机载真实孔径雷达在中小尺度区域海洋遥感中的应用

详细介绍一个适用于中小尺度区域海洋遥感图像采集和科学研究的双极化X 波段机载真实孔径雷达。这个真实孔径雷达曾参加1996 年度、1997 年度和1999 年度美国海军实验室主持的切萨皮克湾( Chesapeake Bay) 淡水层实地考察实验。实验结果表明应用民用产品集成的廉价机载实孔径雷达可以成为研究中小型海洋现象, 如油层污染, 内波和海口淡水层及其边缘等的高性价比的有力遥感工具。文章还探讨了应用于海洋表面遥感的机载真实孔径雷达的系统设计, 最优参数和实践中的局限性。     

An educational interactive numerical model of the Chesapeake Bay

Scientists use sophisticated numerical models to study ocean circulation and other physical systems, but the complex nature of such simulation software generally make them inaccessible to non-expert users. In principle, however, numerical models represent an ideal teaching tool, allowing users to model the response of a complex system to changing conditions. We have designed an interactive simulation program that allows a casual user to control the forcing conditions applied to a numerical ocean circulation model using a graphical user interface, and to observe the results in real-time. This program is implemented using the Regional Ocean Modeling System (ROMS) applied to the Chesapeake Bay. Portions of ROMS were modified to facilitate user interaction, and the user interface and visualization capabilities represent new software development. The result is an interactive simulation of the Chesapeake Bay environment that allows a user to control wind speed and direction along with the rate of flow from the rivers that feed the bay. The simulation provides a variety of visualizations of the response of the system, including water height, velocity, and salinity across horizontal and vertical planes.     

Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay

Coastal waters (Case 2) are generally more optically complex than oceanic waters and contain much higher quantities of colored detrital matter (CDM, a combination of dissolved organic matter and detrital particulates) as well as suspended sediment. Exclusion of CDM in the retrieval can lead to an overestimation of chlorophyll a concentration (C). We present a validation of a Case 2 version of the coupled spectral optimization algorithm (SOA) for simultaneous atmospheric correction and water parameter retrieval using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color data. Modeling of water constituents uses the Garver, Siegel and Maritorena (GSM) semi-analytic bio-optical model locally tuned for Chesapeake Bay. This includes a parameterization for CDM through its absorption spectrum.SOA-retrieved C and CDM are compared with in situ measurements in Chesapeake Bay. Results are also compared with output from two alternate models 1) the standard algorithm (Std) and 2) the standard atmospheric correction combined with the locally tuned GSM model (StdGSM). The comparisons indicate that the SOA is a viable alternative to both given models in Chesapeake Bay. In contrast, StdGSM appears to require improvement before it can be considered for operational use in these waters. Perhaps the most important result is the high-quality of CDM retrievals with the SOA. They suggest that there is value added using the SOA method in Chesapeake waters, as the Std method does not retrieve CDM. In a companion paper we describe in detail the model implementation, and its accuracy and limitations when applied to the Chesapeake Bay.     

An integrated modelling system for management of the Patuxent River estuary and basin,Maryland, USA

The Patuxent River watershed is a heavily impacted basin (2290 km²) and estuarine tributary (120 km²) of the Chesapeake Bay, USA. To assist management of the basin, we are testing a coupled modelling system composed of a watershed model (HSPF), an estuarine circulation model (CH3D), and an estuarine water‐quality model (CE‐QUAL‐ICM). The modelling system is being tested to guide the development of Total Maximum Daily Loads (TMDLs), and therefore errors in the models must be carefully evaluated. A comparison of daily total nitrogen (TN) concentrations simulated in HSPF with observations indicated that there was no significant bias, with an rms error of 37%. In contrast, modelled total phosphorus (TP) and total suspended solids (TSS) had significant bias with larger rms errors (65% and 259%, respectively). In the estuary, CH3D accurately simulated tides, temperature, and salinity. CE‐QUAL‐ICM overestimated nitrogen (N) and phosphorus (P) in the upper estuary and underestimated in the lower estuary, primarily because intertidal marshes are not currently a model component. Model errors declined from short (1 day) to long (multi‐year) timescales as under‐ and overestimations cumulatively cancelled. Watershed model errors propagate into the estuarine models, interacting with each subsequent model's errors, which limits the effectiveness of this TMDL management tool at short timescales.     

