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At least 109 records · Page 6

LAVA Pressure Transducer Trade Study

The Regolith and Environment Science and Oxygen and Lunar Volatile Extraction (RESOLVE) payload will transport the (LAVA) subsystem to hydrogen-rich locations on the moon supporting NASA's in-situ resource utilization (ISRU) programs. There, the LAVA subsystem will analyze volatiles that evolve from heated regolith samples in order to quantify how much water is present. To do this, the system needs resilient pressure transducers (PTs) to calculate the moles in the gas samples. The PT trade study includes a comparison of newly-procured models to a baseline unit with prior flight history in order to determine the PT model with the best survivability in flight-forward conditions.

Trade Study↗

A Programmable SDN+NFV Architecture for UAV Telemetry Monitoring

With the explosive growth in UAV numbers forecast worldwide, a core concern is how to manage the ad-hoc network configuration required for mobility management. As UAVs migrate among ground control stations, associated network services, routing and operational control must also rapidly migrate to ensure a seamless transition. In this paper, we present a novel, lightweight and modular architecture which supports high mobility, resilience and flexibility through the application of SDN and NFV principles on top of the UAV infrastructure. By combining SDN programmability and Network Function Virtualization we can achieve resilient infrastructure migration of network services, such as network monitoring and anomaly detection, coupled with migrating UAVs to enable high mobility management. Our container-based monitoring and anomaly detection Network Functions (NFs) can be tuned to specific UAV models providing operators better insight during live, high-mobility deployments. We evaluate our architecture against telemetry from over 80flights from a scientific research UAV infrastructure.

Resilience↗

The Open-Ocean Sensible Heat Flux and Its Significance for Arctic Boundary Layer Mixing During Early Fall

The increasing ice-free area during late summer has transformed the Arctic to a climate system with more dynamic boundary layer (BL) clouds and seasonal sea ice growth. The open-ocean sensible heat flux, a crucial mechanism of excessive ocean heat loss to the atmosphere during the fall freeze season, is speculated to play an important role in the recently observed cloud cover increase and BL instability. However, lack of observations and understanding of the resilience of the proposed mechanisms, especially in relation to meteorological and interannual variability, has left a poorly constrained BL parameterization scheme in Arctic climate models. In this study, we use multiyear Japanese cruise-ship observations from RV Mirai over the open Arctic Ocean to characterize the surface sensible heat flux (SSHF) during early fall and investigate its contribution to BL turbulence. It is found that mixing by SSHF is favored during episodes of high surface wind speed and is also influenced by the prevailing cloud regime. The deepest BLs and maximum ocean-atmosphere temperature difference are observed during cold air advection (associated with the stratocumulus regime), yet, contrary to previous speculation, the efficiency of sensible heat exchange is low. On the other hand, the SSHF contributes significantly to BL mixing during the uplift (low pressure) followed by the highly stable (stratus) regime. Overall, it can explain 10 of the open ocean BL height variability, whereas cloud-driven (moisture and radiative) mechanisms appear to be the other dominant source of convective turbulence. Nevertheless, there is strong interannual variability in the relationship between the SSHF and the BL height which can be intensified by the changing occurrence of Arctic climate patterns, such as positive surface wind speed anomalies and more frequent conditions of uplift. This study highlights the need for comprehensive BL observations like the RV Mirai for better understanding and predicting the dynamic nature of the Arctic climate.

