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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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NASA NTRS2024

Prelaunch Spectral Characterization of the Operational Land Imager-2

The Landsat-9 satellite, launched in September 2021, carries the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. The focal plane modules used on OLI-2 were manufactured as spares for OLI and much of the spectral characterization of the components was performed for OLI. However, while the spectral response of the fully assembled OLI was characterized by a double monochromator system, the OLI-2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR is a system of tunable lasers that cover 350–2500 nm which are fiber-coupled to a 30 in integrating sphere permanently monitored by NIST-traceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The spectral performance of the OLI-2 was, in general, much better than requirements. The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the spectral characterization from the component-level to the instrument-level of the Landsat-9 OLI-2.

Landsat-9↗
NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗
NASA NTRS2016

MABEL Photon-Counting Laser Altimetry Data in Alaska for ICESat-2 Simulations and Development

Multiple Altimeter Beam Experimental Lidar (MABEL) maps Alaskan crevasses in detail, using 50 of the expected along-track Advanced Topographic Laser Altimeter System (ATLAS) signal-photon densities over summer ice sheets. Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) along-track data density, and spatial data density due to the multiple-beam strategy, will provide a new dataset to mid-latitude alpine glacier researchers.

Landsat-8↗
NASA NTRS2016

Harmonizing Landsat and Sentinel-2 Reflectances for Better Land Monitoring

When combined, Landsat and ESA Sentinel-2 observations can provide 2-4 day coverage for the global land area. A collaboration among NASA GSFC (Goddard Space Flight Center), University of Maryland, and NASA Ames has developed a processing chain to create seamless, "harmonized" reflectance products using standardized atmospheric correction, BRDF (Bidirectional Reflectance Distribution Function) adjustment, spectral bandpass adjustment, and gridding algorithms. These products point the way to a "30-m MODIS (Moderate Resolution Imaging Spectroradiometer)" capability for agricultural and ecosystem monitoring by leveraging international sensors.

GEO-GLAM↗