Remote sensing of coastal discharge sites using SPOT-simulation data

Abstract

The use of SPOT-simulation data to identify and delineate the structure of effluent plumes in a tidal estuary is considered. Results are presented to demonstrate the capability of the data to locate the effluent discharged from short coastal outfalls and a long sea outfall, respectively, in the Firth of Forth. In addition, coastal water movements are revealed and the processed images are interpreted in terms of correlations between suspended sediment distributions and estuarine bathymetry variations. The relationship between the present results and previous studies at the same site are discussed.     

High-resolution mapping of river-hydrothermal water mixing: Yellowstone National Park

Thermal mixing in rivers is a common geophysical phenomenon that controls myriad processes, from aquatic ecological functions to stream and groundwater biogeochemistry. We present high-resolution remotely-sensed temperature distributions of thermal plumes discharging into rivers collected from Yellowstone National Park. Airborne (4 m pixel size) and ground-based (centimetre or better spatial resolution) images corroborate the presence of these mixing zones. They illustrate that thermal discharges in rivers may not be well-mixed with the bulk flow even after traversing distances corresponding to several stream widths. This allows for large thermal gradients (>30°C) to persist between the thermal discharge and the bulk flow. The plumes may have pronounced internal temperature gradients that vary in space and time. The images illustrate the potential of portable high-resolution sensors not only for acquiring observations needed for fundamental understanding of non-isothermal mixing processes but also for providing temperature distributions necessary for understanding many thermally-mediated processes.     

Using a three-dimensional particle-tracking model to estimate the residence time and age of water in a tidal estuary

A three-dimensional hydrodynamic model that includes a Lagrangian particle-tracking simulation was applied to the Danshuei River estuarine system in northern Taiwan. The model's accuracy was validated with data from 1999; the results from the model agreed well with empirical observations of water surface elevation, tidal currents, and salinity. The validated model was then used to investigate the residence time and water age in response to different levels of freshwater discharge. A regression analysis of the model results revealed that an exponential equation best explained the correlation between residence time and freshwater input. We found that the residence times during the low and high freshwater discharge episodes were 4.4 and 2.5 days, respectively. The water age during the low-flow periods was greater than that during the high-flow periods. The modelled residence time and water age values without density-induced circulation were higher than those with density-induced circulation, which indicates that density-induced estuarine circulation may play a significant role in the estuary.     

Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage

Accurately predicting tidal levels, including tidal and freshwater discharge effects, is important for human activities in estuaries. The traditional harmonic analysis method and numerical modeling are usually adopted to simulate and predict estuary water stages. This study applied artificial neural networks (ANNs) as an alternative modeling approach to simulate the water stage time-series of the Danshui River estuary in northern Taiwan. We compared this approach with vertical (laterally averaged) 2D and 3D hydrodynamic models. Five ANN models were constructed to simulate the water stage time-series at the Shizi Tou, Taipei Bridge, Rukuoyan, Xinhai Bridge, and Zhongzheng Bridge locations along the Danshui River estuary. ANN models can preserve nonlinear characteristics between input and output variables and are superior to physical-based hydrodynamic models during the training phase. The simulated results reveal that the vertical 2D and 3D hydrodynamic models could not capture the observed water stages during an input of high freshwater discharge from upstream boundaries, while the ANN could match the observed water stage. However, during the testing phase, the ANN approach was slightly inferior to the 2D and 3D models at the Xinhai Bridge, Zhongzheng Bridge, and Rukouyan locations. Our results show that the ANN was able to predict the water stage time-series with reasonable accuracy, suggesting that ANNs can be a valuable tool for estuarine management.     

AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China

The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10×10⁵ m³ year⁻¹ of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1×1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 °C. During the summer and fall months (May to November), there is a larger thermal plume extending 8-10 km south along the coast from DNPS, and the temperature change is about 1.0 °C. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume.     

