Search

Summary Data from MERIS onboard Envisat and MODIS onboard Terra and Aqua for 15–16 May 2010 were used to study powerful imprints of atmospheric gravity waves (AGWs) on the western part the Black Sea surface. Two cold fronts crossed the sea following the warm front and caused the AGWs which modulated the sea surface. Imprints of AGWs appeared as stripes of alternating brightness, they had crest length more than a hundred kilometers and wavelength of units of kilometers. Wave amplitude of AGWs imprints, evaluated by a 90%-depth of light penetration into the sea at 490 nm z90, the value inverse to the diffuse attenuation coefficient Kd_490, was units of decimeterxs. MODIS 250-m data of remote sensing reflectance, wind components and atmospheric pressure near the sea surface were obtained by processing the top of atmosphere data with the SeaDAS software package. Negative correlations of fluctuations of z90 with fluctuations of wind stress and atmospheric pressure were found on the transects of more than ten kilometers. The impact of wind stress on the origination of AGW imprints was found to be determinant, while the impact of atmospheric pressure was not more than units of percent.

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b bp(555) (m−1). We developed a simple empirical algorithm for deriving b bp(555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high‐quality bio‐optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R rs(λ) (sr−1), and the spectral particulate backscattering coefficients, b bp(λ). The LH metric used is defined as the magnitude of R rs(555) relative to a linear baseline drawn between R rs(490) and R rs(670). Using an independent validation dataset, we compared the skill of the LH‐based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta‐scores and z‐tests for overlapping confidence intervals were also explored as potential methods for assessing model skill., Key Points A reflectance line height metric was used as a predictor of the particulate backscattering coefficient at 555 nmThe degree of overlap metric was used to correct validation skill metrics for measurement uncertainties in modeled and observed dataBland–Altman and zeta‐score plots were explored as alternatives to one‐to‐one scatter plots routinely used for algorithm validation

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245&ndash, 715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales. Variability in satellite retrievals of CDOM absorption and spectral slope anomalies are tightly coupled to changes in atmospheric and oceanographic conditions associated with El Ni&ntilde, o Southern Oscillation (ENSO), strongly covary with the multivariate ENSO index in a large region of the tropical Pacific, and provide insights into the potential evolution and feedbacks related to sea surface dissolved carbon in a warming climate. Further validation of the DOC algorithm developed here is warranted to better characterize its limitations, particularly in mid-ocean gyres and the southern oceans.

Summary Satellite Moderate Resolution Imaging Spectroradiometer (MODIS) data of water leaving radiance of 859 nm with a spatial resolution of 250 m were used to investigate the impact of atmospheric gravity waves (AGWs), which manifested as stripes in clouds and on the sea surface. On the basis of an evaluation of the characteristics of AGWs and sea depth, it was shown that the surface stripes, or surface waves (SWs) were imprints of AGWs. Crests of SWs were like prolongations of cloud stripes on the combined radiance images testifying that SWs were shifted by minus a quarter of the period relative to AGWs.

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99 % of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Underwater Optical Wireless Communication (UOWC) is not a new idea, but it has recently attracted renewed interest since seawater presents a reduced absorption window for blue-green light. Due to its higher bandwidth, underwater optical wireless communications can support higher data rates at low latency levels compared to acoustic and RF counterparts. The paper is aimed at those who want to undertake studies on UOWC. It offers an overview on the current technologies and those potentially available soon. Particular attention has been given to offering a recent bibliography, especially on the use of single-photon receivers.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission., Key Points GEDI's simulator has been validated and found accurate enough for pre‐launch calibration activitiesThe uncertainties of the simulator have been quantified and ALS beam density identified as a sufficient measure of accuracyInteresting quirks of full‐waveform metrics have been highlighted and investigated

