The Observation, Modeling, and Retrieval of Bio-Optical Properties for Coastal Waters of the Southern Chesapeake Bay

The primary purpose of this study was to develop an inverse method to retrieve the inherent optical properties (IOPs) and biogeochemical parameters (e.g. chlorophyll a concentration and salinity) appropriate to monitor the water quality and biogeochemical processes from remote sensing of the coastal waters in the southern Chesapeake Bay and coastal Mid-Atlantic Bight region (MAB) dominated by Case 2 waters. For this purpose, knowledge of the relationship between remote sensing reflectance (Rrs) and IOPs and the effect from bottom reflectance on Rrs, is required. A substantial investigation of IOPs has been conducted for the coastal waters of the southern Chesapeake Bay. Although phytoplankton are the dominant contributors to IOPs of oceanic Case 1 waters, colored dissolved organic matter (CDOM) derived from non-phytoplankton sources and sedimentary particles also play very important roles in coastal Case 2 waters. Strongly influenced by riverine discharge, the shallow coastal waters of the southern Chesapeake Bay provide challenges and opportunities to develop regionally specific IOP retrieval methods from remotely sensed ocean color imagery. A semi-analytical radiative transfer model (PZ06_Ed), based on the analysis of the simulated results of an exact radiative transfer model, Hydrolight® [Mobley, 1994], was developed to estimate the vertical distribution of downwelling plane irradiance [Ed(z)] from IOPs and sky conditions (e.g. cloud coverage and solar zenith angle). Compared to the significant overestimation of the simple Gordon [1989] model for particle-rich environments, PZ06_Ed agreed with Hydrolight® with \u3c 6% of the root-mean-square (RMS) error. Field observations from the coastal waters of the southern Chesapeake Bay validated the predictions of PZ06_ Edwith RMS error from 10% to 14%. The SeaWiFS imagery of the diffuse attenuation coefficient (Kd) estimated from PZ06_Ed is significantly improved from the Mueller [2000] model and displays obviously the coastal processes in the lower MAB, including the riverine outflow from the Chesapeake Bay and the mixing of the Gulf Stream with the local waters. The quadratic model (e.g. GSMO1) describing Rrs and IOPs has been widely used in bio-optics to retrieve inherent optical properties (IOPs). In this study, the derived coefficients (l1 and l2) by Gordon et al. [1988] were re-evaluated from Hydrolight® simulations and incorporated into a semi-analytical radiative transfer model (PZ06_ Rrs) that included bottom effects for optically shallow waters. Compared with Hydrolight® simulations and field observations in the Chesapeake Light Tower (CLT), Rrs calculated from PZ06_Rrs typically agreed within 5% and about 7% to 13% of RMS, respectively. Hydrolight ® simulations and field observations also confirmed that PZ06_Rrs improved the retrieval of biogeochemical-related parameters, including [Chl], adg(443), and bbp(443), compared to global ocean color algorithms (e.g. OC3M) and semi-analytic models without considering the bottom effects (e.g. GSM01-CLT). Finally, the relatively successful inverse modeling provides a promising method to study ecosystem-level biogeochemical and physical parameters from remote sensing for coastal waters of southern Chesapeake Bay and even lower MAB

The Empirical Models to Correct Water Column Effects for Optically Shallow Water

Seagrass as one of the blue carbon sinks plays an important role in environment, and it can be tracked remotely in the optically shallow water. Usually the signals of seagrass are weak which can be confused with the water column. The chapter will offer a model to simulate the propagation of light. The model can be used to improve the accuracy of seagrass mapping. Based on the in situ data, we found that the appropriate wavebands for seagrass mapping generally lie between 500–630 nm and 680–710 nm as well. In addition, a strong relationship between the reflectance value at 715 nm and LAI was found with a correlation coefficient of 0.99. The chapter provided an improved algorithm to retrieve bottom reflectance and map the bottom types. That would be meaningful for management and preservation of coastal marine resources

Estimation of CDOM in Inland Waters via Water Bio-Optical Properties Using a Remote Sensing Approach

