Image2_Joint Characterization of the Cryospheric Spectral Feature Space.jpg

• Main Authors: Christopher Small, Daniel Sousa
• Format: Still Image
• Language: unknown
• Published: 2022
• Subjects: Physical Geography and Environmental Geoscience not elsewhere classified; Earth Sciences not elsewhere classified; Photogrammetry and Remote Sensing; AVIRIS; cryosphere; hyperspectral; manifold learning; EOF; Greenland; glacier; Ice Sheet; Sea ice
• Online Access: https://doi.org/10.3389/frsen.2021.793228.s003; https://figshare.com/articles/figure/Image2_Joint_Characterization_of_the_Cryospheric_Spectral_Feature_Space_jpg/19102511

Multispectral and hyperspectral feature spaces are useful for a variety of remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In many of these applications, models are used to project the higher dimensional continuum of reflectances (or radiances) onto lower dimensional mappings of the image target’s physical properties or categorical composition. In such cases, characterization of the feature space dimensionality, geometry and topology can provide fundamental guidance for effective model design. Utility of this characterization, however, hinges on identification of appropriate basis vectors for the feature space. The objective of this study is to compare and contrast two fundamentally different approaches for identifying feature space basis vectors via dimensionality reduction. In so doing, we illustrate how these two approaches can be combined to render a joint characterization that reveals spectral properties not apparent using either approach alone. We use a diverse collection of AVIRIS-NG reflectance spectra of ice and snow to illustrate the utility of the joint characterization to facilitate both modeling and classification of snow and ice reflectance. Joint characterization is also shown to assist with interpretation of physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide both physically interpretable dimensions representing the global structure of cryospheric reflectance properties as well as local manifold structures revealing clustering not resolved within the global continuum. The joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet and multiple clusters of ice reflectance properties common to both glacier and sea ice in different locations. The clustering revealed in the t-SNE feature spaces, and extended to the joint characterization, distinguishes subtle ...