The Bio-Optical Forecasting (BioCast) system is a model that provides the US Navy with short-term forecasts of the ocean's optical environment. The forecasts are required to support a broad spectrum of naval operations, including mine countermeasure, anti-submarine, and expeditionary warfare operations. The BioCast system works by treating any geo-referenced surface ocean optical property provided via the US Navy's satellite data processing systems as a prognostic state variable. BioCast will then ingest operational ocean model velocity forecasts and calculate the three-dimensional optical property (pseudo-tracer) transport. BioCast verification statistics generated via forecast comparison to "next-day" satellite images show superior performance over 24-hour persistence of composite satellite data. Future operational modifications to BioCast, such as complex internal transformation submodels, must demonstrate superior performance to the established benchmark metrics and/or persistence over the operational forecast time horizon. Future BioCast applications will expand to include an interface with three-dimensional system performance simulation techniques that will predict how specific US Navy sensors will perform in the ocean's optical environment.

Interpretation of remote sensing reflectance from coastal waters at different wavelengths of light yields valuable information about water column constituents, which in turn, gives information on a variety of processes occurring in coastal waters, such as primary production, biogeochemical cycles, sediment transport, coastal erosion, and harmful algal blooms. The Hyperspectral Infrared Imager (HyspIRI) is well suited to produce global, seasonal maps and specialized observations of coastal ecosystems and to improve our understanding of how phytoplankton communities are spatially distributed and structured, and how they function in coastal and inland waters. This paper draws from previously published studies on high-resolution, hyperspectral remote sensing of coastal and inland waters and provides an overview of how the HyspIRI mission could enable the retrieval of new aquatic biophysical products or improve the retrieval accuracy of existing satellite-derived products (e.g., inherent optical properties, phytoplankton functional types, pigment composition, chlorophyll-a concentration, etc.). The intent of this paper is to introduce the development of the HyspIRI mission to the coastal and inland remote sensing community and to provide information regarding several potential data products that were not originally part of the HyspIRI mission objectives but could be applicable to research related to coastal and inland waters. Further work toward quantitatively determining the extent and quality of these products, given the instrument and mission characteristics, is recommended.

The bulk backscattering ratio ( b b p &tilde, ) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of modeled structural heterogeneity of phytoplankton cells on b b p &tilde, b b p &tilde, is modeled considering viruses, heterotrophic bacteria, phytoplankton, organic detritus, and minerals. Three case studies are defined according to the relative abundance of the components. Two case studies represent typical situations in open ocean, oligotrophic waters, and phytoplankton bloom. The third case study is typical of coastal waters with the presence of minerals. Phytoplankton cells are modeled by a two-layered spherical geometry representing a chloroplast surrounding the cytoplasm. The b b p &tilde, values are higher when structural heterogeneity is considered because the contribution of coated spheres to light backscattering is higher than homogeneous spheres. The impact of heterogeneity is, however, strongly conditioned by the hyperbolic slope &xi, of the particle size distribution. Even if the relative abundance of phytoplankton is small (&lt, 1%), b b p &tilde, increases by about 58% (for &xi, = 4 and for oligotrophic waters), when the heterogeneity is taken into account, in comparison with a particulate population composed only of homogeneous spheres. As expected, heterogeneity has a much smaller impact (about 12% for &xi, = 4 ) on b b p &tilde, in the presence of suspended minerals, whose increased light scattering overwhelms that of phytoplankton.

Starting in 2001, we proposed and extensively demonstrated (using a DMD: Digital Micromirror Device) an agile pixel Spatial Light Modulator (SLM)-based optical imager based on single pixel photo-detection (also called a single pixel camera) that is suited for operations with both coherent and incoherent light across broad spectral bands. This imager design operates with the agile pixels programmed in a limited SNR operations starring time-multiplexed mode where acquisition of image irradiance (i.e., intensity) data is done one agile pixel at a time across the SLM plane where the incident image radiation is present. Motivated by modern day advances in RF wireless, optical wired communications and electronic signal processing technologies and using our prior-art SLM-based optical imager design, described using a surprisingly simple approach is a new imager design called Coded Access Optical Sensor (CAOS) that has the ability to alleviate some of the key prior imager fundamental limitations. The agile pixel in the CAOS imager can operate in different time-frequency coding modes like Frequency Division Multiple Access (FDMA), Code-Division Multiple Access (CDMA), and Time Division Multiple Access (TDMA). Data from a first CAOS camera demonstration is described along with novel designs of CAOS-based optical instruments for various applications.

