Search NASA⌕ Search

SEARCH · Search NASA

Results for “Quantitation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Quantitative Assessment of the HLS Surface Reflectance Consistency

The Harmonized Landsat and Sentinel-2 (HLS) project produces compatible surface reflectance (SR) from observations acquired by Landsat-8/9 OLI and Sentinel-2A/2B MSI. The HLS harmonization procedures include atmosphere correction, cloud masking, view angle normalization, and bandpass adjustment. The objective of this study is to quantitatively assess the reflectance consistency between Landsat and Sentinel-2 within the Version 2 HLS data. We collected 545 pairs of same-day Landsat/Sentinel-2 images across the globe to represent a wide range of vegetation types and climate regimes. The mean absolute difference (MAD) in reflectance between Landat and Sentinel-2 was calculated as a consistency indicator for each harmonization step. The MAD generally increased after the atmosphere correction, and then greatly decreased after the BRDF and bandpass adjustments, to smaller than the top-of-atmosphere MAD values. The MAD ranged from 0.0048 to 0.0093 for the six common spectral bands (blue, green, red, NIR, SWIR1, and SWIR2) in the final products, only slightly greater than the difference between Landsat and Sentinel-2 calibrations.. An evaluation on a few commonly used vegetation indices also showed good agreement between Landsat and Sentinel-2 reflectance. All these evaluations demonstrate that the HLS project produces a consistent SR dataset from Landsat-OLI and Sentinel-MSI, which will be a valuable resource for a wide range of remote sensing applications.

Qiang Zhou↗
NASA NTRSDate not supplied

Progress on Quantitative Infrared Thermography at the NASA Langley Aerothermodynamic Laboratory

A quantitative infrared thermography technique is being developed for accurate and high-resolution measurements of surface temperature and heat flux in the hypersonic wind tunnels at NASA Langley Research Center. A hemisphere test campaign was carried out in the Langley Aerothermodynamic Laboratory 20-Inch Mach 6 Air Tunnel to assess the current performance of the technique and to identify remaining challenges. Measurements were obtained for three different model materials over a range of freestream Reynolds numbers. The raw infrared images were converted to temperature via a radiometric calibration of the camera sensor. Corrections were made for losses due to test article emissivity and window transmissivity. A preliminary uncertainty analysis yielded an estimated accuracy within 4 K at moderate viewing angles, similar to that of a standard thermocouple. Surface heat flux was obtained from the measured surface temperatures via a finite-difference solution of the one-dimensional heat equation that modeled thermal variation of the material properties. Comparisons of the measured heating to both computational and theoretical predictions demonstrated agreement within the expected uncertainty of the technique. The heat transfer measurement near the stagnation point was found to be highly sensitive to the test article emissivity, which establishes the need for improved characterization of the wind tunnel model materials for high temperature applications.

infrared thermography↗
NASA NTRS2013

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

MODIS↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