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.

Statistical analysis of temporal relationships between large earthquakes (M w ≥ 7) and volcanic eruptions suggests that seismic waves may trigger eruptions over great (>1000 km) distances from the epicenter, but a robust relationship between volcanic and teleseismic activity remains elusive. Here we investigate the relationship between dynamic stresses propagated by surface waves and a volcanic response, manifested by changes in sulfur dioxide (SO 2) emissions measured by the spaceborne Ozone Monitoring Instrument (OMI). Surface wave amplitudes for a catalog of 69 earthquakes in A.D. 2004-2010 are modeled at 12 persistently degassing volcanoes detected by the OMI. The volcanic response is assessed by examining daily OMI SO 2 measurements in 28 day windows centered on earthquakes meeting a variable peak dynamic stress threshold. A positive volcanic response is identified if the average post-earthquake SO 2 mass was at least 20% larger than the pre-earthquake SO 2 mass. We find two distinct volcanic responses, correlating strongly with eruption style. Open-vent, basaltic volcanoes exhibit a positive response to earthquake-generated dynamic stress (i.e., the earthquake triggers increased SO 2 discharge), and andesitic volcanoes exhibit a negative response. We suggest that the former is consistent with disruption or mobilization of bubbles, or magma sloshing, in low-viscosity magmas, whereas the latter observation may reflect more dominant controls on degassing in viscous magmas or a post-earthquake reduction in permeability. Overall this analysis suggests that the potential effects of large earthquakes should be taken into account when interpreting trends in volcanic gas emissions.

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Frequent Aureoumbra lagunensis blooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early-warning system for harmful algal blooms, we apply varimax-rotated principal component analysis (VPCA) to 12 images spanning~1.5 years. The method partitions visible-NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel-3A Ocean and Land Colour Instrument (OLCI) and a field-based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated with A. lagunensis in both spectral datasets indicate high correlations to Ochrophyta cell counts (R 2 ≥ 0.92, p < 0.001). Recombining components exhibiting a red-edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel-3A OLCI-based VPCA spectral identification and indicate A. lagunensis was highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem. Plain Language Summary Toxic or nuisance blooms of microscopic plankton are causing environmental, economic, and public health problems in Indian River Lagoon, Florida, and other coastal waters. Monitoring from boats can be expensive compared to remote sensing methods, but the remote sensing signal must be validated. Here we present results that document that the brown tide that develops in the Indian River Lagoon can be identified with very little error using different types of supporting data sets. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.