boundary layer↗

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗

Handling Emergency Management in [an] Object Oriented Modeling Environment

It has been understood that protection of a nation from extreme disasters is a challenging task. Impacts of extreme disasters on a nation's critical infrastructures, economy and society could be devastating. A protection plan itself would not be sufficient when a disaster strikes. Hence, there is a need for a holistic approach to establish more resilient infrastructures to withstand extreme disasters. A resilient infrastructure can be defined as a system or facility that is able to withstand damage, but if affected, can be readily and cost-effectively restored. The key issue to establish resilient infrastructures is to incorporate existing protection plans with comprehensive preparedness actions to respond, recover and restore as quickly as possible, and to minimize extreme disaster impacts. Although national organizations will respond to a disaster, extreme disasters need to be handled mostly by local emergency management departments. Since emergency management departments have to deal with complex systems, they have to have a manageable plan and efficient organizational structures to coordinate all these systems. A strong organizational structure is the key in responding fast before and during disasters, and recovering quickly after disasters. In this study, the entire emergency management is viewed as an enterprise and modelled through enterprise management approach. Managing an enterprise or a large complex system is a very challenging task. It is critical for an enterprise to respond to challenges in a timely manner with quick decision making. This study addresses the problem of handling emergency management at regional level in an object oriented modelling environment developed by use of TopEase software. Emergency Operation Plan of the City of Hampton, Virginia, has been incorporated into TopEase for analysis. The methodology used in this study has been supported by a case study on critical infrastructure resiliency in Hampton Roads.

Tokgoz, Berna Eren↗

Soil Carbon Losses Reduce Soil Moisture in Global Climate Model Simulations

Most agricultural soils have experienced substantial soil organic carbon losses in time. These losses motivate recent calls to restore organic carbon in agricultural lands to improve biogeochemical cycling and for climate change mitigation. Declines in organic carbon also reduce soil infiltration and water holding capacity, which may have important effects on regional hydrology and climate. To explore the regional hydroclimate impacts of soil organic carbon changes, we conduct new global climate model experiments with NASA Goddard Institute for Space Studies ModelE that include spatially explicit soil organic carbon concentrations associated with different human land management scenarios. Compared to a “no land use” case, a year 2010 soil degradation scenario, in which organic carbon content (OCC; weight %) is reduced by a factor of ∼0.12 on average across agricultural soils, resulted in soil moisture losses between 0.5 and 1 temporal standard deviations over eastern Asia, northern Europe, and the eastern United States. In a more extreme idealized scenario where OCC is reduced uniformly by 0.66 across agricultural soils, soil moisture losses exceed one standard deviation in both hemispheres. Within the model, these soil moisture declines occur primarily due to reductions in porosity (and to a lesser extent infiltration) that overall soil water holding capacity. These results demonstrate that changes in soil organic carbon can have meaningful, large-scale effects on regional hydroclimate and should be considered in climate model evaluations and developments. Further, this also suggests that soil restoration efforts targeting the carbon cycle are likely to have additional benefits for improving drought resilience.

Atmosphere-land interaction↗

Development of an Adaptive Droop Control Method for Interconnected Lunar DC Microgrids Using Power Hardware-in-the-Loop

NASA’s Artemis Program outlines the need for a habitat capable of sustaining human life as well as mining and producing raw materials on the lunar surface. This mission is viewed as a means towards deeper space exploration, with plans for reaching Mars and beyond. Human presence on the moon is not possible without the ability to generate and distribute energy, namely electricity, through a network of energy sources, loads, and power converters called a microgrid. Multiple microgrids can be deployed on the moon based on location and need. Separate microgrids will require interconnection to increase resiliency and reliability given the mission’s high criticality. A method for adaptive control over power converters connecting two dc microgrids is proposed. A simulation is modeled after the lunar power system with two approaches to power converter droop control, allowing for a more flexible and adaptive microgrid architecture. Further experiments are conducted using the control methods in a power hardware-in-the-loop test environment to study the performance of hardware converter control used in this application.

dc microgrid↗

A Computational Framework for Making Early Design Decisions in Deep Space Habitats

The dynamics of systems of systems often involve complex interactions among the individual systems, making the implications of design choices challenging to predict. Design features in such systems may trigger unexpected behaviors or result in large variations in safety, performance or resilience. To provide a means of simulating such systems for aiding in these decisions, we have developed a prototype tool, the control-oriented dynamic computational modeling tool (CDCM). The CDCM provides rapid simulation capabilities to perform trade studies in systems of systems. The general class of systems of systems that we aim to examine involve multiple hazards, damage, cascading consequences, repair and recovery. We especially focus on systems-of-systems that incorporate a health management system (HMS) that can monitor the state of the habitat and make decisions about actions to take. In this paper we describe the features of the CDCM, the architecture we devised for simulation of systems-of-systems, the unique functionalities of this tool, and we provide a demonstration of the capabilities by performing two illustrative examples. We articulate the use of this tool for making early design decisions and demonstrate its use for trade studies that consider a model of a deep space habitat. We also share some experiences and lessons that may be useful for others seeking to address similar problems.

