Optical Properties Of Water Column Research Articles

Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems

The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues.

Seasonality and drivers of water column optical properties on the northwestern Barents Sea shelf

Hydrographic and bio-optical measurements were conducted along a south–north transect on the northwestern Barents Sea shelf from early spring to late summer in 2021. Strong climate change manifestations observed in this region are rapidly changing the marine environment. These rare observations cover the seasonal evolution from well-mixed and sea ice-covered winter conditions, through sea ice retreat in spring, to late summer where the sea ice had largely retreated and the water column was stratified due to sea ice melt.Phytoplankton drives the spatial and temporal variability in optical properties in most of the water column, but increased scattering and absorption could also be seen in the bottom boundary layer due to resuspended particles. The relationship between chlorophyll-a and particulate absorption deviates from the globally observed relationship, likely due to light adaptations in the ice-covered water masses. We recommend developing specific models for spring phytoplankton growth in ice-covered waters to accurately account for self-shading effects. The absorption of colored dissolved organic matter (CDOM) was relatively low, due to waters of Atlantic origin in the studied region, and varied considerably less than particulate absorption. CDOM was nevertheless the optically dominant ocean constituent in the very clear waters in late winter.Regional relationships for estimating particulate organic carbon (POC) and chlorophyll-a concentrations from in situ attenuation and fluorescence measurements were developed. POC may act as an alternative indicator to chlorophyll-a for optical properties in ice-covered open ocean, which is relevant for light availability parametrizations in biogeochemical ocean models.

Performance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment

Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.

Assessing the current water clarity status of ~100,000 lakes across southern Canada: A remote sensing approach

Canada has more lakes than any other country, making comprehensive monitoring a huge challenge. As more and more satellite data become readily available, and as faster data processing systems make massive satellite data operations possible, new opportunities exist to use remote sensing to develop comprehensive assessments of water quality at very large spatial scales. In this study, we use a published empirical algorithm to estimate Secchi depth from Landsat 8 reflectance data in order to estimate water clarity in lakes across southern Canada. Combined with ancillary information on lake morphological, hydrological, and watershed geological and landuse characteristics, we were able to assess broad spatial patterns in water clarity for the first time. Ecological zones, underlying geological substrate, and lake depth had particularly strong influences on clarity across the whole country. Lakes in western mountain ecozones had significantly clearer waters than those in the prairies and plains, while lakes in sedimentary rock formations tended to have lower clarity than lakes in intrusive rock. Deep lakes were significantly clearer than shallow lakes over most of the country. Water clarity was also significantly influenced by human impact (urbanization, agriculture, and industry) in the watershed, with most lakes in high impact areas having low clarity or very low clarity. Finally, we used in situ measured data to help interpret the underlying optical water column constituents influencing clarity across Canada, and found that chlorophyll-a, total suspended solids, and color dissolved organic matter all had strong but varying underlying effects on water clarity across different ecozones. This research provides an important step towards further research on the relationship between water column optical properties and the health and vulnerability status of lakes across the country.

Vertical distribution of subsurface phytoplankton layer in South China Sea using airborne lidar

The vertical distribution of subsurface phytoplankton in the ocean contains key information about not only ocean ecology but also water column optical properties relevant to remote sensing. The existing methods for measuring this vertical distribution are mainly field bio-optical and biogeochemical observations, which are time- and labor-intensive and do not provide sufficient spatial coverage. Passive satellite remote sensing provides global observations of phytoplankton but offers no information on subsurface vertical structure. In this study, we made the first quantitative measurements of vertical distribution of the subsurface phytoplankton layer (SPL) in the South China Sea (SCS) using airborne lidar. A total of five lidar flight experiments were conducted between 2017 and 2019 in the SCS, and approximately 2.5 terabytes of data were obtained. A hybrid retrieval method combining the Klett method for klidar and the perturbation method forβπ was developed. The lidar-retrieved chlorophyll-a concentrations and in situ data show good agreement (R2 greater than 0.8). The mean absolute percentage error for the lidar-retrieved chlorophyll-a concentrations is less than 15%. SPLs were observed both in Sanya Bay and in the open sea near Lingshui city, Hainan Province, China. The SPLs were observed at depths between 50 and 70 m in the open sea near Lingshui city, and between 5 and 30 m in Sanya Bay. The SPL depths have spatiotemporal variability, and we analyzed the possible factors (monsoon, seafloor depth, and sea surface temperature) that influence this spatiotemporal variability. The results show that lidar technology has a great potential for wide-range and long-term monitoring of SPLs, and is a good complement for discrete in situ observations and passive satellite remote sensing.

Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data

Polar-orbiting ocean color satellites such as Landsat-8, Suomi National Polar-orbiting Partnership (SNPP), and Sentinel-3 offer valuable image data for the derivation of water bathymetry in optically shallow environments. Because of the multi-spectral limitation, however, it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms. In this contribution, we present and test a physics-based algorithm for improved retrieval of bathymetry with multi-spectral sensors. The algorithm leverages the temporal variation of water-column optical properties in two satellite measurements. By incorporating two remote sensing reflectance spectra in an optimization procedure, it enhances the spectral constraining condition for the optimization, thus leading to improved retrieval accuracy. This scheme is evaluated using synthetic multi-spectral data. It is shown that the new approach can provide accurate estimation of water depths over 0–30 m range with three types of benthic substrates (corals, seagrass, and sand) and for a wide range of water column optical properties. Based on the degree of improvement, Landsat-8 appears to be benefited the most, followed by SNPP, and then Sentinel-3. The application of the new approach is demonstrated with satellite images over shallow waters (0–30 m) dominated with coral reefs, seagrass, and sand, respectively. This proof-of-concept study confirms the promise of multi-spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements, suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters.

Water Column Optical Properties of Pacific Coral Reefs Across Geomorphic Zones and in Comparison to Offshore Waters

Despite the traditional view of coral reefs occurring in oligotrophic tropical conditions, water optical properties over coral reefs differ substantially from nearby clear oceanic waters. Through an extensive set of optical measurements across the tropical Pacific, our results suggest that coral reefs themselves exert a high degree of influence over water column optics, primarily through release of colored dissolved organic matter (CDOM). The relative contributions of phytoplankton, non-algal particles, and CDOM were estimated from measurements of absorption and scattering across different geomorphic shallow-water reef zones (<10 m) in Hawaii, the Great Barrier Reef, Guam, and Palau (n = 172). Absorption was dominated at the majority of stations by CDOM, with mixtures of phytoplankton and CDOM more prevalent at the protected back reef and lagoon zones. Absorption could be dominated by sediments and phytoplankton at fringing reefs and terrestrially impacted sites where particulate backscattering was significantly higher than in the other zones. Scattering at three angles in the backward direction followed recent measurements of the particulate phase function. Optical properties derived from satellite imagery indicate that offshore waters are consistently lower in absorption and backscattering than reef waters. Therefore, the use of satellite-derived offshore parameters in modeling reef optics could lead to significant underestimation of absorption and scattering, and overestimation of benthic light availability. If local measurements are not available, average optical properties based on the general reef zone could provide a more accurate means of assessing light conditions on coral reefs than using offshore water as a proxy.

Vertical distributions of blooming cyanobacteria populations in a freshwater lake from LIDAR observations

The vertical distributions of freshwater cyanobacteria populations are important to plankton community structure, ecology and for influencing water column optical properties relevant to remote sensing. In August of 2014, we examined the vertical structure of a cyanobacteria bloom across the western basin of Lake Erie with new technologies, including LIDAR and a digital holographic system. In addition, vertical profiles of environmental and optical properties were made. The active LIDAR penetrated the water column, and provided a detailed picture of the particle distribution for the whole water column. The holographic system provided digital images processed for particle size, count and identification of Microcystis and Planktothrix - the two main cyanobacteria genera that were present. The correlations between the LIDAR backscatter intensity and the cyanobacteria cell counts from holography averaged to 0.53 and ranged from −0.13 to 0.96 based on nearest matchups. The vertical structure of the overall cyanobacteria population was influenced by wind speed, and to a lesser degree the solar heating of surface waters. On a more detailed level, Microcystis populations were consistently nearer to the surface relative to Planktothrix. Pigments from surface samples revealed a higher degree of photoprotection for Planktothrix-dominated communities. The vertical distributions of the cyanobacteria genera were related to light intensity in the water column and known tolerances and/or preferences for each genus. Vertical profiles of optical properties supported the patterns seen in the LIDAR and holographic data, and had direct implications on the exiting light field. These combined data provide a unique view into the natural variations in spatial (vertical and horizontal) distribution patterns of cyanobacteria and resulting impacts on remote sensing detection and associated interpretations, and demonstrate the potential for these technologies to observe cyanobacteria in lake environments.

Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA

AbstractRemote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements with a 1.6‐km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths with a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis and the K nearest neighbors regression machine learning algorithm. Linear, quadratic, exponential, power, and lowess Optimal Band Ratio Analysis models enabled flexible curve‐fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. K nearest neighbors regression increased observed versus predicted (OP) R2 values, particularly for the satellite image; we also found that preprocessing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

Approach for identifying optically shallow pixels when processing ocean-color imagery.

Ocean-color remote sensing is routinely used to derive marine geophysical parameters from sensor-observed spectral water-leaving radiances. However, in clear geometrically shallow regions, traditional ocean-color algorithms can be confounded by light reflected from the seafloor. Such regions are typically referred to as "optically shallow". When performing spatiotemporal analyses of ocean color datasets, optically shallow features such as submerged sand banks and coral reefs can lead to unexpected regional biases. Most contemporary approaches mask or flag suspected optically shallow pixels based on ancillary bathymetric data. However, the extent to which seafloor reflectance contaminates the water-leaving radiance is dependent on bathymetry, water clarity and seafloor albedo. In this paper, an approach for flagging optically shallow pixels has been developed that considers all three of these variables. In the method, the optical depth of the water column at 547 nm, ζ(547), is predicted from bathymetric data and estimated water-column optical properties. If ζ(547) is less then the pre-defined threshold, a pixel is flagged as potentially optically shallow. Radiative transfer modeling was used to identify a conservative threshold value of ζ(547) = 20 for a bright sand seafloor. In addition, pixels in waters shallower than 5 m are also flagged. We also examined how varying bathymetric datasets may affect the optically shallow flag using MODIS data. It is anticipated that the optically shallow flag will benefit end-users when quality controlling derived ocean color products. Further, the flag may prove useful as a mechanism for switching between optically deep and shallow algorithms during ocean color processing.

Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters

Remote sensing based approaches have been widely used over the years to monitor and manage submerged aquatic vegetation (SAV) or aquatic macrophytes mainly by mapping their spatial distribution and at the most, modeling SAV biomass. Remote sensing based studies to map SAV heights are rare because of the complexities in modeling water column optical proprieties. SAV height is a proxy for biomass and can be used to estimate plant volume when combined with percent cover. The objective of this study was to explore the feasibility of a satellite sensor to estimate the SAV height distribution in an inland reservoir. Also to test different radiative transfer theory based bio-optical models for estimating SAV heights using SPOT-6 data. The satellite-based multispectral data have rarely been used and SPOT-6 data, to the best of our knowledge, have never been used to estimate SAV heights in inland water bodies. In addition to depth and hydroacoustic data, in situ hyperspectral radiance and irradiance were measured at different depths to compute remote sensing reflectance (Rrs) and the attenuation coefficients (Kd and KLu). Two models, Palandro et al. (2008) and Dierssen et al. (2003), were used to derive bottom reflectance from both in situ and atmospherically corrected SPOT-6 Rrs. Bottom reflectance-based vegetation indices (green-red index, slope index, and simple ratio) were used to estimate SAV heights. Validation was performed using echosounder acquired hydroacoustic data. In situ model calibration produced an R2 of 0.7, however, the validation showed a systematic underestimation of SAV heights and high Root Mean Square Error (RMSE); indicating that there is a greater sensitivity in in situ models to localized variations in water column optical properties. The model based on SPOT-6 data presented higher accuracy, with R2 of 0.54 and RMSE of 0.29 m (NRMSE = 15%). Although the models showed a decreased sensitivity for SAVs at depths greater than 5 m with a height of 1.5 m, the finding nonetheless is significant because it proves that re-calibration of existing bottom reflectance models with more field data can enhance the accuracy to be able to periodically map SAV heights and biomass in inland waters. Although the initial results presented in this study are encouraging, further calibration of the model is required across different species, seasons, sites, and turbidity regime in order to test its application potential.