Submarine groundwater discharge in tidal flats revealed by space-borne synthetic aperture radar

Most space-borne sensors cannot detect subsurface features. Groundwater is a typical subsurface feature, and its discharge to coastal ocean waters plays an important role in transporting terrestrial chemical constituents and providing habitats for various species of fauna and flora. This is the first paper to report observational evidence for submarine groundwater discharge (SGD) in tidal flats using space-borne synthetic aperture radar (SAR). Tidal flats are composed of high-moisture-saturated sediments and water puddles. These shallow water puddles were imaged effectively by using SAR systems. The presence of water puddles is usually indicated by low radar backscatter in SAR images due to specular reflections on the water surface. This effect was proved by comparing radar backscattering coefficients obtained from two space-borne SAR systems, TerraSAR-X and RADARSAT-2, with those obtained from two theoretical scattering models, IEM and Oh model. We observed relatively large, widely distributed water puddles in belt shape along the upper parts of the tidal flat, which were confirmed to be related to the discharge of groundwater. The results of this research suggest that SAR can be a powerful tool for observing and determining the areal distributions of possible groundwater discharge in large tidal flats, which is normally difficult to detect with traditional measurement tools or survey techniques for groundwater discharge. We firmly believe that this technique can reduce significantly the efforts of field work to confirm SGD in tidal flats.     

Modelling the uncertainties in predicting produced water concentrations in the North Sea

A random walk model has been used to compute concentration distributions of dispersed oil in the North Sea resulting from produced water discharges. This formed part of a joint study commissioned by Statoil, OLF and BP International with modelling being undertaken by SINTEF and the Brixham Environmental Laboratory. The model has been set up using predicted tidal currents from the Norwegian Meteorological Office 20-km grid three-dimensional Continental Shelf model for the year 1990. Climatology data from the North Sea have been used to define the variation of the thermocline depth at monthly intervals over the year, and wind data from the East Shetland Basin have been used to compute the vertical mixing rates. Peak concentrations of dispersed oil were predicted to be approximately 3 μg l⁻¹ in the East Shetland Basin, assuming no biodegradation; this value is consistent with the measurements of Stagg et al. (In: Reed, M., Johnsen, S. (Eds), Produced Water 2, Environmental Issues and Mitigation Technologies, Plenum Press, 1996), where the data were collected in the immediate vicinity of the discharges and the effects of degradation would be expected to be negligible.A matrix of model runs was undertaken with different parameter values for the degradation of dispersed oil, horizontal and vertical mixing, and the mixing across the thermocline. These results were used to estimate the sensitivity of the model and the uncertainty in the predicted concentration fields. The results indicate that for the areas of high concentration, a band approximately 150-km wide stretching up the centre of the North Sea, parameterisation of the vertical mixing is most important for predicting the concentration levels. For areas more remote from the discharges and closer to the land masses, the degradation rate of material has most effect on predicted concentrations.     

Remote sensing for the identification of coastal plumes: case studies of Delaware Bay

Two Delaware coastal plume events are studied using various remote sensing data. Satellite images obtained from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) are processed and enhanced to identify coastal plumes from Delaware Bay. Both visible band and infrared sensors give similar results in terms of the plume boundary. The gradient images of both types of sensors show the twin‐front structure near Cape Henlopen, which is also captured in a Synthetic Aperture Radar (SAR) image. Six consecutive SST images are used to track plume variability due to tidal forcing and the features of the plume front are found to be different between ebb and flood tides. Five consecutive SST images taken during a pronounced upwelling‐favourable wind event show the plume widened and separated offshore, while cooler water upwelled onshore of the separated plume.     

Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou,China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R _n), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning.     

