Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R_rs, is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R_rs (uc(R_rs)) at each pixel. Uncertainty in R_rs, in turn, is dependent on the propagation of various uncertainty sources through the R_rs retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R_rs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(R_rs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ R_rs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R_rs at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/m³) with valid uc(R_rs) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that uc(R_rs) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.

Towards the Spectral Mapping of Plastic Debris on Beaches

Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from samples that only contained organic debris and sand between 1.56% and 7.94% surface cover; however other samples with similar surface cover did not have reflectance that was statistically different from samples containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes.

Atmospheric correction over the ocean for hyperspectral radiometers using multi-angle polarimetric retrievals

We developed a fast and accurate polynomial based atmospheric correction (POLYAC) algorithm for hyperspectral radiometric measurements, which parameterizes the atmospheric path radiances using aerosol properties retrieved from co-located multi-wavelength multi-angle polarimeter (MAP) measurements. This algorithm has been applied to co-located spectrometer for planetary exploration (SPEX) airborne and research scanning polarimeter (RSP) measurements, where SPEX airborne was used as a proxy of hyperspectral radiometers, and RSP as the MAP. The hyperspectral remote sensing reflectance obtained from POLYAC is accurate when compared to Aerosol Robotic Network (AERONET), and Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. POLYAC provides a robust alternative atmospheric correction algorithm for hyperspectral or multi-spectral radiometric measurements for scenes involving coastal oceans and/or absorbing aerosols, where traditional atmospheric correction algorithms are less reliable.

Application of Spectral Mixture Analysis to Vessel Monitoring Using Airborne Hyperspectral Data

As marine transportation has increased in coastal regions, maritime accidents associated with vessels have steadily increased. Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Various spectral libraries of marine objects and seawaters were constructed through in-situ experiments for spectral analysis of the internal structures of vessels. The hyperspectral images were dimensionally reduced through principal component analysis. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The N-FINDR and VCA techniques presented a total of 14 vessels, the ICA technique detected seven vessels, and the PPI technique detected two vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. The sizes of the vessels were estimated by extracting the boundaries of the vessels through a two-dimensional gradient and applying the ellipse fitting method. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square errors of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively. The application of spectral mixing methods provided a high probability of detecting the objects, as well as the overall structures of the decks of the vessels.

PACO: Python-Based Atmospheric COrrection

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.

Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point

The NASA's Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317⁻780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

Remote Sensing of Coral Reefs: Uncertainty in the Detection of Benthic Cover, Depth, and Water Constituents Imposed by Sensor Noise

Coral reefs are biologically diverse and economically important ecosystems that are on the decline worldwide in response to direct human impacts and climate change. Ocean color remote sensing has proven to be an important tool in coral reef research and monitoring. Remote sensing data quality is driven by factors related to sensor design and environmental variability. This work explored the impact of sensor noise, defined as the signal to noise ratio (SNR), on the detection uncertainty of key coral reef ecological properties (bottom depth, benthic cover, and water quality) in the absence of environmental uncertainties. A radiative transfer model for a shallow reef environment was developed and Monte Carlo methods were employed to identify the range in environmental conditions that are spectrally indistinguishable from true conditions as a function of SNR. The spectrally averaged difference between remotely sensed radiance relative to sensor noise, ε, was used to quantify uncertainty in bottom depth, the fraction of benthic cover by coral, algae, and uncolonized sand, and the concentration of water constituents defined as chlorophyll, dissolved organic matter, and suspended calcite particles. Parameter uncertainty was found to increase with sensor noise (decreasing SNR) but the impact was non-linear. The rate of change in uncertainty per incremental change in SNR was greatest for SNR < 500 and increasing SNR further to 1000 resulted in only modest improvements. Parameter uncertainty was complicated by the bottom depth and benthic cover. Benthic cover uncertainty increased with bottom depth, but water constituent uncertainty changed inversely with bottom depth. Furthermore, water constituent uncertainty was impacted by the type of constituent material in relation to the type of benthic cover. Uncertainty associated with chlorophyll concentration and dissolved organic matter increased when the benthic cover was coral and/or benthic algae while uncertainty in the concentration of suspended calcite increased when the benthic cover was uncolonized sand. While the definition of an optimal SNR is subject to user needs, we propose that SNR of approximately 500 (relative to 5% Earth surface reflectance and a clear maritime atmosphere) is a reasonable engineering goal for a future satellite sensor to support research and management activities directed at coral reef ecology and, more generally, shallow aquatic ecosystems.