An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters

The Operational Land Imager (OLI) onboard Landsat-8 satellite can provide remote sensing reflectance (Rrs) of aquatic environments with high spatial resolution (30 m), allowing for benthic habitat mapping and monitoring of bathymetry and water column optical properties. To facilitate these applications, accurate sensor-derived Rrs is required. In this study, we assess atmospheric correction schemes, including NASA's NIR-SWIR approach, Acolite's NIR and SWIR approaches and the cloud-shadow approach. We provide the first comprehensive evaluation for Landsat-8 Rrs retrievals in optically shallow coral reefs, along with an investigation of Landsat-8 Rrs products in a temperate turbid embayment. The obtained Landsat-8 Rrs data products are evaluated with concurrent in situ hyperspectral Rrs measurements. Our analyses show that the NASA and the cloud-shadow approaches generated reliable Rrs products across shallow coral reefs and optically deep waters. This evaluation suggests that high quality Rrs products are achievable from the Landsat-8 satellite in optically shallow environments, which supports further application of Landsat-8 type measurements for coral reef studies.

Estimation of underwater visibility in coastal and inland waters using remote sensing data.

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z sd , namely, turbidity and length attenuation coefficient (1/(c+K d ), c=beam attenuation coefficient; K d =diffuse attenuation coefficient at 531nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R rs (443)+R rs (490))/(R rs (555)+R rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K d ) at 531nm, K d (490) is estimated as a function of reflectance ratio (R rs (670)/R rs (490)), which provides the bio-optical link between chlorophyll concentration and K d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z sd and in situ Z sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z sd and in situ Z sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z sd and in situ Z sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z sd products. The modeled and satellite-derived Z sd values were compared with measurement data which yielded RMSE=0.079, MRE=-0.016, and R 2 =0.95 for the modeled Z sd and RMSE=0.075, MRE=0.020, and R 2 =0.95 for the satellite-derived Z sd products.

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

AbstractRemote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi‐empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2–3 m in the clear‐flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16–0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29–0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies. Copyright © 2015 John Wiley & Sons, Ltd.

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae.

Comparison of spaceborne measurements of sea surface salinity and colored detrital matter in the Amazon plume

AbstractLarge rivers are key hydrologic components in oceanography, particularly regarding air‐sea and land‐sea exchanges and biogeochemistry. We enter now in a new era of Sea Surface Salinity (SSS) observing system from Space with the recent launches of the ESA Soil Moisture and Ocean Salinity (SMOS) and the NASA Aquarius/Sac‐D missions. With these new sensors, we are now in an excellent position to revisit SSS and ocean color investigations in the tropical northwest Atlantic using multiyear remote sensing time series and concurrent in situ observations. The Amazon is the world's largest river in terms of discharge. In its plume, SSS and upper water column optical properties such as the absorption coefficient of colored detrital matter (acdm) are strongly negatively correlated (<−0.7). Local quasi‐linear relationships between SSS and acdm are derived for these plume waters over the period of 2010–2013 using new spaceborne SSS and ocean color measurements. Results allow unprecedented spatial and temporal resolution of this coupling. These relationships are then used to estimate SSS in the Amazon plume based on ocean color satellite data. This new product is validated against SMOS and in situ data and compared with previously developed SSS retrieval models. We demonstrate the potential to estimate tropical Atlantic SSS for the extended period from 1998 to 2010, prior to spaceborne SSS data collection.

Mapping gravel bed river bathymetry from space

Understanding river form and behavior requires an efficient means of measuring channel morphology. This study evaluated the potential to map the bathymetry of two clear‐flowing, shallow (<3 m deep) gravel bed rivers <60 m wide from 2 m‐pixel WorldView2 satellite images. Direct measurements of water column optical properties were used to quantify constraints on depth retrieval. The smallest detectable change in depth was 0.01–0.04 m and the maximum detectable depth was 5 m in green bands but <2 m in the near‐infrared; lower sensor radiometric resolution yields less precise estimates over a smaller range. An algorithm for calibrating a band ratioXto field measurements of depthdproved effective when applied to spectra extracted from images (R2= 0.822 and 0.594 for the larger and smaller stream, respectively) or measured in the field (R2= 0.769 and 0.452). This procedure also identified optimal wavelength combinations, but different bands were selected for each site. Accuracy assessment of bathymetric maps produced using various calibration approaches and image types indicated that: 1) a lineardvs.Xrelation provided depth estimates nearly as accurate as a quadratic formulation; 2) panchromatic and pan‐sharpened multispectral images with smaller 0.5 m pixels did not yield more reliable depth estimates than the original images; and 3) depth retrieval was less reliable in pools due to saturation of the radiance signal. This investigation thus demonstrated the feasibility, as well as the limitations, of measuring the bathymetry of clear, shallow gravel bed rivers from space.