Amir Behjat↗

Creating Formal Characterizations of Routine Contingency Management in Commercial Aviation

The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.

Safety Critical Systems↗

San Diego Urban Development: Utilizing NASA Earth Observations to Identify Drivers of Extreme Urban Heat and Generate a High-Resolution Vulnerability Index for Urban Planning and Climate Resiliency in San Diego, California

Exposure to heat exacerbated by an increase in urbanization as well as increasing global temperatures has become a growing concern for cities and their residents. Excess heat can cause increased heat-related morbidity, mortality, and energy costs. Vulnerability to heat-related illnesses is oftentimes correlated to demographics, socioeconomic status, and pre-existing health conditions. The City of San Diego, California boasts 1.4 million residents and, like many other major cities, has experienced increases in heat-related hospitalizations and mortality. The burden of urban heat is also not equal amongst communities; areas with lower income and communities of color bear a disproportionate burden. In partnership with the City of San Diego, and American Geophysical Union’s (AGU) Thriving Earth Exchange, the DEVELOP team used Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) imagery to identify areas of highest heat based on land surface temperature from 2015-2020. Our analyses showed that health demographics such as obesity and cardiovascular health were the strongest indicators for heat vulnerability. In addition, various inputs (land use/land cover, tree canopy, and building intensity derived from the City of San Diego data along with albedo from Landsat 8) were used in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model to investigate changes in cooling rates in current and future scenarios for the city. The model results showed that cooling is expected to occur due to a 5% increase in tree canopy. The City of San Diego can use these results to inform the development of the Climate Resilient San Diego plan and prioritize at-risk communities for cooling interventions.

John Dialesandro↗

The NASA DEVELOP Model of Community Science & Engagement: Localizing Earth Science Information

The NASA DEVELOP Program conducts 10 week feasibility studies that apply Earth observation data to address community priorities and support informed decision making. Part of NASA’s Earth Action Capacity Building Program, DEVELOP builds skills in both participants ( recent graduates, and early/transitioning career professionals), who work on small interdisciplinary project teams, and partner organizations (state and local governments, federal agencies, non profit and for profit organizations, universities, and international organizations) that work closely with DEVELOP to design the project. Projects address a wide variety of environmental issues, such as disaster risk and resilience, air pollution, and the impact of urban development, with a growing number of projects exploring how satellite data can help inform decision making around environmental injustices. A subset of DEVELOP partner organizations are local municipalities or community led non profits, with the co production model serving as an effective engagement tool and an introduction for communities to engage in Earth science research and become familiar with satellite remote sensing. This presentation will highlight the DEVELOP co production model, community project use cases, and lessons learned in partnering with local communities.

Michael Pazmino↗

Feeding Ten Billion People Is Possible Within Four Terrestrial Planetary Boundaries

