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42 records · Page 3

Satellite Remote Sensing: Ocean Color

Satellite ocean color instruments routinely provide global, synoptic views of the Earth's marine biosphere. These spaceborne radiometers measure light exiting the top of the atmosphere at discrete wavelengths in the ultraviolet to shortwave infrared region of the spectrum. This includes measurements of the color of the ocean - information used to infer the contents of the sunlit upper ocean, such as concentrations of phytoplankton, suspended sediments, and dissolved organic carbon. Continuous marine biological, ecological, and biogeochemical data records from satellite ocean color instruments now span over twenty years. This time-series not only supports Earth system and climate research, but also ecosystem and watershed management activities, including detection of nuisance and harmful algal blooms.

Biosphere↗

In situ spectroradiometric calibration of EREP imagery and estuarine and coastal oceanography of Block Island sound and adjacent New York coastal waters

The author has identified the following significant results. The first part of the study resulted in photographic procedures for making multispectral positive images which greatly enhance the color differences in land detail using an additive color viewer. An additive color analysis of the geologic features near Willcox, Arizona using enhanced black and white multispectral positives allowed compilation of a significant number of unmapped geologic units which do not appear on geologic maps of the area. The second part demonstrated the feasibility of utilizing Skylab remote sensor data to monitor and manage the coastal environment by relating physical, chemical, and biological ship sampled data to S190A, S190B, and S192 image characteristics. Photographic reprocessing techniques were developed which greatly enhanced subtle low brightness water detail. Using these photographic contrast-stretch techniques, two water masses having an extinction coefficient difference of only 0.07 measured simultaneously with the acquisition of S190A data were readily differentiated.

Yost, E. F.↗

Exploring the human-nature dynamics of Hunga Tonga Hunga Ha'apai, Earth's newest landmass

This paper examines the human-nature dynamics of volcanic eruptions through a multidisciplinary exploration of the recently-formed Hunga Tonga Hunga Ha‘apai (HTHH) landmass in the Kingdom of Tonga. HTHH was formed in early 2015 in the Ha‘apai island group in southwestern Tonga. This landmass has persisted longer than expected, providing a rare opportunity to examine pathways of erosion and biological colonization, and offering a glimpse into the cultural dynamics of a continuously changing Polynesian seascape. In 2018 and 2019, a collaborative partnership between the Kingdomof Tonga, Sea Education Association (SEA), and the United States National Aeronautics and Space Administration (NASA) ran expeditions to HTHH to calibrate satellite observations via field, ship, and drone-based measurements. In accessing HTHH via SEA's sailing school vessel (SSV) the Robert C. Seamans, a team of scientists and students combined fieldwork in anthropology, oceanography, and physical volcanology to study micro- and macro-scale dynamics of this classical surtseyan eruption site. In this pathfinding paper, we discuss the value of involving students in the process of real-time scientific discovery with on-the-fly adaptive hypothesis testing; this collaborative project provided mutual benefit to initiatives in science and education by offering robust data collection, while expanding environmental and cultural literacy. Additionally, we integrate collaborative research on HTHH with ethnographic research to understand how this ever changing “sea of islands” or intimately interconnected island spaces impact social-cultural relations within Oceania. We emphasize the importance of such multidisciplinary research in understanding the complexity of human-nature relations in a rapidly changing world.

Emily B. Hite↗

Assessment of Oceanographic Conditions during the North Atlantic EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Field Campaign

