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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the −75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Cambodia↗
NASA NTRS2018

Cloud Impacts on Photochemistry: Building a Climatology of Photolysis Rates from the Atmospheric Tomography Mission

Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry– climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J -cloudy (standard, sometimes cloudy conditions) to J -clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.

actinic flux spectroradiometers↗
NASA NTRS2019

Surface Roughness-Induced Spectral Degradation of Multi-Spaceborne Solar Diffusers Due to Space Radiation Exposure

Solar diffusers (SDs) have often been used as the onboard calibrators for the radiometric calibration of reflective solar band imaging sensors. After being spaceborne, the reflectance of SDs is observed to degrade with spectral dependence due to exposure to solar UV and energetic particle radiation. Long-term spectral reflectance data of SDs onboard multiple LEO imaging sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) on SNPP, are analyzed. The reflectance of SDs on these three instruments degrades faster for the shorter wavelength (0.4-0.6 μm) bands than the longer wavelength bands. The Surface Roughness-induced Rayleigh Scattering (SRRS) model is applied to simulate the SD degradation on these instruments, and the growth of the surface roughness parameter of the SDs is derived. It is determined that the change of surface roughness scale length is ~tens of nanometers. To show the consistency of roughness growth rates among the SDs on Terra/Aqua MODIS and SNPP VIIRS instruments, the functional dependences of the growth rates are characterized according to the SD exposure time and the stage of surface roughness. It is also found that the flattening or reverse in the growth trend of the surface roughness for these three SDs occurred around the same interval between October 2013 and October 2015. The confirmation of the applicability of SRRS model with the long-term spectral reflectance data from three independent spaceborne SDs facilitates a better understanding of the origin and physical processes of the SD degradation.

Visible Infrared Imaging Radiometer Suite (VIIRS)↗
NASA NTRS2001

A Spectralon BRF data base for MISR calibration applications

The Multi-angle Imaging SpectroRadiometer (MISR) is an Earth-observing sensor which provides global retrievals of aerosols, clouds, and land surface parameters. Instrument specifications require an accurate absolute calibration, as well as accurate camera-to-camera, band-to-band, and pixel-to-pixel relative response determinations. In order to achieve these requirements, MISR makes use of an on-board calibrator (OBC), as well as vicarious calibration (VC) experiments.

Satellite↗
NASA NTRS1995

The MISR Calibration Program

The Multi-angle Imaging SpectroRadiometer (MISR) is currently under development for NASA's Earth Observing System (EOS). The instrument consists of nine pushbroom cameras, each with four spectral bands in the visible and near-infrared. Detailed are the MISR preflight characteristics, design of the On-Board Calibrator, and the radiance product processing. Detailed specifications and test plans are given.

multi-angle↗
NASA NTRS1995

State of The Art Radiometer Standards For NASA'S Earth Observing System

The Multi-Angle Imaging SpectroRadiometer (MISR), to be launched in 1998, is one of five instruments on NASA's first Earth Observing System (EOS) platform. The 3% absolute radiometric calibration requirement is considered challenging, particularly since it must be maintained through the five-year mission life. The Instrument requirements have led to the development of an On-Board Calibrator (OBC) consisting of diffuse panels and photodiode-based radiometric standards.

SpectroRadiometer↗
NASA NTRS2019

Response Versus Scan-Angle Assessment of MODIS Reflective Solar Bands in Collection 6.1 Calibration

