Intercomparison of Approaches to the Empirical Line Method for Vicarious Hyperspectral Reflectance Calibration

Abstract

Analysis of visible remote sensing data research requires removing atmospheric effects by conversion from radiance to at-surface reflectance. This conversion can be achieved through theoretical radiative transfer models, which yield good results when well-constrained by field observations, although these measurements are often lacking. Additionally, radiative transfer models often perform poorly in marine or lacustrine settings or when complex air masses with variable aerosols are present. The empirical line method (ELM) measures reference targets of known reflectance in the scene. ELM methods require minimal environmental observations and are conceptually simple. However, calibration coefficients are unique to the image containing the reflectance reference. Here we compare the conversion of hyperspectral radiance observations obtained with the NASA Glenn Research Center Hyperspectral Imager to at-surface reflectance factor using two reflectance reference targets. The first target employs spherical convex mirrors, deployed on the water surface to reflect ambient direct solar and hemispherical sky irradiance to the sensor. We calculate the mirror gain using near concurrent at-sensor reflectance, integrated mirror radiance, and in situ water reflectance. The second target is the Lambertian-like blacktop surface at Maumee Bay State Park, Oregon, OH, where reflectance was measured concurrently by a downward looking, spectroradiometer on the ground, the aerial hyperspectral imager and an upward looking spectroradiometer on the aircraft. These methods allows us to produce an independently calibrated at-surface water reflectance spectrum, when atmospheric conditions are consistent. We compare the mirror and blacktop-corrected spectra to the in situ water reflectance, and find good agreement between methods. The blacktop method can be applied to all scenes, while the mirror calibration method, based on direct observation of the light illuminating the scene validates the results. The two methods are complementary and a powerful evaluation of the quality of atmospheric correction over extended areas. We decompose the resulting spectra using varimax-rotated, principal component analysis, yielding information about the underlying color producing agents that contribute to the Ortiz et al.Vicarious Hyperspectral Reflectance Calibration Methods observed reflectance factor scene, identifying several spectrally and spatially distinct mixtures of algae, cyanobacteria, illite, haematite, and goethite. These results have implications for future hyperspectral remote sensing missions, such as PACE, HyspIRI, and GeoCAPE.

Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers

“…Moreover, given the state of sewage treatment with OSDS in the IRL, we predict that components associated with brown tide algae will be concentrated in the Banana River region, building upon the results of Barile (2018). The results from this study were part of a larger 2-year-long research project on the IRL designed to demonstrate the applicability of the method following prior studies conducted in the nutrient-polluted waters of Lake Erie (Ali et al, 2014Ortiz et al, 2017Ortiz et al, , 2019 and the meso-to oligotrophic waters of the U.S. Virgin Islands (Schlaerth, 2018).…”

“…Using an Analytical Spectral Devices™ (ASD) FieldSpec® Handheld 2 (HH2) hyperspectral spectroradiometer, we measured the absolute surface water reflectance at the 11 sample locations using a 10°field of view foreoptic attachment, yielding a pixel size of~50 cm given the elevation of the instrument above the water surface. Measurements were collected to avoid sun glint, but the absolute measurement geometry is less important in our application that traditional remote sensing methods because the KSU spectral decomposition method relies on derivative spectroscopy, rather than direct analysis of reflectance spectra which minimizes geometric interferences (Ortiz et al, 2017). The reflectance spectra from the hyperspectral ASD FieldSpec® HH2 were averaged to 10 nm resolution from 400-700 nm.…”

“…While phycocyanin is assumed to be indicative of cyanobacteria in inland waters, it is also present in Cryptophytes and Rhodophytes (Dring, 1982). The increasing number of bands on high-band count multispectral and hyperspectral sensors particularly when used in optically complex environments result in the inclusion of significant amounts of redundant information because all bands in the visible spectrum are highly correlated (Ortiz et al, 2013(Ortiz et al, , 2017(Ortiz et al, , 2019. New methods of analysis designed to work with redundant information and to capitalize on extraction of spectral shape information are needed.…”

Spatiotemporal Relationships of Phytoplankton Blooms, Drought, and Rainstorms in Freshwater Reservoirs

“…Cloud-free scenes were collected from 2008 to 2017 and corrected to minimize variations caused by weather, season, and instrument. Scenes were standardized using atmospheric corrections by the Dark Object Subtract method, radiometric corrections, and gain and offset calibrations [35,59]. Using the resulting surface reflectance products, two pigment detection algorithms were applied.…”

Optimizing Requirements for a Compact Spaceborne Adaptive Spectral Imaging System in Subpixel Target Detection Applications

“…10 shows four calibration panels attributed with the target materials, and an array of the vehicle types that were placed within the scene. The panels attributed with the black and white materials were used for atmospheric compensation using the empirical line method [40]. The 18-wheeler was selected as a vehicle that was large enough to generate full target pixels.…”