesensing instruments which have become recently operational will provide a significant enhancement in our ability to studydisease vector’s life cycles and habitats. These instruments are on the International Space Station (ISS) and includeECOSTRESS, DESIS, and GEDI. ECOSTRESS is a 5 channel, thermal IR instrument with 70 m resolution and approximately1-5 day repeat cycle of day/night pairs. DESIS jointly developed by German Aerospace Center and Teledyne Brown Engineeringis a hyperspectral sensor system of 235 channels and 30 m resolution. DESIS data is only being acquired on demand. GEDI is ahigh-resolution laser ranger used for observing Earth’s forests and topography. NASA’s current ISS instrument configuration provides measurements of the critical environmental measures of environmentalstate functions important to vector & disease life cycles. Remote sensing data provide a spatial context and time series oflandscape scale process functions represented by land use mapping and measurements of ecological functions. Global publichealth is entering a new information age through the use of spatial models of disease vector/host ecologies driven by the use ofremotely sensed data to measure environmental and structural factors critical in determining disease vector habitats. In 2018, NASA initiated a new study for the Surface Biology and Geology (SBG) Designated Observable, identified in the 2018National Academies’ Decadal Survey entitled, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observationfrom Space.” (https://www.nap.edu/catalog/24938/thriving-on-our-changing-planet-a-decadal-strategy-for-earth) . The SBG isplanned to collect global remote sensing measurements using a hyperspectral spectrometer and multispectral thermal data. Thesedata sets will provide a significant enhancement in our ability to study disease vector’s life cycles and habitats globally. The 3sensors on the ISS provide precursor data to prepare the community for the application of future SBG data toward diseasestudies.

Disease Vectors↗

NASA NTRS2021

Ultra Long-Lived, Self-Surveying Autonomous Air Quality Sensing - Executive Summary

We set out to evolve ultra-low power air quality sensing technologies developed at JSC to add a highly accurate positioning sensor based on SBIR technology to give a self-surveying air quality monitoring platform with years-long lifetime on a small, disposable coin cell battery. Using Radio Frequency Identification (RFID) technology for data transport, the system can take advantage of RFID-based inventory management systems in place on lunar exploration assets to provide this capability with extremely small SWAP impacts. Years-long operational lifetimes enable flexible, autonomous environmental monitoring during lengthy intervals between and unprecedented situational awareness during crewed missions. Software integration of the localization system into the JSC RFID sensing platform was advanced, but the COVID-19 pandemic complicated and slowed maturation of the localization system SBIR product, and center closure indefinitely deferred a final hardware integration and system demonstration. In the meantime, progress was made to mature the air-quality sensing platform for flight, including hardware, software, antenna, and mechanical improvements. The underlying RFID sensing capability was also adapted to a drawer motion sensing system, which is currently (FY21) being taken toward an ISS flight demonstration as part of the RFID Enhanced Autonomous Logistics Management (REALM)-3 experiment.

Raymond Summers Wagner↗

NASA NTRSDate not supplied

Applications of Advanced Perception and Distributed Sensing Technology towards Autonomous Advanced Air Mobility Applications

Emerging concepts for Advanced Air Mobility (AAM) envisions responsive air transportation capabilities that can safely move people and cargo between places - including local, regional, intraregional, and urban - previously not served or underserved by aviation. Expanding traditional aviation services to these environments, particularly when autonomous operations are involved, face a number of challenges that will require advances beyond the state-of-the-art techniques for airborne sensing and perception, which goes beyond the limits of scalability and applicability of the current air transportation system infrastructure. Additionally, the emerging field of distributed sensing and ‘smart spaces’– where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting and can be exploited by the agents through real-time wireless communication – may provide realistic near-time solutions to limitations imposed by traditional aviation techniques. This paper outlines the needs, challenges, and opportunities for advanced perception and distributed sensing techniques to meet the emerging needs for advanced AAM operations in the national airspace. A general roadmap for research, development, and maturation of perception and distributed sensing (P&DS) technologies is proposed to guide future development through verification, validation, certification into airborne systems. Through this analysis of challenges and the proposed roadmap for technology maturation, we hope to accelerate transition of advanced research techniques from other disciplines into this domain.