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also served as benchmark measurements for on-orbit cross-calibration studies. As the two MODIS instruments have operated well beyond their design lifespan of six years, the measurements from the onboard calibrators alone become inadequate to characterize the sensor’s response at all scan angles, as evinced by long-term drifts observed at certain scan positions of the Aqua-MODIS 0.64- and 0.86-μm bands in Collection 6 (C6) data set. The latest MODIS Level 1B C6.1 data set incorporates earth-view response trending from invariant desert sites as supplemental inputs to characterize the scan-angle calibration dependencies for all RSB. This article presents a deep convective cloud (DCC)-based calibration approach for an independent evaluation of the MODIS RSB response versus scan-angle (RVS) performance in C6.1. The long-term calibration stability and RVS differences in C6.1 have been significantly improved for Aqua-MODIS RSB. The observed RVS differences of more than 2% in Aqua-MODIS C6 bands 1 and 2 have been reduced to within 1% in C6.1. Some RSBs of Terra-MODIS have suffered temporal drifts up to ~2% and calibration shifts up to 3%, particularly around 2016 when the Terra satellite entered into safe mode. The DCC approach has been found very effective in tracking the on-orbit RVS changes over time.

Clouds and the Earth’s Radiant Energy System (CERE↗
NASA NTRS2020

Estimating Wildfire-Generated Ozone Over North America Using Ozonesonde Profiles and a Differential Back Trajectory Technique

An objective method, employing HYSPLIT back-trajectories and Moderate Resolution Imaging Spectroradiometer (MODIS) fire observations, is developed to estimate ozone enhancement in air transported from regions of active forest fires at 18 ozone sounding sites located across North America. The Differential Back Trajectory (DBT) method compares mean differences between ozone concentrations associated with fire-affected and fire-unaffected parcels. It is applied to more than 1100 ozonesonde profiles collected from these sites during the summer months June to August, 2006, 2008, 2010 and 2011. Layers of high ozone associated with low humidity were first removed from the ozonesonde profiles to minimize the potential effects of stratospheric intrusions on the calculations. No significant influence on average ozone levels by North American fires was found for stations located at Arctic latitudes. The ozone enhancement for stations nearer large fires, such as Trinidad Head and Bratt’s Lake, was up to 324.8% of the TTOC (Total Tropospheric Ozone Column). Fire ozone accounted for up to 8.3% of TTOC at downwind sites such as Yarmouth, Sable Island, Narragansett, and Walsingham. The results are consistent with other studies that have reported an increase in ozone production with the age of the smoke plume.

Moderate Resolution Imaging Spectroradiometer (MOD↗
NASA NTRS2019

Success of Montreal Protocol Demonstrated by Comparing High-Quality UV Measurements with “World Avoided” Calculations from Two Chemistry-Climate Models

The Montreal protocol on Substances that Deplete the ozone Layer has been hailed as the most successful environmental treaty ever (https://www.unenvironment.org/news-and-stories/story/montreal-protocol-triumph-treaty). Yet, although our main concern about ozone depletion is the subsequent increase in harmful solar UV radiation at the earth’s surface, no studies to date have demonstrated its effectiveness in that regard. Here we use long-term UV Index (UVI) data derived from high-quality UV spectroradiometer measurements to demonstrate its success in curbing increases in UV radiation. Without this landmark agreement, UVi values would have increased at mid-latitude locations by approximately 20% between the early 1990s and today and would approximately quadruple at mid-latitudes by 2100. In contrast, an analysis of UVI data from multiple clean-air sites shows that maximum daily UVI values have remained essentially constant over the last ~20 years in all seasons, and may even have decreased slightly in the southern hemisphere, especially in Antarctica, where effects of ozone depletion were larger. Reconstructions of the UVi from total ozone data show evidence of increasing UVI levels in the 1980s, but unfortunately, there are no high-quality UV measurements available prior to the early 1990s to confirm these increases with direct observations.