Propagation of sensor noise in oceanic hyperspectral remote sensing

In previous works, the authors have shown via numerical simulation that sensor noise, even assuming otherwise perfect knowledge of the environment, can cause large scale variations in the retrieval of concentrations of biophysical parameters in a water body, and also investigated methods for using statistical measures (such as the Mahalanobis distance) to help mitigate these issues. In this work, we derive explicit formulas that can be used to estimate how uncertainty in the sensor radiance is propagated to uncertainty in the remote sensing reflectanceR_rs(λ), without the need for simulations. In particular, the formulas show that the variation in R_rs(λ)is affected by not only the noise characteristics of the sensor, but also by the conditions (atmospheric parameters, viewing angles, altitude) under which the data is collected. We include validation results for the formulas over a wide range of atmospheric conditions, and show by example how the collection conditions can affect the uncertainty in R_rs(λ).

Simultaneous determination of aerosol optical depth and exponent of the Junge power law from MODIS shortwave infrared bands over Qinghai Lake

The water-leaving radiances for shortwave infrared (SWIR) channels can be negligible, and these channels also contain information on aerosol particle size. Therefore, the satellite-based data of SWIR channels can be used to estimate aerosol particle size over inland waters [Appl. Opt.39, 887 (2000)APOPAI0003-693510.1364/AO.39.000887]. Supposing the actual atmospheric aerosol size distribution is based on the Junge power law, in this paper an iterative algorithm is used to simultaneously determine the aerosol optical depth (AOD) and the exponent of the Junge power law from Aqua MODIS L1B reflectance data of channels 1.64 μm and 2.13 μm over Qinghai Lake. Whether using the constant or variable aerosol complex refractive index (ACRI), the retrieved exponent of the Junge power law is always larger than the product value. Supposing the product values are accurate, for the constant ACRI, there are 68.91% and 25.48% pixels of acceptable retrieval AOD and the exponent of the Junge power-law value, respectively. Likewise, there are 71.63% and 43.75% pixels for variable ACRI. Compared with the retrieval error under constant ACRI, there are 58.65% and 98.72% pixels, with a smaller AOD and Junge power-law index retrieval error under variable ACRIs, respectively. In addition, the precision of the AOD retrieved with variable ACRI is improved when the AOD product is less than 0.17. However, under the current environment with frequent aerosol particle pollution, the same ACRI for the ten wavelengths can achieve results with equivalent accuracy compared with variable ACRI.

Pencil-like imaging spectrometer for bio-samples sensing

Spectrally-resolved imaging techniques are becoming central to the investigation of bio-samples. In this paper, we demonstrate the use of a WIFI-camera as a detection module to assemble a pencil-like imaging spectrometer, which weighs only 140 g and has a size of 3.1 cm in diameter and 15.5 cm in length. The spectrometer is standalone, and works wirelessly. A smartphone or network computer can serve as the data receiver and processor. The wavelength resolution of the spectrometer is about 17 nm, providing repeatable measurements of spatial two-dimensional images at various wavelengths for various bio-samples, including bananas, meat, and human hands. The detected spectral range is 400 nm - 675 nm and a white LED array lamp is selected as the light source. Based on the detected spectra, we can monitor the impacts of chlorophyll, myoglobin, and hemoglobin on bananas, pork, and human hands, respectively. For human hand scanning, a 3D spectral image data cube, which exhibits excellent signal to background noise ratio, can be obtained within 16 sec. We envisage that the adaptation of imaging spectrometer devices to the widely-accepted smartphone technology will help to carry out on-site studies in various applications. Besides, our pencil-like imaging spectrometer is cost-effective (<$300) and easy to assemble. This portable imaging spectrometer can facilitate the collection of large amounts of spectral image data. With the help of machine learning, we can realize object recognition based on spectral classification in the future.