Global agriculture puts heavy pressure on planetary boundaries, posing the challenge to achieve future food security without compromising Earth system resilience. On the basis of process-detailed, spatially explicit representation of four interlinked planetary boundaries (biosphere integrity, land-system change, freshwater use, nitrogen flows) and agricultural systems in an internally consistent model framework, we here show that almost half of current global food production depends on planetary boundary transgressions. Hotspot regions, mainly in Asia, even face simultaneous transgression of multiple underlying local boundaries. If these boundaries were strictly respected, the present food system could provide a balanced diet (2,355 kcal per capita per day) for 3.4 billion people only. However, as we also demonstrate, transformation towards more sustainable production and consumption patterns could support 10.2 billion people within the planetary boundaries analysed. Key prerequisites are spatially redistributed cropland, improved water–nutrient management, food waste reduction and dietary changes. Adoption of the Sustainable Development Goals by all nations in 2015 is the first ever commitment to a world development path that safeguards the stability of the Earth system as a prerequisite for meeting universal human standards1. The longstanding challenge of achieving food security through sustainable agriculture is particularly acute in this context as world agriculture is a leading cause for the current transgressions of multiple planetary boundaries (PBs) globally and regionally2–5. The PB framework is a comprehensive scientific attempt to synoptically define our planet’s biogeophysical limits to anthropogenic interference. It suggests bounds to nine interacting processes that together delineate a Holocene-like Earth system state. The Holocene is chosen as the reference state as it is the only period known to provide a safe operating space for a world population of several billion people, and according to a precautionary principle, the PBs are set in sufficient distance from processes that may critically undermine Earth system resilience and global sustainability. A challenging question, thus, is whether human development goals such as food security can be met while maintaining multiple PBs along with their subglobal manifestations. Further PB transgressions could jeopardize the chances of providing sufficient food for a world population projected to be wealthier and reach >9 billion by 2050. This conundrum portrays a tradeoff between Earth’s biophysical carrying capacity and humankind’s rising food demand, calling in response for radical rethinking of food production and consumption patterns6–9. Yield gap closures, avoidance of excessive input use, shifts towards less resource-demanding diets, food waste reductions and efficient international trade are crucial options for sustainably increasing the food supply10–15. For example, enhancing water-use efficiency on irrigated and rain-fed farms can triple or quadruple crop yields in low-performing systems, suggesting possible global gains of >20% (ref. 16). Even higher gains appear feasible through globally optimized configurations of the land-use pattern17, and cutting food losses by half could generate food for another billion people18. Thus, collective large-scale implementation of such options could sustain food for a further growing world population19. Yet achieving this within a safe operating space as defined by PBs requires not only a halt to but actually a reversal of existing PB transgressions. Previous studies suggest that such a reconciliation might be possible, but these were based on aggregate representations of PBs (not accounting for the spatial patterns of limits, transgressions and interactions) or considered only one boundary in isolation17,20–23. Here, we systematically quantify to what extent current food production depends on local to global transgressions of the PBs for biosphere integrity, land-system change, freshwater use and nitrogen (N) flows, along with the potential of a range of solutions to avoid these transgressions and still increase food supply (Table 1). To this end, we configured an internally consistent process-based model of the terrestrial biosphere including agriculture (LPJmL) with multiple spatially distributed PBs and their interactions. LPJmL is among the longest-established and best-evaluated biosphere models, showing robust performance regarding simulation of, for example, carbon, water and crop yield dynamics (Supplementary Figs. 1 and 2 and Supplementary Table 1; see ref. 24 for a comprehensive benchmarking and Supplementary Methods for more detail on model evaluations). In principle following established definitions4, we refine the computation of some PBs with respect to their regional patterns and interactions (Methods), providing globally gridded precautionary limits to human interference with the Earth system at a level of great detail. In particular, we account for the evidence that many PBs need to be represented spatially explicitly4 to cover their

Gerten, Dieter↗

Assessing Woody Vegetation Trends in Sahelian Drylands Using MODIS Based Seasonal Metrics

Woody plants play a major role for the resilience of drylands and in peoples' livelihoods. However, due to their scattered distribution, quantifying and monitoring woody cover over space and time is challenging. We develop a phenology driven model and train/validate MODIS (MCD43A4, 500m) derived metrics with 178 ground observations from Niger, Senegal and Mali to estimate woody cover trends from 2000 to 2014 over the entire Sahel. The annual woody cover estimation at 500 m scale is fairly accurate with an RMSE of 4.3 (woody cover %) and r(exp 2) = 0.74. Over the 15 year period we observed an average increase of 1.7 (+/- 5.0) woody cover (%) with large spatial differences: No clear change can be observed in densely populated areas (0.2 +/- 4.2), whereas a positive change is seen in sparsely populated areas (2.1 +/- 5.2). Woody cover is generally stable in cropland areas (0.9 +/- 4.6), reflecting the protective management of parkland trees by the farmers. Positive changes are observed in savannas (2.5 +/- 5.4) and woodland areas (3.9 +/- 7.3). The major pattern of woody cover change reveals strong increases in the sparsely populated Sahel zones of eastern Senegal, western Mali and central Chad, but a decreasing trend is observed in the densely populated western parts of Senegal, northern Nigeria, Sudan and southwestern Niger. This decrease is often local and limited to woodlands, being an indication of ongoing expansion of cultivated areas and selective logging.We show that an overall positive trend is found in areas of low anthropogenic pressure demonstrating the potential of these ecosystems to provide services such as carbon storage, if not over-utilized. Taken together, our results provide an unprecedented synthesis of woody cover dynamics in theSahel, and point to land use and human population density as important drivers, however only partially and locally offsetting a general post-drought increase.