Monitoring of Colored dissolved organic matter (CDOM) in inland waters provides important information for tracing carbon cycle at the land-water interface and studying aquatic ecosystem. Remote sensing estimation of CDOM in the inland waters offers an alternative approach to field samplings in examining CDOM spatial-temporal dynamics. However, CDOM retrieval is a challenge due to the lack of algorithm for resolving bottom effect in shallow inland waters. Moreover, an effective approach based on multi-spectral, high spatial resolution and global coverage satellite images is in urgent need. To resolve these challenges, shallow water bio-optical properties (SBOP) algorithm was developed to overcome bottom reflectance effect on the total water leaving reflectance in shallow inland water. SBOP algorithm included the bottom reflectance in building underwater light transfer model. It was designed based on the field spectral data from four cruises in Lake Huron. SBOP algorithm had an obviously advantage over previous deep water CDOM algorithm (e.g. QAA-CDOM). In this study, Landsat-8 multi-spectral satellite imagery was selected to derive CDOM spatial-temporal dynamics in lake and river waters. The coastal blue band (443 nm), global coverage and high spatial resolution (30 m) of Landsat-8 images offered suitable data for inland water CDOM mapping. The SBOP algorithm was applied on Landsat-8 images in broad ranges of inland waters with high accuracy (Lake Huron (R2 = 0.87), 14 northeastern freshwater lakes (R2 = 0.80), and 6 large Arctic Rivers (R2 = 0.87)). Both the spatial patterns and seasonal dynamics were derived to study the multiple factors’ impact on terrestrially derived CDOM input to the rivers and lakes, including river discharge, watershed landcover, and temperature. This new satellite approach of CDOM estimation in inland waters provided high accuracy spatial-temporal information for studying land-water carbon cycle and aquatic environment

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectancesthat can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namelythe ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a watermass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and itsdissolved and particulate constituents. Because of their dependence on the concentration and composition ofmarine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This informationis critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbonproduction and export, phytoplankton dynamics, and responses to climatic disturbances. Given their im-portance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products intothe community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., theglobal, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mis-sion), we present a synopsis of the current state of the art in the retrieval of these core optical properties.Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separatedbased their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated witheach approach are provided, as well as common performance metrics used to evaluate them. We discuss currentknowledge gaps and make recommendations for future investment for upcoming missions whose instrumentcharacteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches

Estimating the water quality condition of river and lake water in the Midwestern United States from its spectral characteristics

This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. The second part of this study focuses on the calibration of an existing bio-geo-optical model for studying the spatial variability of chl, non-algal particles (NAP), and colored dissolved organic matter (CDOM) in episodic St. Joseph River plumes in southern Lake Michigan. One set of EO-1 Hyperion imagery and one set of boat-based spectrometer measurements were successfully acquired to capture episodic plume events. Coincident water quality measurements were also collected during these plume events. A database of inherent optical properties (IOPs) measurements and spectral signatures was generated and used to calibrate a bio-geo-optical model. Finally, a comprehensive spectral-biogeochemical database was developed for the Wabash River and its tributaries in Indiana by conducting field sampling of the rivers using a boat platform over different hydrologic conditions during summer 2014. In addition to the various spectral measurements taken by a handheld field spectrometer, this database includes corresponding in situ measurements of water quality parameters (chl, NAP, and CDOM), nutrients (TN, TP, dissolved organic carbon (DOC)), water-column IOPs, water depths, substrate types and bottom reflectance spectra. The temporal variability of water quality parameters and nutrients in the rivers was analyzed and studied. A look-up table (LUT) based spectrum matching methodology was applied to the collected observations in the database to simplify the retrieval of water quality parameters and make the data accessible to a wider range of end users. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chlorophyll absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination ( R2) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R2 = 0.75). In addition, we also found that reflectance within the NIR wavelengths (700–890 nm) all showed strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Field measured concentrations of NAP and CDOM at 67% of the sampled sites in the St Joseph River plume fall within one standard deviation of the retrieved means using the spectrometer measurements and the calibrated bio-geo-optical model. The percentage of sites within one standard deviation (88%) is higher for the estimation of chl concentrations. Despite the dynamic nature of the observed plume and the time lag during field sampling, 77% of sampled sites were found to have field measured chl and NAP concentrations falling within one standard deviation of the Hyperion derived values. The spatial maps of water quality parameters generated from the Hyperion image provided a synoptic view of water quality conditions. Analysis highlights that concentrations of NAP, chl, and CDOM were more than three times higher in conjunction with river outflow and inside the river plumes than in ambient water. It is concluded that the storm-initiated plume is a significant source of sediments, carbon and chl to Lake Michigan. The temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions, while no significant correlations existed between these parameters and streamflow for the Tippecanoe River, probably due to the two upstream reservoirs. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflows (CSO)) to a river, water temperature, and nutrients are important factors controlling instream concentrations of phytoplankton. The LUT retrieved chl and NAP concentrations were in good agreement with field measurements with slopes close to 1.0. The average estimation errors for NAP and chl were within 4.1% and 37.7%, respectively, of independently obtained lab measurements. The CDOM levels were not well estimated and the LUT retrievals for CDOM showed large variability, probably due to the small data range collected in this study and the insensitivity of remote sensing reflectance, Rrs, to CDOM change. (Abstract shortened by ProQuest.

Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-ShallowWater Using SeaWiFS

This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives