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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Ulsig, Laura↗
NASA NTRS2019

The Refractive Index of Amorphous and Crystalline Water Ice in the UV-vis

Amorphous solid water (ASW) is found on icy dust grains in the interstellar medium (ISM), as well as on comets and other icy objects in the outer solar system. The optical properties of ASW are thus relevant for many astrophysical environments, but in the ultraviolet–visible (UV–vis), its refractive index is not well constrained. Here, we introduce a new method based on UV–vis broadband interferometry to measure the wavelength dependent refractive index n(λ) of amorphous water ice from 10 to 130 K, i.e., for different porosities, in the wavelength range of 210–757 nm. We also present n(λ) for crystalline water ice at 150 K, which allows us to compare our new method with literature data. Based on this, a method to calculate n(λ, ρ) as a function of wavelength and porosity is reported. This new approach carries much potential and is generally applicable to pure and mixed ice, both amorphous and crystalline. The astronomical and physical–chemical relevance and future potential of this work are discussed.

Kofman, Vincent↗
NASA NTRS2019

Developing an Air Quality Index for Microgravity Indoor Environments/Space Missions

Indoor pollution sources (on Earth) that release gases or particles into the air are the primary cause of air quality problems in indoor environments. The development of an adequate tool to understand pollution levels in a certain location is of high importance. This tool must be able to inform about the levels of pollution in a simple and understandable way but also, used to take a series of predetermined measures to protect the health of the exposed population. One of the most useful and up to date approaches for characterizing air pollution is the Air Quality Index (AQI). It is an easily-calculated powerful data-driven tool, that summarizes a complex phenomenon, such as air pollution, in straightforward indicators. The AQI system has been developed in different countries around the world, mainly for outdoor environments, based on the results of risk assessments, epidemiology studies, and current local air pollution regulations and standards.Air quality in microgravity indoor environments is of fundamental importance to crew health, with concerns encompassing both gaseous contaminants and particulate matter. Although the concentration of gases in the microgravity indoor environment is well studied, aerosols remain one of the major pollutants that affect air quality and has reported adverse health effects and hasn't been reported under these unique conditions. Earth-based AQIs can't be extrapolated to microgravity indoor environments due to different aerosol characteristics and altered lung deposition in low gravity.Concurrent with the aerosol-focused AQI effort, we assess and document how the process would apply for combining particles & gases into a composite index, with the ability to query each AQI independently. All this information can be combined in a spacecraft-specific AQI for future space missions and habitats. The objective of this work are to determine what areas of expertise will contribute, what research and data will be required, and explore the scope of effort needed to formulate a spacecraft AQI in addition to analyzing ISS aerosol sampling data and incorporate results from both aerosol Sampling experiments (the only relevant data available from space).

Sorek-Hamer, Meytar↗
NASA NTRSDate not supplied

Developing a Customizable Composite Drought Index for Pakistan

Pakistan has experienced intense agricultural droughts in recent years, with the southern provinces experiencing the most severe drought conditions. These prolonged dry conditions often result in failed crop production, impacting families and communities. To reduce the impacts a drought may have on a community by identifying dry conditions, drought indices are used. A customizable composite drought index (CDI) is developed to improve the spatial and temporal understanding of historical agricultural droughts that have affected Pakistan. An in depth analysis using 9 input variables will provide information regarding the most important variables for differing locations. This framework can enhance drought monitoring and forecasting systems. The performance of the CDI will be evaluated by using production data from different crops that have significant economic value in Pakistan. These crops include wheat, rice, maize, cotton and barley. The expected relationship between the CDI and crop production would be to see production decrease when the CDI decreases, an indication that crops failed due to drought. The CDI is made more precise by evaluating only agricultural areas using the months of the growing season for each specific crop. This will allow for a custom drought index to be developed based on crop type and geographic location that only uses the most important variables, while still capturing the full extent of historical drought.

Caily Schwartz↗
NASA NTRS2021

Spatio-Temporal Patterns of Optimal Landsat Data for Burn Severity Index Calculations: Implications for High Northern Latitudes Wildfire Research

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differ-enced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of sub-optimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as “optimal”. In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made.

Dong Chen↗
NASA NTRS2022

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test such hypothesis across the Contiguous United States during April 2015 – December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). Results show that the assimilation of leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multi-variate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Data assimilation↗
NASA NTRS2023

An Updated Geomagnetic Index-Based Model for Determining the Latitudinal Extent of Energetic Electron Precipitation

Energetic Electron Precipitation (EEP) from the Earth's plasma sheet and the radiation belts is an important feature of atmospheric dynamics through their destruction of ozone in the lower thermosphere and mesosphere. Therefore, understanding the magnitude of the atmospheric impact of the Sun-Earth interaction requires a comprehensive understanding of the intensity and location of EEP. This study improves the accuracy of a previous pressure-corrected Dst model that predicts the equatorward extent of >43, >114, and >292 keV EEP using the measurements from the Medium Energy Proton Electron Detector detector of six National Oceanic and Atmospheric Administration/Polar Orbiting Environmental Satellites and EUMETSAT/METOP satellites. The improvement is achieved through multiple linear regression of pressure-corrected Dst and pressure-corrected Ring Current (RC) indices. The RC index mitigates the baseline variation of the Dst index that created an inherent solar cycle bias in the previous model. The new model is then extended to the Southern Hemisphere (SH) after removing the South Atlantic Anomaly longitudes from the data. More than 80% of the residuals lie within ±1.8° Corrected Geomagnetic Latitude (CGMLat) in the Northern Hemisphere and within ±1.98° CGMLat in the SH.

E. M. Babu↗
NASA NTRSDate not supplied

Seasonal Prediction of an Extreme Temperature Index over the United States

Heat extremes have the potential to cause significant societal harm; advanced warming of the frequency of these events for a given season could help with planning and mitigation measures. While an individual temperature extreme event may not be predictable on seasonal time scales, the number of such events could be. In this study, we use an index representing the number of extreme days per summer to examine the ability of NASA’s Goddard Earth Observing System (GEOS) sub-seasonal to seasonal (S2S) prediction system to predict summertime temperature extremes over the continental United States. The index is defined as the number of days per summer (June-August) where the daily mean temperature is above the calendar-day 90th percentile and is computed for 1991-2020 using the GEOS-S2S seasonal retrospective forecasts as well as the Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). Temporal correlations are assessed over this period at the grid point scale, indicating significant correlation between the retrospective forecasts and MERRA-2, particularly in the western half of the United States. Spatial correlations between the retrospective forecast and MERRA-2 indices are computed over the United States for each year revealing year-to-year variability in the forecast skill. We investigate local and remote processes associated with increased prediction skill of summertime temperature extremes in a given year.

Natalie Thomas↗
NASA NTRS2015

The 2010 Russian Drought Impact on Satellite Measurements of Solar-Induced Chlorophyll Fluorescence: Insights from Modeling and Comparisons with the Normalized Differential Vegetation Index (NDVI)

We examine satellite-based measurements of chlorophyll solar-induced fluorescence (SIF) over the region impacted by the Russian drought and heat wave of 2010. Like the popular Normalized Difference Vegetation Index (NDVI) that has been used for decades to measure photosynthetic capacity, SIF measurements are sensitive to the fraction of absorbed photosynthetically-active radiation (fPAR). However, in addition, SIF is sensitive to the fluorescence yield that is related to the photosynthetic yield. Both SIF and NDVI from satellite data show drought-related declines early in the growing season in 2010 as compared to other years between 2007 and 2013 for areas dominated by crops and grasslands. This suggests an early manifestation of the dry conditions on fPAR. We also simulated SIF using a global land surface model driven by observation-based meteorological fields. The model provides a reasonable simulation of the drought and heat impacts on SIF in terms of the timing and spatial extents of anomalies, but there are some differences between modeled and observed SIF. The model may potentially be improved through data assimilation or parameter estimation using satellite observations of SIF (as well as NDVI). The model simulations also offer the opportunity to examine separately the different components of the SIF signal and relationships with Gross Primary Productivity (GPP).

vegetation↗
NASA NTRS2016

From Toes to Top-of-the-Atmosphere: Fowler Sneaker Index

Fowler Sneaker Index (FSI), developed by a NASA summer intern, is a new Ocean Color application that facilitates continuous monitoring of environmental conditions in the Chesapeake Bay. It builds on three decades of citizen science data collected by former Maryland State Senator Bernie Fowler, during his yearly "Wade-ins in the Patuxent River". FSI demonstrates how NASA's Earth-observing tools, in combination with a concerned and engaged public, can take science from the tips of our toes-to-top-of the atmosphere and back.

Optics Express↗