This manuscript presents an overview of NASA’s EXport Processes in the Ocean from Remote Sensing 2021 Field Campaign in the North Atlantic (EXPORTS NA) and provides quantitative and dynamical descriptions of the physical processes modulating water transformations during the study. A major programmatic goal was to conduct the sampling in a Lagrangian mode so that ocean ecological and biogeochemical changes can be observed independent from physical advective processes. To accomplish this goal, EXPORTS NA conducted a multi-ship, multi-asset field sampling program within a retentive, anticyclonic mode water eddy. Beneath depths of ∼100 m, Lagrangian sampling assets remained within the eddy core waters (ECWs) throughout the experiment, demonstrating that the ECWs within the mode water eddy were retentive. However, strong westerly winds from four storm events deepened the mixed layer (ML) of the surface core waters (SCWs) above the eddy’s mode water core by 25-40 m and exchanged some of the SCWs with surface waters outside of the eddy via Ekman transport. Estimates of flushing times ranged from 5-8 days, with surface exchange fractions ranging from 20-75% and were consistent with particle tracking advected by combined geostrophic and Ekman velocities. The relative contributions of horizontal and vertical advection on changes in ECW tracers depended on the horizontal and vertical gradients of that tracer. For example, horizontal advection played a large role in ECW salinity fluxes, while vertical entrainment played a larger role in the fluxes of nutrients into SCW ML. Each storm injected nutrients and low oxygen waters into the ML, after which the surface ocean ecosystem responded by reducing nutrient concentrations and increasing %O2 saturation levels. Overall, ECW values of chlorophyll and POC were the largest at the onset of the field program and decreased throughout the campaign. The analysis presented provides a physical oceanographic context for the many measurements made during the EXPORTS NA field campaign while illustrating the many challenges of conducting a production-flux experiment, even in a Lagrangian frame, and the inherent uncertainties of interpreting biological carbon pump observations that were collected in a Eulerian frame of reference.

oceanography↗

Atmospheric correction of AVIRIS data in ocean waters

Hyperspectral data offers unique capabilities for characterizing the ocean environment. The spectral characterization of the composition of ocean waters can be organized into biological and terrigenous components. Biological photosynthetic pigments in ocean waters have unique spectral ocean color signatures which can be associated with different biological species. Additionally, suspended sediment has different scattering coefficients which result in ocean color signatures. Measuring the spatial distributions of these components in the maritime environments provides important tools for understanding and monitoring the ocean environment. These tools have significant applications in pollution, carbon cycle, current and water mass detection, location of fronts and eddies, sewage discharge and fate etc. Ocean color was used from satellite for describing the spatial variability of chlorophyll, water clarity (K(sub 490)), suspended sediment concentration, currents etc. Additionally, with improved atmospheric correction methods, ocean color results produced global products of spectral water leaving radiance (L(sub W)). Ocean color results clearly indicated strong applications for characterizing the spatial and temporal variability of bio-optical oceanography. These studies were largely the results of advanced atmospheric correction techniques applied to multispectral imagery. The atmosphere contributes approximately 80 percent - 90 percent of the satellite received radiance in the blue-green portion of the spectrum. In deep ocean waters, maximum transmission of visible radiance is achieved at 490nm. Conversely, nearly all of the light is absorbed by the water at wavelengths greater than about 650nm and thus appears black. These spectral ocean properties are exploited by algorithms developed for the atmospheric correction used in satellite ocean color processing. The objective was to apply atmospheric correction techniques that were used for procesing satellite Coastal Zone Color Scanner (CZCS) data to AVIRIS data. Quantitative measures of L(sub W) from AVIRIS are compared with ship ground truth data and input into bio-optical models.

Terrie, Gregory↗

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

Ocean color remote sensing provides synoptic-scale, near-daily observations of marine inherent optical properties (IOPs). Whilst contemporary ocean color algorithms are known to perform well in deep oceanic waters, they have difficulty operating in optically clear, shallow marine environments where light reflected from the seafloor contributes to the water-leaving radiance. The effect of benthic reflectance in optically shallow waters is known to adversely affect algorithms developed for optically deep waters [1, 2]. Whilst adapted versions of optically deep ocean color algorithms have been applied to optically shallow regions with reasonable success [3], there is presently no approach that directly corrects for bottom reflectance using existing knowledge of bathymetry and benthic albedo.To address the issue of optically shallow waters, we have developed a semi-analytical ocean color inversion algorithm: the Shallow Water Inversion Model (SWIM). SWIM uses existing bathymetry and a derived benthic albedo map to correct for bottom reflectance using the semi-analytical model of Lee et al [4]. The algorithm was incorporated into the NASA Ocean Biology Processing Groups L2GEN program and tested in optically shallow waters of the Great Barrier Reef, Australia. In-lieu of readily available in situ matchup data, we present a comparison between SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Property Algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA).

QAA↗