Coupling and comparing a spatially- and temporally-detailed eutrophication model with an ecosystem network model: An initial application to Chesapeake Bay

Coastal waters are modeled for a variety of purposes including eutrophication remediation and fisheries management. Combining these two approaches provides insights which are not available from either approach independently. Coupling is confounded, however, by differences in model formulations and “currencies.” We present here an initial coupling of a spatially- and temporally-detailed eutrophication model, CE-QUAL-ICM, with a network fisheries model, Ecopath. We list commonalities between the models and present algorithms and software for the exchange of information. The models are applied to the central portion of Chesapeake Bay for a contemporary summer period. After comparison of the representations of Chesapeake Bay by the two models, an illustrative example one-way, off-line, coupling is presented. In an initial examination of a 20% increase in predation on phytoplankton by a small, highly-exploited fish (Atlantic menhaden, Brevoortia tyrannus), computed reduction in phytoplankton biomass is accompanied by increased production due to enhanced nutrient recycling. Minimal impact on the structure of the food web or on biomass of higher-trophic level organisms is computed. The algorithms and software can be adapted to alternate eutrophication models and Ecopath applications and provide the first, necessary, steps for subsequent coupling with the time-variable Ecosim model.     

Application of geospatial models to map potential Ruditapes philippinarum habitat using remote sensing and GIS

This study applied geographic information system (GIS)-based models to map the potential Ruditapes philippinarum (Korean littleneck clam) habitat area in the Geunso Bay tidal flat, Korea. Remote-sensing techniques were used to construct spatial datasets of ecological environments, and field observations were undertaken to determine the distribution of macrobenthos. The mapping of potential habitat was completed and eight controlling factors relating to the distribution of tidal macrobenthos were determined. These were the tidal flat digital elevation model, slope, aspect, tidal annual exposure duration, distance from tidal channels, tidal channel density, spectral reflectance of the near-infrared bands, and surface sedimentary types, which were all generated from satellite imagery. The spatial relationships between the distribution of R. philippinarum and each control factor were calculated using a frequency ratio, logistic regression, and artificial neural networks combined with GIS data. Individuals were randomly divided into a training set (50%) to analyse habitat potential using the frequency ratio, logistic regression, and artificial neural network models, and a test set (50%) to validate the predicted habitat potential map. The relationships were overlaid to produce a potential habitat map with a R. philippinarum habitat potential (RPHP) index value. These maps were validated by comparing them to surveyed habitat locations such as those in the validation data set. From the validation results, the frequency ratio model showed prediction accuracy of 82.88%, while the accuracy of the logistic regression and artificial neural networks models was 70.77% and 80.45%, respectively. Thus, the frequency ratio model provided a more accurate prediction than the other models. Our data demonstrate that frequency ratio, logistic regression, and artificial neural networks models based on GIS analysis are effective for generating potential habitat maps of R. philippinarum species in a tidal flat. The results of this study will be useful for conserving and managing the macrofauna of tidal flats.     

Spectral optimization for constituent retrieval in Case 2 waters I: Implementation and performance

We describe in detail the implementation of the spectral optimization algorithm (SOA) for Case 2 waters for processing of ocean color data. This algorithm uses aerosol models and a bio-optical reflectance model to provide the top-of atmosphere (TOA) reflectance. The parameters of both models are then determined by fitting the modeled TOA reflectance to that observed from space, using non-linear optimization. The algorithm will be incorporated into the SeaDAS software package as an optional processing switch of the Multi-Sensor Level-1 to Level-2 code. To provide potential users with an understanding of the accuracy and limitations of the algorithm, we generated a synthetic data set and tested the performance of the SOA with both correct and incorrect bio-optical model parameters. Application of the SOA to actual SeaWiFS data in the Lower Chesapeake Bay (for which surface measurements were available) showed that 20% errors in the bio-optical model parameters still enabled retrieval of chlorophyll a and the total absorption coefficient of dissolved plus particulate detrital material at 443 nm with an error of less than 30% and 20%, respectively. In a companion paper we present a validation study of the application of the algorithm in the Chesapeake Bay.     

Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay

The NASA Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODIS-Aqua) provides a viable data stream for operational water quality monitoring of Chesapeake Bay. Marine geophysical products from MODIS-Aqua depend on the efficacy of the atmospheric correction process, which can be problematic in coastal environments. The operational atmospheric correction algorithm for MODIS-Aqua requires an assumption of negligible near-infrared water-leaving radiance, nL_w(NIR). This assumption progressively degrades with increasing turbidity and, as such, methods exist to account for non-negligible nL_w(NIR) within the atmospheric correction process or to use alternate radiometric bands where the assumption is satisfied, such as those positioned within shortwave infrared (SWIR) region of the spectrum. We evaluated a decade-long time-series of nL_w(λ) from MODIS-Aqua in Chesapeake Bay derived using NIR and SWIR bands for atmospheric correction. Low signal-to-noise ratios (SNR) for the SWIR bands of MODIS-Aqua added noise errors to the derived radiances, which produced broad, flat frequency distributions of nL_w(λ) relative to those produced using the NIR bands. The SWIR approach produced an increased number of negative nL_w(λ) and decreased sample size relative to the NIR approach. Revised vicarious calibration and regional tuning of the scheme to switch between the NIR and SWIR approaches may improve retrievals in Chesapeake Bay, however, poor SNR values for the MODIS-Aqua SWIR bands remain the primary deficiency of the SWIR-based atmospheric correction approach.     

The application of remotely sensed data in the study of frontal systems in the Tay Estuary,Scotland, U.K.

The results of a remote sensing study of tidal mixing processes in the Tay estuary are correlated with the results of mathematical and physical models and ground sampling data in an attempt to assess the nature, the spatial and temporal extent, and the variability in the formation of convergent fronts in the Tay estuary. The application of remote sensing methods to the study of estuarine mixing processes in the Tay Estuary, supported by ground measurements, reveal convergent front mechanisms which had not previously been recognized in the Tay estuary. This integrated approach enables the various features and mixing mechanisms found in the lower section of the estuary to be related. The limitations of using a two-dimensional mathematical and a physical model to predict the complex mixing processes affecting water quality in the Tay Estuary are discussed. The widespread occurrence of convergent systems in the estuarine environment suggests that to obtain an accurate estuarine water quality model, the effect of fronts should be included. The limitations of remote sensing data, the problems of geometrically rectifying airborne imagery in the coastal zone, and two methods of correcting for atmospheric effects in the thermal infrared waveband are also discussed.     

A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation

The study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) red-channel reflectance with a spatial resolution of 250 m to estimate suspended sediment concentration (SSC) in the Mobile Bay estuary, Alabama. Based on monthly in situ sampling, a new algorithm was developed using an exponential regression model. The concentration of inorganic suspended sediments (ISS) in Mobile Bay and Mississippi Sound was concerned and mapped by applying the new algorithm. The ISS maps during a cold front passage have revealed how the resuspension and transport of sediments respond to the variable wind forcing in this micro-tidal system. Particle tracking based on a three-dimensional hydrodynamic model was utilized to explain what was observed from the satellite imagery. It has been found that the rapid disappearance of the surface ISS after a cold front passage was mainly caused by settling of sediments rather than flushing out of the estuary. The study demonstrates that a combination of ISS mapped from the MODIS band-1 reflectance and three-dimensional numerical modelling is an effective tool to analyse sediment dynamics in the Mobile Bay estuary and other similar estuaries.     

Stable isotopes reduce parameter uncertainty of an estuarine carbon cycling model

Quantifying estuarine carbon cycling is complex due to the highly-variable environmental conditions associated with the interaction between tides, riverine inflows, meteorological forcing and internal biogeochemical processes. A Markov-Chain Monte Carlo algorithm was utilized to perform unbiased calibration of parameters used by a 1-D isotope-enabled carbon model applied to stable isotope data collected in Caboolture River Estuary, Australia. The parameter posteriors were ported into a 3-D finite-volume isotope-enabled carbon model and run over a range of hydro-meteorological conditions that occurred during a 1.5-year simulation period. The model highlighted the spatio-temporal variations and uncertainties associated with carbon cycling within the estuary, including the shift from being strongly heterotrophic in the upper estuary with a higher water-atmosphere flux of CO_2, to a more balanced trophic state in the lower estuary. The approach demonstrates the usefulness of isotope data to constrain model uncertainty and advances our ability to undertake carbon budgeting in coastal environments.