Estimation of chlorophyll concentration in waters near Hokkaido using the linear combination method

An inversion algorithm is implemented to retrieve the surface chlorophyll a (Chl) concentration using satellite observation data from the MODIS instrument. The algorithm employs a simple and flexible index (LCI) to combine with the Chl without explicit correction for aerosol scattering. To investigate the sensitivity of LCI to Chl and other influence factors, an oceanic radiative transfer model coupled with a comprehensive bio-optical module is developed. It is studied that the LCI is significantly linear to Chl and not sensitive to other influence factors, except in very low oceanic salinity or scattering angle conditions, where over a 12% relative difference of derived Chl exists. Inversion results show the retrieved Chl are highly consistent with the MODIS operational data products in waters near Hokkaido, with the correlation coefficient, root mean square deviation, and average percentage difference of 0.9702, 0.3756 mg m^-3, and 13.89%, respectively. Investigation of the validity of this algorithm with a variety of atmospheric conditions indicates that the residual influence of atmosphere on the LCI index, after Rayleigh scattering correction, is generally within ± 0.001, allowing the retrieval error of Chl at less than 25% in most cases. A good comparison between retrieval and in situ measurements is also identified and implies that the retrieval accuracy via the LCI method depends on the linear combination coefficients used and the bio-optical module selected, while effects of polarization can be ignored.

Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters

The shortwave infrared (SWIR) bands on the existing Earth Observing missions like MODIS have been designed to meet land and atmospheric science requirements. The future geostationary and polar-orbiting ocean color missions, however, require highly sensitive SWIR bands (> 1550nm) to allow for a precise removal of aerosol contributions. This will allow for reasonable retrievals of the remote sensing reflectance (R_rs) using standard NASA atmospheric corrections over turbid coastal waters. Design, fabrication, and maintaining high-performance SWIR bands at very low signal levels bear significant costs on dedicated ocean color missions. This study aims at providing a full analysis of the utility of alternative SWIR bands within the 1600nm atmospheric window if the bands within the 2200nm window were to be excluded due to engineering/cost constraints. Following a series of sensitivity analyses for various spectral band configurations as a function of water vapor amount, we chose spectral bands centered at 1565 and 1675nm as suitable alternative bands within the 1600nm window for a future geostationary imager. The sensitivity of this band combination to different aerosol conditions, calibration uncertainties, and extreme water turbidity were studied and compared with that of all band combinations available on existing polar-orbiting missions. The combination of the alternative channels was shown to be as sensitive to test aerosol models as existing near-infrared (NIR) band combinations (e.g., 748 and 869nm) over clear open ocean waters. It was further demonstrated that while in extremely turbid waters the 1565/1675 band pair yields R_rs retrievals as good as those derived from all other existing SWIR band pairs (> 1550nm), their total calibration uncertainties must be < 1% to meet current science requirements for ocean color retrievals (i.e., Δ R_rs (443) < 5%). We further show that the aerosol removal using the NIR and SWIR bands (available on the existing polar-orbiting missions) can be very sensitive to calibration uncertainties. This requires the need for monitoring the calibration of these bands to ensure consistent multi-mission ocean color products in coastal/inland waters.

Atmospheric correction for retrieving ground brightness temperature at commonly-used passive microwave frequencies

