Imaging Spectrometry of Water

Dekker, A.G.; Brando, Vittorio; Anstee, Janet; Pinnel, Nicole; Kutser, Tiit; Hoogenboom, Erin J.; Peters, Steef; Pasterkamp, R.; Vos, R.J.; Olbert, C.; Malthus, Tim J.

doi:10.1007/978-0-306-47578-8_11

Cited by 64 publications

(36 citation statements)

References 90 publications

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“…These bands are often highly correlated, thus offering limited information per band. Additional bands in the visible spectrum enhances the ability to retrieve bathymetry, benthic, and water quality characteristics [19,[22][23][24][25][26][27].…”

Section: Factors Affecting Image Qualitymentioning

confidence: 99%

“…This may lead to the signal from the target being saturated. Optimal sun-sensor-target geometry over water includes solar zenith angles of 30t o 60˝and sensor azimuth angles of 0˝or 180˝with respect to the solar azimuth [27].…”

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confidence: 99%

“…Wind-induced waves roughen the water surface, which then acts as a non-Lambertian reflector, causing significant forward scattering. A rough surface generally has the effect of widening the solid angle through which light will penetrate the water column by rapidly changing the direction in which light rays are refracted at the sea surface [27]. Additionally, wave-induced sunglint broadens the reflectance cone of the specular point [34], increasing the likelihood of sunglint contaminating the image.…”

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confidence: 99%

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Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

2016

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Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis.

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Section: Factors Affecting Image Qualitymentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

2016

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“…5, the constant f was assumed to be 0.33 (Morel and Prieur 1977) and the constant Q that relates upwelling radiance to irradiance was assumed to be equal to π. b b is the total backscattering coefficient (m −1 ), which is a function of b w (m −1 ), the scattering coefficient for water, b* SPM (m 2 g −1 ), the specific scattering for SPM, and B, the backscattering to scattering ratio. B was assumed to be 0.03 (Brando et al 2008;Oubelkheir et al 2006;Dekker et al 2001;Loisel et al 2007). The total absorption coefficient (a) is a function of a w (m −1 ), the absorption of water, a* Chl (m 2 mg −1 ), the chlorophyll specific absorption coefficient for pigments, a* SPM (m 2 g −1 ), the specific absorption coefficient for particles, and a* CDOM , the absorption coefficient for CDOM normalised at 440 nm.…”

Section: Appendix 3-gordon Model and Iop/siop Equationsmentioning

confidence: 99%

Spectra of a shallow sea—unmixing for class identification and monitoring of coastal waters

et al. 2011

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Ocean colour-based monitoring of water masses is a promising alternative to monitoring concentrations in heterogeneous coastal seas. Fuzzy methods, such as spectral unmixing, are especially well suited for recognition of water masses from their remote sensing reflectances. However, such models have not yet been applied for water classification and monitoring. In this study, a fully constrained endmember model with simulated endmembers was developed for water class identification in the shallow Wadden Sea and adjacent German Bight. Its performance was examined on in situ measured reflectances and on MERIS satellite data. Water classification by means of unmixing reflectance spectra proved to be successful. When the endmember model was applied to MERIS data, it was able to visualise well-known spatial, tidal, seasonal, and wind-related variations in optical properties in the heterogeneous Wadden Sea. Analyses show that the method is insensitive to small changes in endmembers. Therefore, it can be applied in similar coastal areas. For use in open ocean situations or coastal or inland waters with other specific inherent optical properties, re-simulation of the endmember spectra with local optical properties is required. However, such an adaptation requires only a limited number of local in situ measurements.

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“…Very few papers, with the exception of large scale physical-biological oceanographic applications, published in scientific journals show a global uptake of the science and techniques of aquatic remote sensing into environmentally relevant monitoring and management applications. The majority of past reviews of remote sensing for coastal applications have focused on two areas: (1) improvements to technology, techniques and applications, with limited assessment of accuracy and true costs [1,3,[5][6][7][8][9][10][11] and (2) understanding light interactions in shallow water environments [6,12]. Both of these activities were essential to underpin the use of remote sensing for monitoring and managing coastal resources.…”

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Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Brando

Phinn

2007

J. Appl. Remote Sens

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In all regions of the world, coastal aquatic environments are under increasing levels of environmental stress, due to the large number of people living immediately adjacent to them and using them as a resource. Information on the composition and condition of these water bodies and their benthic environments is a critical piece of knowledge infrastructure for development and application of coastal management practices [1][2][3]. Due to the large and dynamic nature of coastal aquatic environments, remote sensing offers a potential source for this information. However, if remote sensing is so useful and suitable data sets for mapping and monitoring coastal environments have been around since the Landsat era (1972) -why isn't it widely used in coastal resource management applications around the world?There are a number of potential answers to that question, related to suitability of technology, methods and the communication of true costs and capabilities of remote sensing based approaches. Bukata [4] offers the following reasons for the limited "operational" use of remote sensing for monitoring coastal environments:-Inadequate technology; -Inadequate science; -Inadequate or non-existent applications; -Inadequate communication of potential, results and products; -Lack of official standards for image products; -Cost of regular delivery is too high; -Satellite remote sensing is "suspect"; and -Society and/or organizational barriers exist. Very few papers, with the exception of large scale physical-biological oceanographic applications, published in scientific journals show a global uptake of the science and techniques of aquatic remote sensing into environmentally relevant monitoring and management applications. The majority of past reviews of remote sensing for coastal applications have focused on two areas: (1) improvements to technology, techniques and applications, with limited assessment of accuracy and true costs [1,3,[5][6][7][8][9][10][11] and (2) understanding light interactions in shallow water environments [6,12]. Both of these activities were essential to underpin the use of remote sensing for monitoring and managing coastal resources. We are now in a position where we can: collect suitable image data; apply robust models to transform the images to maps of biophysical parameters; collect field data to

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Imaging Spectrometry of Water

Cited by 64 publications

References 90 publications

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Spectra of a shallow sea—unmixing for class identification and monitoring of coastal waters

Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

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