Autonomy↗

DOE OSTI2025

Distributed Fiber Optic Sensing for in-well hydraulic fracture monitoring

This study presents the results from in-well hydraulic fracture monitoring within a horizontal well in an unconventional reservoir utilizing Distributed Fiber Optic Sensing (DFOS). An in-house-developed Brillouin-based Distributed Strain Sensing (DSS) interrogator was deployed to obtain strain measurements, complemented by a commercial Raman-based Distributed Temperature Sensing (DTS) interrogator for temperature measurements and a commercial Rayleigh-based Low-Frequency Distributed Acoustic Sensing (LF-DAS) interrogator for strain-rate measurements. Examined over a ten-day period, the spatio-temporal distribution of temperature-compensated strain obtained from DSS and DTS revealed distinct signatures of the multi-stage hydraulic fracturing process. These signatures were analyzed with respect to fracture width growth and closure, residual strain effects, and fracture conductivity near the wellbore. Fracture widths within the fracture zone were estimated for individual stages. The findings were assessed with LF-DAS measurements for further evaluation. This work integrates DFOS-measured strain, temperature, and strain-rate data for monitoring in-well hydraulic fracturing, with the goal of supporting future studies in interpreting DFOS measurements for improved understanding of hydraulic fracturing in unconventional reservoirs.

58 GEOSCIENCES↗

DOE OSTI2025

More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing

Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of an extra shot budget. Given that shots are a precious resource for sensing – as infinite measurements could lead to infinite precision – care must be taken to truly guarantee that any shot not being used for sensing is actually leading to some metrological improvement. In this work, we study whether investing shots in error-mitigation, inference techniques, or combinations thereof, can improve the sensitivity of a noisy quantum sensor on a (shot) budget. We present a detailed bias–variance error analysis for various sensing protocols. Our results show that the costs of zero-noise extrapolation techniques outweigh their benefits. We also find that pre-characterizing a quantum sensor via inference techniques leads to the best performance, under the assumption that the sensor is sufficiently stable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Thermochemical Properties of a Water-Soluble Zr(IV)-Flavonolate Complex for Fluoride Sensing

Detection of fluoride in water samples is critical for monitoring many chemical, environmental, and biological processes, however, these measurements often rely on costly instrumentation. Here, this situation has motivated the design of simple to use, fieldable fluoride sensors. Previously, a fluorometric sensor based on (EDTA)Zr(H 2 O) 2 (EDTA = ethylenediamine-N,N,N′,N′-tetraacetate) and 3-hydroxyflavone (FlvH) was reported for fluoride detection in ethanol-H 2 O mixtures. Herein, we report the synthesis and characterization of the previously proposed Zr(IV)-flavonolate complex as the dimethylammonium salt, [(EDTA)Zr(Flv)][NMe 2 H 2 ] (Flv = 3-hydroxyflavonolate), and detail its utility for aqueous fluoride sensing. This species exhibits an intense blue fluorescence upon excitation with near-UV light (λ ex = 390 nm; λ em = 460 nm) that is quenched in the presence of NaF (100 mM acetate buffer, pH = 5) with good sensitivity (LOD = 20.9 nM; [F – ] from 10 –8 to 10 –4 M) under neat aqueous conditions. The equilibrium constant (K eq = 1.26 ± 0.00184 × 10 8 M –2 ) and free energy (Δ$G^°_{\textrm{rxn}}$ = –11.0 kcal mol –1 ) were determined under the conditions used for fluoride sensing, indicating a highly favorable reaction with F – in water. Finally, we developed paper-based sensing strips for qualitative fluoride sensing by immobilizing the sensor in a deposited gel enabling good selectivity and reduced interference due to slower ion diffusion.

aqueous chemistry↗

DOE OSTI2025

Monitoring strain evolution in water-sand systems using distributed acoustic sensing for geohazard early warning