An analysis of the atmospheric impact on ground brightness temperature (Tg) is performed for numerous land surface types at commonly-used frequencies (i.e., 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz). The results indicate that the atmosphere has a negligible impact on Tg at 1.4 GHz for land surfaces with emissivities greater than 0.7, at 6.93 GHz for land surfaces with emissivities greater than 0.8, and at 10.65 GHz for land surfaces with emissivities greater than 0.9 if a root mean square error (RMSE) less than 1 K is desired. To remove the atmospheric effect on Tg, a generalized atmospheric correction method is proposed by parameterizing the atmospheric transmittance τ and upwelling atmospheric brightness temperature Tba↑. Better accuracies with Tg RMSEs less than 1 K are achieved at 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz, and worse accuracies with RMSEs of 1.34 K and 4.35 K are obtained at 23.8 GHz and 89.0 GHz, respectively. Additionally, a simplified atmospheric correction method is developed when lacking sufficient input data to perform the generalized atmospheric correction method, and an emissivity-based atmospheric correction method is presented when the emissivity is known. Consequently, an appropriate atmospheric correction method can be selected based on the available data, frequency and required accuracy. Furthermore, this study provides a method to estimate τ and Tba↑ of different frequencies using the atmospheric parameters (total water vapor content in observation direction Lwv, total cloud liquid water content Lclw and mean temperature of cloud Tclw), which is important for simultaneously determining the land surface parameters using multi-frequency passive microwave satellite data.

Radiative transfer simulations of the two-dimensional ocean glint reflectance and determination of the sea surface roughness

An optimized discrete-ordinate radiative transfer model (DISORT3) with a pseudo-two-dimensional bidirectional reflectance distribution function (BRDF) is used to simulate and validate ocean glint reflectances at an infrared wavelength (1036 nm) by matching model results with a complete set of BRDF measurements obtained from the NASA cloud absorption radiometer (CAR) deployed on an aircraft. The surface roughness is then obtained through a retrieval algorithm and is used to extend the simulation into the visible spectral range where diffuse reflectance becomes important. In general, the simulated reflectances and surface roughness information are in good agreement with the measurements, and the diffuse reflectance in the visible, ignored in current glint algorithms, is shown to be important. The successful implementation of this new treatment of ocean glint reflectance and surface roughness in DISORT3 will help improve glint correction algorithms in current and future ocean color remote sensing applications.

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

Space station image captures a red tide ciliate bloom at high spectral and spatial resolution

Mesodinium rubrum is a globally distributed nontoxic ciliate that is known to produce intense red-colored blooms using enslaved chloroplasts from its algal prey. Although frequent enough to have been observed by Darwin, blooms of M. rubrum are notoriously difficult to quantify because M. rubrum can aggregate into massive clouds of rusty-red water in a very short time due to its high growth rates and rapid swimming behavior and can disaggregate just as quickly by vertical or horizontal dispersion. A September 2012 hyperspectral image from the Hyperspectral Imager for the Coastal Ocean sensor aboard the International Space Station captured a dense red tide of M. rubrum (10(6) cells per liter) in surface waters of western Long Island Sound. Genetic data confirmed the identity of the chloroplast as a cryptophyte that was actively photosynthesizing. Microscopy indicated extremely high abundance of its yellow fluorescing signature pigment phycoerythrin. Spectral absorption and fluorescence features were related to ancillary photosynthetic pigments unique to this organism that cannot be observed with traditional satellites. Cell abundance was estimated at a resolution of 100 m using an algorithm based on the distinctive yellow fluorescence of phycoerythrin. Future development of hyperspectral satellites will allow for better enumeration of bloom-forming coastal plankton, the associated physical mechanisms, and contributions to marine productivity.

Expected improvements in the quantitative remote sensing of optically complex waters with the use of an optically fast hyperspectral spectrometer-a modeling study

Using simulated data, we investigated the effect of noise in a spaceborne hyperspectral sensor on the accuracy of the atmospheric correction of at-sensor radiances and the consequent uncertainties in retrieved water quality parameters. Specifically, we investigated the improvement expected as the F-number of the sensor is changed from 3.5, which is the smallest among existing operational spaceborne hyperspectral sensors, to 1.0, which is foreseeable in the near future. With the change in F-number, the uncertainties in the atmospherically corrected reflectance decreased by more than 90% across the visible-near-infrared spectrum, the number of pixels with negative reflectance (caused by over-correction) decreased to almost one-third, and the uncertainties in the retrieved water quality parameters decreased by more than 50% and up to 92%. The analysis was based on the sensor model of the Hyperspectral Imager for the Coastal Ocean (HICO) but using a 30-m spatial resolution instead of HICO’s 96 m. Atmospheric correction was performed using Tafkaa. Water quality parameters were retrieved using a numerical method and a semi-analytical algorithm. The results emphasize the effect of sensor noise on water quality parameter retrieval and the need for sensors with high Signal-to-Noise Ratio for quantitative remote sensing of optically complex waters.

