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Results for “SPATIAL RESOLUTION”

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 253 records · Page 14
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NASA NTRS2019

Stars at High Spatial Resolution

We summarize some of the compelling new scientific opportunities for understanding stars and stellar systems that can be enabled by sub-milliarcsec (sub-mas) angular resolution, UV/Optical spectral imaging observations, which can reveal the details of the many dynamic processes (e.g., evolving magnetic fields, accretion, convection, shocks, pulsations, winds, and jets) that affect stellar formation, structure, and evolution. These observations can only be provided by long baseline interferometers or sparse aperture telescopes in space, since the aperture diameters required are in excess of 500 m – a regime in which monolithic or segmented designs are not and will not be feasible - and since they require observations at wavelengths (UV) not accessible from the ground. Such observational capabilities would enable tremendous gains in our understanding of the individual stars and stellar systems that are the building blocks of our Universe and which serve as the hosts for life throughout the Cosmos.

Kenneth G Carpenter↗
NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗
NASA NTRS2021

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed from CALIPSO: A Neural Network Approach

An innovative neural network retrieval algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS(AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurement of 2015 also compare with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission –Water "Shizuku"(GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind –surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from Hu et al. (2008), as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

wind aerosols CALIPSO↗