UV Index (UVI) data derived from high-quality UV s↗
NASA NTRS2024

Retrievals of Aerosol Optical Depth Over the Western North Atlantic Ocean During ACTIVATE

Aerosol optical depth was retrieved from two airborne remote sensing instruments, the Research Scanning Polarimeter (RSP) and Second Generation High Spectral Resolution Lidar (HSRL-2), during the National Aeronautics and Space Administration (NASA) Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). The field campaign offers a unique opportunity to evaluate an extensive 3-year dataset under a wide range of meteorological conditions from two instruments on the same platform. However, a long-standing issue in atmospheric field studies is that there is a lack of reference datasets for properly validating field measurements and estimating their uncertainties. Here we address this issue by using the triple collocation method, in which a third collocated satellite dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) is introduced for comparison. HSRL-2 is found to provide a more accurate retrieval than RSP over the study region. The error standard deviation of HSRL-2 with respect to the ground truth is 0.027. Moreover, this approach enables us to develop a simple, yet efficient, quality control criterion for RSP data. The physical reasons for the differences in two retrievals are determined to be cloud contamination, aerosols near the surface, multiple aerosol layers, absorbing aerosols, non-spherical aerosols, and simplified retrieval assumptions. These results demonstrate the pathway for optimal aerosol retrievals by combining information from both lidars and polarimeters for future airborne and satellite missions.

Aerosol optical depth↗
DOE OSTI2024

Approximation of ice phenology of Maine lakes using Aqua MODIS surface temperature data

Studies of lake ice phenology have historically relied on limited in situ data. Relatively few observations exist for ice out and fewer still for ice in, both of which are necessary to determine the temporal extent of ice cover. Satellite data provide an opportunity to better document patterns of ice phenology across landscapes and relate them to the climatological drivers behind changing ice phenology. We developed a model, the Cumulative Sum Method (CSM), that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Earth-observing Aqua satellite to approximate ice in (the onset of ice cover) and ice out from training datasets of 13 and 58 Maine lakes, respectively, during the 2002/2003 through 2017/2018 ice seasons. Ice in was signaled by reaching a threshold of cumulative negative degrees following the first day of the season below 0°C. Ice out was signaled by reaching a threshold of cumulative positive degrees following the first day of the year above 0°C. The comparison of observed and remotely sensed ice-in dates showed relative agreement with a correlation coefficient of 0.71 and a mean absolute error (MAE) of 9.8 days. Ice-out approximations had a correlation coefficient of 0.67 and an MAE of 8.8 days. Lakes smaller in surface area and nearer the Atlantic coast had the greatest error in approximation. Application of the CSM to 20 additional lakes in Maine produced a comparable ice-out MAE of 8.9 days. Ice-out model performance was weaker for the warmest years; there was a larger MAE of 12.0 days when the model was applied to the years 2019–2023 for the original 58 lakes. The development of this model, which utilizes daily satellite data, demonstrates the promise of remote sensing for quantifying ice phenology over short, temporal scales, and wider geographic regions than can be observed in situ, and allows exploration of the influence of surface temperature patterns on the process and timing of ice in and ice out.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evaluating ecosystem water use efficiency under drought stress: a case study of the Helan Mountain region, northwest China

Context Water use efficiency (WUE) is a fundamental ecological indicator links carbon assimilation and water loss in terrestrial ecosystems. Understanding its responses to drought stress is essential for adaptive ecosystem management, particularly in climate-sensitive mountain landscapes. Objectives This study aimed to investigate drought-driven variations in WUE across major vegetation types in the Helan Mountain region of Northwest China. Specifically, we sought to identify dominant ecological drivers of WUE variability and to disentangle their relative importance and causal pathways. Methods We quantified WUE using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Drought Severity Index (DSI) data from 2001 to 2020. To examine WUE – drought relationships across contrasting vegetation types, we employed a spatially explicit analytical framework integrating Random Forest (RF) modeling, partial correlation analysis, and structural equation modeling (SEM). Results Regional WUE exhibited relatively stable interannual dynamics, yet pronounced spatial heterogeneity that was strongly modulated by drought conditions. Vegetation properties, particularly Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), emerged as the dominant determinants of WUE, with NDVI alone explaining over 20% of its spatial variance in forest and grassland during non-drought periods. SEM analyses revealed that climate forcing influenced WUE mainly through indirect pathways mediated by soil moisture availability and vegetation structural dynamics, rather than through direct climatic controls. Among all regulating factors, LAI acted as the central control node governing ecosystem carbon–water coupling. In contrast, short-term climatic stress, especially atmospheric demand and drought duration, exerted weak or negative direct effects on WUE. Ecosystem-specific responses were observed, with croplands mainly regulated by soil water availability, whereas forests and grasslands showed more sensitive to atmospheric drought stress. Together, these results reveal a hierarchical control framework where soil–vegetation interactions mediate climate impacts on WUE, driving strong spatial heterogeneity in drought responses across mountain landscapes. Conclusions Our findings highlight the pivotal role of indirect drought effects mediated by vegetation and soil processes in shaping ecosystem WUE. The identified soil–vegetation–climate regulatory hierarchy provides mechanistic insight into landscape–scale drought sensitivity and supports integrated modeling approaches for evaluating ecosystem resilience and sustainable management in arid mountain regions.