16 pages, 13 figures, 2 tables, Optical measurements including remote sensing provide a potential tool for the identification of dominant phytoplankton groups and for monitoring spatial and temporal changes in biodiversity in the upper ocean. We examine the application of an unsupervised hierarchical cluster analysis to phytoplankton pigment data and spectra of the absorption coefficient and remote-sensing reflectance with the aim of discriminating different phytoplankton assemblages in open ocean environments under non-bloom conditions. This technique is applied to an optical and phytoplankton pigment data set collected at several stations within the eastern Atlantic Ocean, where the surface total chlorophyll-a concentration (TChla) ranged from 0.11 to 0.62 mg m− 3. Stations were selected on the basis of significant differences in the ratios of the two most dominant accessory pigments relative to TChla, as derived from High Performance Liquid Chromatography (HPLC) analysis. The performance of cluster analysis applied to absorption and remote-sensing spectra is evaluated by comparisons with the cluster partitioning of the corresponding HPLC pigment data, in which the pigment-based clusters serve as a reference for identifying different phytoplankton assemblages. Two indices, cophenetic and Rand, are utilized in these comparisons to quantify the degree of similarity between pigment-based and optical-based clusters. The use of spectral derivative analysis for the optical data was also evaluated, and sensitivity tests were conducted to determine the influence of parameters used in these calculations (spectral range, smoothing filter size, and band separation). The results of our analyses indicate that the second derivative calculated from hyperspectral (1 nm resolution) data of the phytoplankton absorption coefficient, aph(λ), and remote-sensing reflectance, Rrs(λ), provide better discrimination of phytoplankton pigment assemblages than traditional multispectral band-ratios or ordinary (non-differentiated) hyperspectral data of absorption and remote-sensing reflectance. The most useful spectral region for this discrimination extends generally from wavelengths of about 425-435 nm to wavelengths within the 495–540 nm range, although in the case of phytoplankton absorption data a broader spectral region can also provide satisfactory results, This study was supported by the NASA Biodiversity and Ecological Forecasting Program (Grant NNX09AK17G), the NASA Ocean Biology and Biogeochemistry Program (Grant NNG04GO02G), and the Spanish National Research Council CSIC (projects ANERIS PIF08-015 and HIDRA 2006-301102). Part of this study was performed during a visit of E. T. at Scripps Institution of Oceanography supported also by CSIC (ProgramI3P). The Alfred Wegener Institute for Polar and Marine Research (Bremerhaven, Germany) kindly made it possible for us to participate in the cruise in the eastern Atlantic

Despite the importance of CDOM to upper ocean biogeochemical processes and optics, our current understanding of its spatial and temporal distributions and the factors controlling these distributions is very limited. This eventually prevents an understanding of its relationship to the pool of dissolved organic carbon in coastal and open oceans. This work aims to present a new approach for accurate modeling of absorption spectra of CDOM (a cdom ) and deriving information on its composition in global ocean waters. The modeling approach uses measurements (in situ) of the remote sensing reflectances at two wavelengths (denoted 443 555 R rs ) to estimate a cdom (350) and a cdom (412), applies them to determine two spectral slopes of an exponential curve fit (S) and a hyperbolic curve fit ( γ ), derives an appropriate parameter ( γ o ) for grading the CDOM compositional changes from a cdom (350) and γ , and finally employs a cdom (350), S, and γ o in a modified exponential model to describe a cdom (λ) as a function of wavelength. The robustness of this model was rigorously tested on three independent datasets, such as NOMAD in situ data, NOMAD SeaWiFS match-ups data and IOCCG simulated data (all of them contain a cdom (λ) and R rs (λ)), which represent a variety of waters within coastal and offshore regions around the world. Accuracy of the retrievals found with the new models was generally excellent, with MRE (mean relative error) and RMSE (root mean square error) of − 5.64–3.55% and 0.203–0.318 for the NOMAD in situ datasets, and − 5.63 to −0.98% and 0.136–0.241 for the NOMAD satellite datasets respectively (for λ 412 to λ 670 ). When used with SeaWiFS images collected over the regional and global waters, the new model showed the highest surface abundances of CDOM within the subpolar gyres and continental shelves dominated by terrestrial inputs (and perhaps local production) of colored dissolved materials, and the lowest surface abundances of CDOM in the central subtropical gyres and the open oceans presumably regulated by photobleaching phenomenon, bacterial activity and local processes. Significant interseasonal and interannual seasonal changes in the terrestrially-derived CDOM distributions were noticed from these new products that closely corresponded with the global mean runoff/river discharge induced by climate change/warming scenarios.