Brandt, Martin↗

Airports as Energy Nodes Activity Summary

Advanced aircraft concepts that use non-traditional aviation energy storage methods such as batteries or cryogenic hydrogen are in development and expected to enter regular service at airports worldwide within the next decade. The energy needs for these aircraft may quickly overwhelm the existing energy infrastructure at airports, particularly at smaller and more remote facilities. Without energy upgrades, these airports will not be able to host these advanced vehicles, but without the advanced vehicle traffic, these airports will not have the rationale or funding to build up their energy infrastructure. The Airports as Energy Nodes (ÆNodes) activity, a collaboration between the National Aeronautics and Space Administration (NASA) and the National Renewable Energy Laboratory (NREL), was executed to understand and model the energy needs that advanced aircraft concepts may levy on these smaller airports, determine cost-effective approaches to enhance the airport energy infrastructure, and demonstrate the enhanced resilience of these energy infrastructure upgrades to the airport and surrounding community via “digital twin” simulation at relevant energy and dynamic time scales. The ÆNodes team also investigated future reference aircraft designs and materials to enable cryogenic hydrogen storage for aircraft. The ÆNodes team conducted analysis at two U.S. airport partner sites — Winchester Regional Airport in Winchester, Virginia, and Tweed/New Haven Airport in New Haven, Connecticut. The goal of this partnership was to develop data and reference infrastructure designs that could accommodate advanced aircraft in the future at these airports while also enhancing the resiliency of the energy supply to the surrounding airport community, which could be used to capture funding to enable the infrastructure upgrades. Over the course of the study, a method was developed to estimate air traffic requiring advanced energy services over the course of a year using a mix of historical data and companion studies on advanced aircraft transportation networks. The study has concluded at NASA but continues at NREL, who will develop a final report discussing the energy infrastructure upgrades and digital twin results. Preliminary results indicate that unrestricted adoption of advanced battery-electric aircraft may double traffic at these airports and increase peak daily power usage by an order of magnitude, while increase electricity energy needs by a factor of two to four. The infrastructure upgrades necessary to accommodate these increased energy needs could be used to provide enhanced energy services to the airport community to offset the cost and increase the utility of the upgrades, which will be described in the NREL final report.

Airports↗

New York City Panel on Climate Change 2019 Report Chapter 3: Sea Level Rise

The New York City Panel on Climate Change (NPCC, 2015) sea level rise projections provide the current scientific basis for New York City scientific decision making and planning, as reflected in, for example, the City's Climate Resiliency Design Guidelines. However, since the IPCC (2013) and NPCC (2015) reports, recent observations show mounting glacier and ice sheet losses leading to rising sea levels. Furthermore, new developments in modeling interactions between oceans, atmosphere, and ice sheets suggest the possibility of a significantly higher global mean sea level rise (GMSLR) by 2100 than previously anticipated, particularly under elevated greenhouse gas emission scenarios.

Gornitz, Vivien M.↗

Space Crop Considerations for Human Exploration

NASA has been actively working to both determine how many crops will be needed for early exploration missions as well as updating the “Crop Readiness Level” (CRL) for a library of crops that can be selected for supporting a long-term mission. The Crop Readiness Level (CRL) is modelled after NASA’s Technology Readiness Level (TRL) approach for developing and advancing new technologies for space, first suggested by Barry Finger and published by Wheeler and Strayer [2]. The CRL model has nine levels from “crop identification” to “consumed in space.” The number and variety of crops needed is impacted by both primary factors (nutrition, menu fatigue, behavioral health system resiliency) as well as secondary factors such as ECLSS considerations, crop robustness, and hardware considerations.

Gioia D Massa↗

Modeling Global Indices for Estimating Non-Photosynthetic Vegetation Cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Index optimization↗