Abstract The penetration depth of light in the sea is defined for remote sensing purposes as the depth above which 90% of the diffusely reflected irradiance (excluding specular reflectance) originates. It is demonstrated that for a homogeneous ocean, this is the depth at which the downwelling in-water irradiance falls to 1/e of its value at the surface. Penetration depths as a function of wavelength are presented for a variety of water types, and a mean penetration depth z (90) for a broadband sensor is defined and applied to the MSS on ERTS-1. The maximum z (90) expected for ERTS-l is found to be somewhat less than 20 m.

根据1996-2002年无锡太湖监测站的水质资料分析,太湖悬浮物具有季节性特征,因而分季节的悬浮物估测模型比单一的模型可能更加适合用来估测太湖全年的悬浮物浓度．在分析太湖水体光谱特征的基础上,根据太湖悬浮物的季节性分布特征,使用春夏秋冬四季的Landsat TM/ETM图像和准同步的水质采样数据,建立了太湖分季节的悬浮物估算模型．结果表明:估测因子(B2+B3)/(B2/B3)在舂、秋、冬三季都能很好地估测出悬浮物的浓度(R~3＞0．52)．夏季由于叶绿素的干扰性较大,悬浮物的估测效果不理想．冬季的估测效果最好(R~2=0．81),模型为lnSS=14．656×(B2+B3)/ (B2/B3)

78 A new remote sensing algorithm for total suspended matter (TSM) is developed. 78 This algorithm combines a semi-analytical approach with empirical approaches. 78 The algorithm is based on a complex proxy from four indices of reflectance. 78 The performance is evaluated using a large number of in situ measurements. 78 The algorithm is suitable for coastal waters with a wide range of TSM.

78 An atmospheric correction algorithm for turbid waters was proposed for GOCI. 78 A regional TSM algorithm in Hangzhou Bay was developed for GOCI. 78 GOCI-derived TSM diurnal variations matched the buoy data well.

78 A new remote sensing algorithm for total suspended matter (TSM) is developed. 78 This algorithm combines a semi-analytical approach with empirical approaches. 78 The algorithm is based on a complex proxy from four indices of reflectance. 78 The performance is evaluated using a large number of in situ measurements. 78 The algorithm is suitable for coastal waters with a wide range of TSM.

This study investigates the use of single-band reflectance at visible wavelengths for the derivation of suspended sediment concentrations in the Irish Sea. A reasonably strong relationship was observed between irradiance reflectance at 665 nm ( R 665) and mineral suspended sediment (MSS) concentrations. Variability in the Reflectance SS relationship was found to be the consequence of changes in the mass-specific scattering coefficient ( b MSS*) brought about by differences in particle properties such as grain size and composition. A systematic increase in the slope of the Reflectance SS relationship was observed with increasing b MSS*. A reflectance model is presented that highlights the dependence of reflectance on b MSS* and suggests that the errors in predicted MSS concentrations can be reduced from 56% to as little as 12% with prior knowledge of the scattering properties of the sediments under study. This paper highlights the need for a complete understanding of the scattering properties of particles in order to accurately estimate MSS concentrations from reflectance measurements. It is suggested that in order to obtain quantitative estimates of MSS in moderately turbid waters from space, it may be necessary to pre-determine scattering efficiencies, b MSS*, for the area of interest.

A retrieval algorithm, of total suspended matter (TSM) concentration in the Yellow Sea (YS) and East China Sea (ECS) was developed using observations made in the 2003 Spring and Autumn cruises over the YS and the ECS. Analysis of the in-situ backscattering coefficients of the suspended particles ( b bp) indicates that the accuracy becomes worse when the concentration of TSM ( C TSM) is higher than 20 mg/l. The accuracy of the b bp is improved by using a bio-optical model in which b bp is optimized with a non-linear least-square Levenberg arquardt method. The remote sensing reflectance ( R rs) is obtained by means of the optimization. The optimized R rs for waters with C TSM higher than 20 mg/l, together with the measured R rs for waters with C TSM lower than 20 mg/l, are used to establish the relationships between R rs(748), R rs(869) and R rs(645), which are used in the iterative method for atmospheric correction. Two atmospheric correction algorithms are switched according to the water turbidity. The shortwave infrared wavelengths (SWIR) method is used for waters with high-turbidity, and the iterative method is used otherwise. Results of the atmospheric correction were then applied to the Tassan model modified in this paper to compute the C TSM. Comparison between the retrieval results from MODIS imagery and the in-situ measurements indicates that the algorithms described in this paper can provide a reliable estimation of the C TSM distributions in the YS and ECS.

The normalized peak area (NPA) of remote-sensing reflectance (R rs) in the near-infrared region was used to estimate the concentration of total suspended matter (C TSM) in coastal waters. A linear regression model between C TSM and S NPA (R 265=650.83) was established, where S NPA is the area encompassed by the reflectance curve and the straight line between wavelengths 768 and 840 nm where there is a maximum of R rs near 715 nm. In the Pearl River estuary of South China, this NPA model performed better than other single-band and multi-band regression models, with a root mean square error (RMSE) of 4.07 mg l–1. This model may be widely applied to in situ measurements of TSM.

We present here the results of chlorophyll-a (chl-a) concentration estimation using the red and near infrared (NIR) spectral bands of a Hyperspectral Imager for the Coastal Ocean (HICO) in productive turbid waters of the Azov Sea, Russia. During the data collection campaign in the summer of 2010 in Taganrog Bay and the Azov Sea, water samples were collected and concentrations of chl-a were measured analytically. The NIR-red models were tuned to optimize the spectral band selections and chl-a concentrations were retrieved from HICO data. The NIR-red three-band model with HICO-retrieved reflectances at wavelengths 684, 700, and 720 nm explained more than 85% of chl-a concentration variation in the range from 19.67 to 93.14 mg mand was able to estimate chl-a with root mean square error below 10 mg m. The results indicate the high potential of HICO data to estimate chl-a concentration in turbid productive (Case II) waters in real-time, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems.