An infrared scanning and tracking system for detecting mid-wave infrared spectral characteristics of moving targets

This paper presents the design and fabrication of an infrared scanning and tracking system for detecting mid-wave infrared (MWIR) spectral characteristics of moving targets. The infrared spectra and infrared image are integrated in this system, which is mainly composed of a two-dimensional (2D) scanning mirror, dual-band infrared lens, long-wave infrared imaging unit, MWIR spectrum-measuring unit, and processing-controlling unit. After describing the design specifications of this system, this paper analyzes the detection method and then describes how the tracking was realized by controlling the 2D scanning mirror. Experiments were carried out to verify its feasibility.

Correction for reflected sky radiance in low-altitude coastal hyperspectral images

Low-altitude coastal hyperspectral imagery is sensitive to reflections of sky radiance at the water surface. Even in the absence of sun glint, and for a calm water surface, the wide range of viewing angles may result in pronounced, low-frequency variations of the reflected sky radiance across the scan line depending on the solar position. The variation in reflected sky radiance can be obscured by strong high-spatial-frequency sun glint and at high altitude by path radiance. However, at low altitudes, the low-spatial-frequency sky radiance effect is frequently significant and is not removed effectively by the typical corrections for sun glint. The reflected sky radiance from the water surface observed by a low-altitude sensor can be modeled in the first approximation as the sum of multiple-scattered Rayleigh path radiance and the single-scattered direct-solar-beam radiance by the aerosol in the lower atmosphere. The path radiance from zenith to the half field of view (FOV) of a typical airborne spectroradiometer has relatively minimal variation and its reflected radiance to detector array results in a flat base. Therefore the along-track variation is mostly contributed by the forward single-scattered solar-beam radiance. The scattered solar-beam radiances arrive at the water surface with different incident angles. Thus the reflected radiance received at the detector array corresponds to a certain scattering angle, and its variation is most effectively parameterized using the downward scattering angle (DSA) of the solar beam. Computation of the DSA must account for the roll, pitch, and heading of the platform and the viewing geometry of the sensor along with the solar ephemeris. Once the DSA image is calculated, the near-infrared (NIR) radiance from selected water scan lines are compared, and a relationship between DSA and NIR radiance is derived. We then apply the relationship to the entire DSA image to create an NIR reference image. Using the NIR reference image and an atmospheric spectral reflectance look-up table, the low spatial frequency variation of the water surface-reflected atmospheric contribution is removed.

A fast smoothing algorithm for post-processing of surface reflectance spectra retrieved from airborne imaging spectrometer data

Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved surface reflectance spectra. In the present spectral smoothing technique, model-derived reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived reflectance spectra, is then derived. This gain curve is finally applied to all of the reflectance spectra in a scene to obtain the spectrally smoothed surface reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented.

Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise in the data. In this study, we have compared the Euclidean distance and the Mahalanobis distance as metrics for spectral comparison. In addition to providing justification for the preference of the Mahalanobis Distance over the Euclidean Distance, we have also included a statistical description of noisy hyperspectral data.

Vicarious calibrations of HICO data acquired from the International Space Station

The Hyperspectral Imager for the Coastal Ocean (HICO) presently onboard the International Space Station (ISS) is an imaging spectrometer designed for remote sensing of coastal waters. The instrument is not equipped with any onboard spectral and radiometric calibration devices. Here we describe vicarious calibration techniques that have been used in converting the HICO raw digital numbers to calibrated radiances. The spectral calibration is based on matching atmospheric water vapor and oxygen absorption bands and extraterrestrial solar lines. The radiometric calibration is based on comparisons between HICO and the EOS/MODIS data measured over homogeneous desert areas and on spectral reflectance properties of coral reefs and water clouds. Improvements to the present vicarious calibration techniques are possible as we gain more in-depth understanding of the HICO laboratory calibration data and the ISS HICO data in the future.

Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters

Errors in the estimated constituent concentrations in optically complex waters due solely to sensor noise in a spaceborne hyperspectral sensor can be as high as 80%. The goal of this work is to elucidate the effect of signal-to-noise ratio (SNR) on the accuracy of retrieved constituent concentrations. Large variations in the magnitude and spectral shape of the reflectances from coastal waters complicate the impact of SNR on the accuracy of estimation. Due to the low reflectance of water, the actual SNR encountered for a water target is usually quite lower than the prescribed SNR. The low SNR can be a significant source of error in the estimated constituent concentrations. Simulated and measured at-surface reflectances were used in this study. A radiative transfer code, Tafkaa, was used to propagate the at-surface reflectances up and down through the atmosphere. A sensor noise model based on that of the spaceborne hyperspectral sensor HICO was applied to the at-sensor radiances. Concentrations of chlorophyll-a, colored dissolved organic matter, and total suspended solids were estimated using an optimized error minimization approach and a few semi-analytical algorithms. Improving the SNR by reasonably modifying the sensor design can reduce estimation uncertainties by 10% or more.

Ground-based optical measurements at European flux sites: a review of methods, instruments and current controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903-"Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe" that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites.

Multispectral image enhancement for effective visualization

Color enhancement of multispectral images is useful to visualize the image's spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

Cross calibration of ocean-color bands from moderate resolution imaging spectroradiometer on Terra platform

Ocean-color applications require maximum uncertainties in blue-wavelength water-leaving radiances in oligotrophic ocean of approximately 5%. Water-leaving radiances from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite, however, exhibit temporal drift of the order of 15% as well as sensor changes in response versus scan and polarization sensitivity, which cannot be tracked by onboard calibrators. This paper introduces an instrument characterization approach that uses Earth-view data as a calibration source. The approach models the top of the atmosphere signal over ocean that the instrument is expected to measure, including its polarization, with water-leaving radiances coming from another well-calibrated global sensor. The cross calibration allows for significant improvement in derived MODIS-Terra ocean-color products, with largest changes in the blue wavelengths.

Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii

Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.

Improving atmospheric correction for highly productive coastal waters using the short wave infrared retrieval algorithm with water-leaving reflectance constraints at 412 nm

The recently developed short wave infrared (SWIR) atmospheric correction algorithm for ocean color retrieval uses long wavelength channels to retrieve atmospheric parameters to avoid bright pixel contamination. However, this retrieval is highly sensitive to errors in the aerosol model, which is magnified by the higher variability of aerosols observed over urban coastal areas. While adding extra regional aerosol models into the retrieval lookup tables would tend to increase retrieval error since these models are hard to distinguish in the IR, we explore the possibility that for highly productive waters with high colored dissolved organic matter, an estimate of the 412 nm channel water-leaving reflectance can be used to constrain the aerosol model retrieval and improve the water-leaving reflectance retrieval. Simulations show that this constraint is particularly useful where aerosol diversity is significant. To assess this algorithm we compare our retrievals with the operational SeaWiFS Data Analysis System (SeaDAS) SWIR and near infrared retrievals using in situ validation data in the Chesapeake Bay and show that, especially for absorbing aerosols, significant improvement is obtained. Further insight is also obtained by the intercomparison of retrieved remote sensing reflectance images at 443 and 551 nm, which demonstrates the removal of anomalous artifacts in the operational SeaDAS retrieval.

Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry

The retrieval of ocean color radiometry from space-based sensors requires on-orbit vicarious calibration to achieve the level of accuracy desired for quantitative oceanographic applications. The approach developed by the NASA Ocean Biology Processing Group (OBPG) adjusts the integrated instrument and atmospheric correction system to retrieve normalized water-leaving radiances that are in agreement with ground truth measurements. The method is independent of the satellite sensor or the source of the ground truth data, but it is specific to the atmospheric correction algorithm. The OBPG vicarious calibration approach is described in detail, and results are presented for the operational calibration of SeaWiFS using data from the Marine Optical Buoy (MOBY) and observations of clear-water sites in the South Pacific and southern Indian Ocean. It is shown that the vicarious calibration allows SeaWiFS to reproduce the MOBY radiances and achieve good agreement with radiometric and chlorophyll a measurements from independent in situ sources. We also find that the derived vicarious gains show no significant temporal or geometric dependencies, and that the mission-average calibration reaches stability after approximately 20-40 high-quality calibration samples. Finally, we demonstrate that the performance of the vicariously calibrated retrieval system is relatively insensitive to the assumptions inherent in our approach.

Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations

In the remote sensing of the ocean near-surface properties, it is essential to derive accurate water-leaving radiance spectra through the process of the atmospheric correction. The atmospheric correction algorithm for Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) uses two near-infrared (NIR) bands at 765 and 865 nm (748 and 869 nm for MODIS) for retrieval of aerosol properties with assumption of the black ocean at the NIR wavelengths. Modifications are implemented to account for some of the NIR ocean contributions for the productive but not very turbid waters. For turbid waters in the coastal regions, however, the ocean could have significant contributions in the NIR, leading to significant errors in the satellite-derived ocean water-leaving radiances. For the shortwave infrared (SWIR) wavelengths (approximately > 1000 nm), water has significantly larger absorption than those for the NIR bands. Thus the black ocean assumption at the SWIR bands is generally valid for turbid waters. In addition, for future sensors, it is also useful to include the UV bands to better quantify the ocean organic and inorganic materials, as well as for help in atmospheric correction. Simulations are carried out to evaluate the performance of atmospheric correction for nonabsorbing and weakly absorbing aerosols using the NIR bands and various combinations of the SWIR bands for deriving the water-leaving radiances at the UV (340 nm) and visible wavelengths. Simulations show that atmospheric correction using the SWIR bands can generally produce results comparable to atmospheric correction using the NIR bands. In particular, the water-leaving radiance at the UV band (340 nm) can also be derived accurately. The results from a sensitivity study for the required sensor noise equivalent reflectance, (NE Delta rho), [or the signal-to-noise ratio (SNR)] for the NIR and SWIR bands are provided and discussed.

Remote sensing of phytoplankton chlorophyll-a concentration by use of ridge function fields

A methodology is presented for retrieving phytoplankton chlorophyll-a concentration from space. The data to be inverted, namely, vectors of top-of-atmosphere reflectance in the solar spectrum, are treated as explanatory variables conditioned by angular geometry. This approach leads to a continuum of inverse problems, i.e., a collection of similar inverse problems continuously indexed by the angular variables. The resolution of the continuum of inverse problems is studied from the least-squares viewpoint and yields a solution expressed as a function field over the set of permitted values for the angular variables, i.e., a map defined on that set and valued in a subspace of a function space. The function fields of interest, for reasons of approximation theory, are those valued in nested sequences of subspaces, such as ridge function approximation spaces, the union of which is dense. Ridge function fields constructed on synthetic yet realistic data for case I waters handle well situations of both weakly and strongly absorbing aerosols, and they are robust to noise, showing improvement in accuracy compared with classic inversion techniques. The methodology is applied to actual imagery from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS); noise in the data are taken into account. The chlorophyll-a concentration obtained with the function field methodology differs from that obtained by use of the standard SeaWiFS algorithm by 15.7% on average. The results empirically validate the underlying hypothesis that the inversion is solved in a least-squares sense. They also show that large levels of noise can be managed if the noise distribution is known or estimated.

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Piffalls in atmospheric correction of ocean color imagery: how should aerosol optical properties be computed?

Current methods for the atmospheric correction of ocean-color imagery rely on the computation of optical properties of a mixture of chemically different aerosol particles through combination of the mixture with it into an effective, single-particle component that has an average refractive index. However, a multi-component approach in which each particle type independently grows and changes its refractive index with increasing humidity is more realistic. Computations based on Mie theory and radiative transfer are used to show that the two approaches result in top-of-the-atmosphere radiances that differ more than the water-leaving radiance. Thus, proper atmospheric correction requires a multicomponent approach for the computation of realistic aerosol optical properties.