China↗
DOE OSTI2025

Analysis of contrasting aerosol indirect effects in liquid water clouds over the northern part of Arabian Sea

The extensive daily statistics of aerosol properties, cloud properties, and their mutual correlations provide crucial information for better assessing future climate change. Here, in this paper, 14 years (2010–2023) of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed over the northern part of Arabian Sea (Latitude: 21°–25° N and Longitude: 62°–68° E) to assess the characteristics of aerosols and clouds and their relationships under different meteorological conditions. When aerosol optical depth (AOD) is less than ~0.7, the observations exhibit a positive correlation between AOD and cloud droplet effective radius (CDR) but negative correlations between AOD and cloud droplet number concentration (CDNC), between AOD and cloud optical depth (COD), between AOD and cloud liquid water path (CLWP), and between AOD and cloud geometrical thickness (H). The corresponding aerosol-cloud correlations change signs when the AOD values are larger than 0.7. However, the single folded positive AOD-cloud fraction (CF) relationship is observed in both AOD regimes. Similar correlations are also observed between precipitable water vapor (PWV) and CDR, CDNC, COD, H, CF and CLWP, together with a positive correlation between PWV and AOD. Further isolation of the environmental effects from aerosol effects by stratifying AOD and cloud data into different LTS and PWV bins shows that the signature of the well-known Twomey effect is observed under high LTS-high PWV conditions, while an opposite effect (anti-Twomey) is observed under low PWV conditions, regardless of LTS values. Additionally, negative correlations between AOD and COD, AOD and CLWP, and AOD and H are observed under low LTS, regardless of PWV conditions, with a slight positive correlation when AOD >0.4 under high LTS and PWV conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency

Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr -1 ; NIRv: 0.0003 yr -1 ), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr -1 ; NIRv: 0.0008 yr -1 ) and weakened greening trends after 2000 (NDVI: 0.0004 yr -1 ; NIRv: 0.0001 yr -1 ). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Importance of viewing angle: Hotspot effect improves the ability of satellites to track terrestrial photosynthesis

The product of near-infrared reflectance of vegetation and photosynthetic active radiation (NIRvP) is a new tool for monitoring gross primary productivity (GPP) dynamics in terrestrial ecosystems, due to the discovered linear correlation between NIRvP and GPP. While remote sensing-based NIRvP is considerably influenced by sensor geometry, such geometry impacts on the NIRvP-GPP relationship remain underexplored. In this study, we calculate NIRvP using observations from the Deep Space Climate Observatory (DSCOVR) that provide unique hotspot observation geometry in which the sensor viewing angle coincides with the sun direction. We evaluated the linear correlation between NIRvP and GPP in both the common nadir direction and the special hotspot direction. The results indicate that NIRvP in the hotspot direction significantly outperforms that in the nadir direction for tracking GPP variations across different ecosystems from diurnal to daily scales. This conclusion is further supported by data from the MODerate resolution Imaging Spectroradiometer (MODIS) and simulations using the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. Finally, our research highlights the value of using the unconventional hotspot-based sun-tracking satellite observations for a more accurate characterization of GPP dynamics in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