δ13C is a signature of light availability and photosynthesis in seagrass

We explored the role of light‐saturated (carbon‐limited) photosynthesis on δ13C of turtlegrass (Thalassia testudinum Banks ex Kö;nig) populations from the clear, blue waters of the Great Bahama Bank and the turbid, green waters of Florida Bay using field observations and radiative transfer models. Consistent with numerous previous observations, leaf δ13C decreased significantly with water depth in both regions. However the δ13C for Bahamas turtlegrass was 3‰ heavier than that for Florida Bay turtlegrass at equivalent depths, and broadband irradiance explained even less of the δ13C variations than depth. Instead, leaf δ13C showed a stronger relationship to the fraction of the day that photosynthesis of the intact plant canopy was carbon‐limited. When the Bahamas and Florida Bay δ13C values were related to the fraction of the day that photosynthesis was carbon‐limited, the variations in leaf δ13C observed for Florida and Bahamas populations collapsed into a single relationship that explained 65% of the variation in leaf δ13C. Consequently, turtlegrass from the Bahamas was isotopically heavier than Florida Bay populations growing at equivalent depths because they were more carbon‐limited ( = light‐saturated) for a larger fraction of the day. The ability to predict turtlegrass δ13C from the daily period of carbon‐limited photosynthesis provides a mechanistic link to fundamental relationships between light and photosynthesis that can transcend geographic differences in depth and water‐column optical properties, and may permit leaf δ13C to provide a robust indicator of recent photosynthetic performance and plant survival in response to changing environmental conditions.

Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

The Snake River is an essential feature of Grand Teton National Park, and this dynamic fluvial system maintains diverse habitats while actively shaping the landscape. The complex, ever-changing nature of the river make effective characterization difficult, however; traditional field methods are illsuited for this task. Remote sensing provides an appealing alternative that could facilitate resource management while providing novel insight on the controls of channel form and behavior. This study continued our ongoing assessment of the potential to measure the morphology and dynamics of large, complex rivers such as the Snake via remote sensing (Figure 1). More specifically, we acquired hyperspectral images and bathymetric LiDAR data in August 2012 and are now comparing the depth retrieval capabilities of these sensors; in situ observations of water column optical properties inform this analysis as well. In addition to bathymetry, we are investigating the feasibility of using these data to infer bottom reflectance and hence delineate various substrates, such as gravel and submerged aquatic vegetation. Another new aspect of our research focuses on estimating flow velocities from the hyperspectral images and high-resolution digital aerial photography acquired simultaneously. Extensive field measurements of velocity will help us develop this approach. Similarly, measurements of sediment grain size on exposed bar surfaces will be used to assess whether particle size can be inferred from the highresolution photography. Remotely sensed data also are being used to identify areas of erosion and deposition and hence quantify the sediment flux associated with changes in channel morphology. Additional hyperspectral and bathymetric LiDAR data will be acquired in 2013, along with field measurements of depth, velocity, and bottom type.

BOMBER: A tool for estimating water quality and bottom properties from remote sensing images

BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) is a software package for simultaneous retrieval of the optical properties of water column and bottom from remotely sensed imagery, which makes use of bio-optical models for optically deep and optically shallow waters. Several menus allow the user to choose the model type, to specify the input and output files, and to set all of the variables involved in the model parameterization and inversion. The optimization technique allows the user to retrieve the maps of chlorophyll concentration, suspended particulate matter concentration, coloured dissolved organic matter absorption and, in case of shallow waters, bottom depth and distributions of up to three different types of substrate, defined by the user according to their albedo. The software requires input image data that must be atmospherically corrected to remote sensing reflectance values. For both deep and shallow water models, a map of the relative error involved in the inversion procedure is also given. The tool was originally intended to estimate water quality in lakes; however thanks to its general design, it can be applied to any other aquatic environments (e.g., coastal zones, estuaries, lagoons) for which remote sensing reflectance values are known. BOMBER is fully programmed in IDL (Interactive Data Language) and uses IDL widgets as graphical user interface. It runs as an add-on tool for the ENVI+IDL image processing software and is available on request.