NASA NTRS · 20210004633
UNKNOWN
Explore connections, maps & timelines
Reyes, George, Mahoney, Colin, Hamilton, Mike, Cheng, Li-Jen. 1994-04-05. Analysis of AOTF Hyperspectral Imagery. https://ntrs.nasa.gov/citations/20210004633
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
AIRS and MODIS are widely used for weather, climate, composition, carbon cycle, cross-calibration, and applications. The community asking for new capability in the 2020 timeframe, capabilities desired: (1) Hyperspectral UV to LWIR, High Spatial ?1km IFOV (2) Maximize Synergies of Solar Reflected and IR. Synergies with OCO-2. We expect more users and applications of next gen LEO IR Sounder than GEO. These include: weather, climate, GHG monitoring, aviation, disaster response. There is a new direction for imagers and sounders: (1) Separate Vis/NIR/SWIR from MWIR/LWIR instruments reduces technology risk and complexity. (2) Expect Costs to be lower than CrIS & VIIRS Some additional ideas to reduce costs include: (1) minimum set of requirements (2) mini-grating spectrometers. supports constellation for higher revisit (3) new technology to reduce instrument size (large format fpa's) (4) hosted payloads
Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.
We present an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a NASA Instrument Incubation Proposal funded research project aimed at developing a hyperspectral microwave instrument intended for enhanced remote sensing of atmospheric temperature and water vapor from space. This paper provides preliminary results on HyMPI’s spectral and noise characteristics and a preliminary demonstration of its enhanced water vapor sensitivity and vertical resolution, with a particular focus on the Earth’s Planetary Boundary Layer.