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll- a (chl- a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl- a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl- a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl- a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.

The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the normalized difference chlorophyll index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in situ water sample analysis and above-water hyperspectral surface acquisition system (HyperSAS) remote sensing reflectance in Pensacola Bay (PB) and Choctawhatchee Bay (CB). NDCI algorithm calibrations and validations were completed on HICO data. Linear and best-fit (polynomial) calibrations performed strongly with R2 of 0.90 and 0.96, respectively. The best validation of NDCI resulted with an R2 of 0.74 and root-mean-square error (RMSE) of 1.64 脗碌g/L. A strong spatial correspondence was observed between NDCI and chl-a, with higher NDCI associated with higher chl-a and these areas were primarily located in the northern PB and eastern CB at the river mouths. NDCI could be effectively used as a qualitative chl-a monitoring tool with a reduced need for site-specific calibration.

This letter focuses on water-quality estimation in the northern Adriatic Sea using physically-based methods applied to image obtained with the Hyperspectral Imager for the Coastal Ocean (HICO64). Optical properties of atmosphere and water were synchronously measured to parameterise such methods. HICO64-derived maps of chlorophyll-a (chl-a) and suspended particulate matter (SPM) indicated low values, in the range of 0–302mg02m613 and 0–402g02m613, respectively, correlating significantly with field data (R202=020.71 for chl-a and R202=020.85 for SPM). The results, on analysis, identify clear waters in the open sea and moderately turbid waters near the coast due to river sediment discharge and organic matter from coastal lagoons. These findings support the use of HICO64 data to assess water-quality parameters in coastal zones and suggest the feasibility of integrating them with future-generation space-borne hyperspectral images.

The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring or existing ocean colour satellites. HICO, an Office of Naval Research-sponsored programme, is the first space-based maritime hyperspectral imaging instrument designed specifically for the coastal ocean. HICO has been operating since September 2009 from the Japanese Experiment Module – Exposed Facility on the International Space Station (ISS). The high pixel resolution (approximately 9502m at nadir) and hyperspectral imaging capability offer a unique opportunity for characterizing a wide range of water colour constituents that could be used to assess environmental condition. In this study, we transform atmospherically corrected ISS/HICO hyperspectral imagery and derive environmental response variables routinely used for evaluating the environmental condition of coastal ecosystem resources. Using atmospherically corrected HICO imagery and a comprehensive field validation programme, three regionally specific algorithms were developed to estimate basic water-quality properties traditionally measured by monitoring agencies. Results indicated that a three-band chlorophyll a algorithm performed best (R202=020.62) when compared with in situ measurement data collected 2–402hours of HICO acquisitions. Coloured dissolved organic matter (CDOM) (R202=020.93) and turbidity (R202=020.67) were also highly correlated. The distributions of these water-quality indicators were mapped for four estuaries along the northwest coast of Florida from April 2010 to May 2012. However, before the HICO sensor can be transitioned from proof-of-concept to operational status and its data applied to benefit decisions made by coastal managers, problems with vicarious calibration of the sensor need to be resolved and standardized protocols are required for atmospheric correction. Ideally, the sensor should be placed on a polar orbiting platform for greater spatial and temporal coverage as well as for image synchronization with field validation efforts.

61We investigate challenges faced in temporal analysis of HICO derived bathymetry.61Optimized shallow water inversion model yields precise retrievals in bathymetry.61Retrieved bathymetry can detect temporal changes in depth of less than 1m.61HICO geolocation requires sub-pixel accuracy before use in time series analysis.61New post-processing techniques to image enhancements and tide correction are given.

A new UV submersible spectroradiometer has been employed to determine the diffuse attenuation coefficient for irradiance in the clearest natural waters [K(w)(lambda)] with emphasis on the spectral region from 300 to 400 nm. K(w)(lambda) can be related to the inherent optical properties of pure water, in particular the total absorption coefficient a(w)(lambda) and the molecular scattering coefficient b(m)(lambda), by means of equations derived from radiative transfer theory. We present an analysis showing that limiting values of K(w)(lambda) can be estimated from a(w)(lambda) and vice versa. Published a(w)(lambda) data, which show discrepancies much larger than their estimated accuracies, are briefly reviewed and then compared, via our analysis, with K(w)(lambda) data (our own new and previously published data as well as relevant data of others). This comparative analysis and new data allow a consistent and accurate set of optical properties for the clearest natural waters and for pure fresh water and saltwater to be estimated from 300 to 800 nm.

We study the use of airborne and simulated satellite remote sensing data for classification of three water quality variables: Secchi depth, turbidity, and chlorophyll a. An extensive airborne spectrometer and ground truth data set obtained in four lake water quality measurement campaigns in southern Finland during 1996 1998 was used in the analysis. The class limits for the water quality variables were obtained from two operational classification standards. When remote sensing data is used, a combination of them proved to be the most suitable. The feasibility of the system for operational use was tested by training and testing the retrieval algorithms with separate data sets. In this case, the classification accuracy is 90% for three Secchi depth classes, 79% for five turbidity classes, and 78% for five chlorophyll a classes. When Airborne Imaging Spectrometer for Applications (AISA) data was spectrally averaged corresponding to Envisat Medium Resolution Imaging Spectrometer (MERIS) channels, the classification accuracy was about the same as in the case of the original AISA channels.