Evaluation of bio‐optical inversion of spectral irradiance measured from an autonomous underwater vehicle

Abstract

[1] Autonomous underwater vehicles (AUVs) can map water conditions at high spatial (horizontal and vertical) and temporal resolution, including under cloudy conditions when satellite and airborne remote sensing are not feasible. As part of the RADYO program, we deployed a passive radiometer on an AUV in the Santa Barbara Channel and off the coast of Hawaii to apply existing bio-optical algorithms for characterizing the optical constituents of coastal seawater (i.e., dissolved organic material, algal biomass, and other particles). The spectral differences between attenuation coefficients were computed from ratios of downwelling irradiance measured at depth and used to provide estimates of the in-water optical constituents. There was generally good agreement between derived values of absorption and concurrent measurements of this inherent optical property in Santa Barbara Channel. Wave focusing, cloud cover, and low attenuation coefficients influenced results off the coast of Hawaii and are used to evaluate the larger-scale application of these methods in the near surface coastal oceans.

Hydrography-Driven Variability of Optically Active Constituents of Water in the South Brazilian Bight: Biogeochemical Implications

“…Satellite remote sensing and bio-optical, autonomous, sensorbased monitoring have become the state-of-the-art regarding oceans surveying in the past decades, by effectively increasing sampling resolution. Hence, an extensive effort has been devoted for developing and improving those sensors and platforms (IOCCG, 2008(IOCCG, , 2011Moline et al, 2012). The optical properties of the water, classified as inherent optical properties (IOPs, e.g., absorption and scattering coefficients) and apparent optical properties (AOPs, e.g., diffuse attenuation coefficients, reflectance) can provide valuable information on the oceans' biogeochemistry and therefore have been extensively sampled throughout the global oceans (IOCCG, 2006).…”

Detection and quantification of oil under sea ice: The view from below

“…The large visible contrast between ice and oil in images simplifies the classification process; when coupled with a sonar system, this is likely to greatly reduce any ambiguity in the interpretation of the sonar signal. However, as with all optical methods, camera-based oil detection is sensitive to variability in the optical properties of the overlaying water column, whether natural or spill-related (Moline et al, 2012). Additional laboratory tests are needed to quantify the effects of these and other ice conditions on the returned acoustic signal.…”

Introduction to special section on Recent Advances in the Study of Optical Variability in the Near‐Surface and Upper Ocean

“…An Odyssey autonomous underwater vehicle (AUV; hereafter called Odyssey ) [ Moline et al , 2012, Figure 1] was deployed with physical and optical instrumentation during the SBC experiment to obtain spatial variability data in the vicinity of the R/P FLIP and the R/V Kilo Moana. One of the advantages of AUV's is that they can collect data away from ship's influence (e.g., shading and reflections).…”

“…However, similar changes as those noted for chlorophyll a occur around 20–22 September (YD 264–266). In this case, the beam attenuation coefficient maximum lies very near the base of the MLD whereas the chlorophyll a maximum was deeper by about 10 m. Whether this feature was caused locally or resulted from advection will be explored by analyzing data collected by the Odyssey AUV and the REMUS AUV [ Moline et al , 2012] and by using general circulation models [e.g., Dong et al , 2012].…”

“…Primary R/P FLIP time series span the period of approximately YD 244 (1 September 2009) through YD 257 (14 September 2009). The REMUS AUV was also deployed from the R/V Kilo Moana on YD 243, 247, and 248 (31 August and 4 and 5 September 2009) in order to obtain horizontal spatial variability data as well as to obtain a unique view of the ocean environment [ Moline et al , 2012]. Satellite wind, sea surface temperature (Figure 15) and ocean color data (Figure 20) provided regional atmospheric and oceanographic context for the study.…”