Rainfall-driven hazards such as landslides, debris flows, and earthen dam failures often arise when water changes the internal strain within sand. This study evaluates the ability of distributed acoustic sensing to monitor these strain changes in real time. We embed a fiber-optic cable in a sand-filled glass cylinder and run controlled dry- and wet-sand experiments to measure how strain develops as water infiltrates, saturates, and drains from the sand. The sensing system detects uneven water movement in dry sand and enables millimeter-scale estimates of infiltration rates, and in wet sand it tracks rising water levels, delayed strain peaks after saturation, and abrupt strain shifts during drainage. These results show that fiber-optic sensing captures subtle strain evolution throughout the full water-sand interaction cycle. The study demonstrates that fiber-optic sensing offers promising potential for real-time and cost-effective monitoring and early warning of rainfall-induced geohazards.

58 GEOSCIENCES↗

NASA NTRS2006

Fundamental Limits to Wavefront Sensing in the Submillimeter

With the advent of large-format submillimeter wavelength detector arrays, and a new 25 m diameter submillimeter telescope under consideration, the question of optimal wavefront sensing methods is timely. Indeed, not only should bolometric array detectors allow the use of a variety of wavefront sensing techniques already in use in the optical/infrared, but in some cases it should actually be easier to apply these techniques because of the more benign temporal properties of the atmosphere at long wavelengths. This paper thus addresses the fundamental limits to wavefront sensing at submillimeter wavelengths, in order to determine how well a telescope surface can be measured in the submillimeter band. First several potential measurement approaches are discussed and compared. Next the theoretical accuracy of a fringe phase measurement in the submillimeter is discussed. It is concluded that with Mars as the source, wavefront sensing at the micron level should be achievable at submillimeter wavelengths in quite reasonable integration times.

wavefront sensing↗

NASA NTRS2006

A Laboratory Experiment for Demonstrating Post-Coronagraph Wave Front Sensing and Control for Extreme Adaptive Optics

Direct detection of exo-planets from the ground will become a reality with the advent of a new class of extreme-adaptive optics instruments that will come on-line within the next few years. In particular, the Gemini Observatory will be developing the Gemini Planet Imager (GPI) that will be used to make direct observations of young exo-planets. One major technical challenge in reaching the requisite high contrast at small angles is the sensing and control of residual wave front errors after the starlight suppression system. This paper will discuss the nature of this problem, and our approach to the sensing and control task. We will describe a laboratory experiment whose purpose is to provide a means of validating our sensing techniques and control algorithms. The experimental demonstration of sensing and control will be described. Finally, we will comment on the applicability of this technique to other similar high-contrast instruments.

coronography↗

NASA NTRS2013

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters : Synthetic Sensor Radiance Formulation - Pt. 1

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Simulations↗

NASA NTRS2013

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters: Equivalent Sensor Radiance Formulation - Part 1

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Simulations↗

NASA NTRS2012

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Multi-scale↗

NASA NTRS2014

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

Fusion↗

NASA NTRS2015

Transitioning Earth Remote Sensing Data to Benefit Society: A Paradigm for a Center of Excellence

Over the past decade there has been a substantial increase in the number of Earth remote sensing satellites launched for research and operational usage and numerous others planned by the international community. These satellites have been used to varying degrees by their supporting agencies for weather and environmental monitoring, climate studies, disaster monitoring and response, and other humanitarian activities. While there are success stories on useful applications of remote sensing data, the broader use of these satellite assets by other organizations and entities has been limited for a number of reasons including lack of data services, data dissemination issues, and a general failure to engage the broader end user community with useful data access and knowledge of how to use the data and products. This paper describes some of these current limitations on the broader use of Earth remote sensing data by the international community and describes the concept of a general "Center of Excellence" to facilitate the development, transition, and utilization of these Earth remote sensing observations by the broader international community.

Transition to operations↗

NASA NTRS2016

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

CAR↗

NASA NTRS2018

Using Remote Sensing Mapping and Growth Response to Environmental Variability to Aide Aquatic Invasive Plant Management

Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plants communities in the California Delta.

Remote Sensing↗