The purpose of this manual is to provide guidance to the U.S. IOOS and the optics community at large for the real-time QC of optics measurements using an agreed-upon, documented, and implemented standard process. This manual is also a deliverable to the U.S. IOOS Regional Associations and the ocean observing community and represents a contribution to a collection of core variable QC documents. Published To gauge the success of the QARTOD project, it helps to be aware of groups working to utilize these QC tests. We request that manual users notify us of their efforts or intentions to implement QARTOD processes by sending a brief email to data.ioos@noaa.gov or posting a notice at http://www.linkedin.com/groups?gid=2521409. Refereed Current

The optical backscattering from particles in the ocean is an important quantity that has been measured by remote sensing techniques and in situ instruments. In this paper, we compare estimates of this quantity from airborne lidar with those from an in situ instrument on an underwater glider. Both of these technologies allow much denser sampling of backscatter profiles than traditional ship surveys. We found a moderate correlation (R = 0.28, p < 10−5), with differences that are partially explained by spatial and temporal sampling mismatches, variability in particle composition, and lidar retrieval errors. The data suggest that there are two different regimes with different scattering properties. For backscattering coefficients below about 0.001 m−1, the lidar values were generally greater than the glider values. For larger values, the lidar was generally lower than the glider. Overall, the results are promising and suggest that airborne lidar and gliders provide comparable and complementary information on optical particulate backscattering.

Wave-front sensing from artificial beacons is normally performed by formation of a focused spot in the atmosphere and sensing of the wave-front distortions produced during the beam's return passage. We propose an alternative method that senses the distortions produced during the outgoing path by forming an intensity pattern in the atmosphere that is then viewed from the ground. A key advantage of this method is that a parallel beam is used, and therefore the wave-front measurements will not suffer from the effects of focal anisoplanatism. We also envisage other geometries, all based on the concept of projecting a pupil pattern onto the atmosphere.

Adaptive optics systems are essential on all large telescopes for which image quality is important. These are complex systems with many design parameters requiring optimization before good performance can be achieved. The simulation of adaptive optics systems is therefore necessary to categorize the expected performance. We describe an adaptive optics simulation platform, developed at Durham University, which can be used to simulate adaptive optics systems on the largest proposed future extremely large telescopes as well as on current systems. This platform is modular, object oriented, and has the benefit of hardware application acceleration that can be used to improve the simulation performance, essential for ensuring that the run time of a given simulation is acceptable. The simulation platform described here can be highly parallelized using parallelization techniques suited for adaptive optics simulation, while still offering the user complete control while the simulation is running. The results from the simulation of a ground layer adaptive optics system are provided as an example to demonstrate the flexibility of this simulation platform.

Modeling of radiation characteristics of semitransparent media containing particles or bubbles in the independent scattering limit is examined. The existing radiative properties models of a single particle in an absorbing medium using the approaches based on (1) the classical Mie theory neglecting absorption by the matrix, (2) the far field approximation, and (3) the near field approximation are reviewed. Comparison between models and experimental measurements are carried out not only for the radiation characteristics but also for hemispherical transmittance and reflectance of porous fused quartz. Large differences are found among the three models predicting the bubble radiative properties when the matrix is strongly absorbing and/or the bubbles are optically large. However, these disagreements are masked by the matrix absorption during calculation of radiation characteristics of the participating medium. It is shown that all three approaches can be used for radiative transfer calculations in an absorbing matrix containing bubbles.