Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption

Clark, R. K.; Fay, Temple H.; Walker, Charles L.

doi:10.1364/ao.26.4036_1

Cited by 60 publications

(21 citation statements)

References 3 publications

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“…This method (fully documented by Lyzenga et al (2006) but originally proposed by others (Paredes and Spero 1983;Lyzenga 1985;Clark et al 1987)) consists of a regression using a physically based predictor of water depth. It uses a number of pixels with known (i.e.…”

Section: Introductionmentioning

confidence: 99%

Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery

Kanno¹,

Tanaka²,

Kurosawa³

et al. 2013

Marine Geodesy

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Multispectral satellite remote sensing can predict shallow-water depth distribution inexpensively and exhaustively, but it requires many in-situ measurements for calibration. To extend its feasibility, we improved and employed a recently developed technique, for the first time, to obtain a generalized predictor of depth. We used six WorldView-2 images and obtained a predictor that yielded a 0.648 m root-mean-square error against a dataset with a 5.544 m standard deviation of depth. The predictor can be used with as few as two pixels with known depth per image, or with no depth data whatsoever, if only relative depth is needed. (98 words) Subject termsBathymetry, multispectral, satellite remote sensing, coral reef 4 Main Text

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Section: Introductionmentioning

confidence: 99%

Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery

Kanno¹,

Tanaka²,

Kurosawa³

et al. 2013

Marine Geodesy

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“…The potential to estimate water column depth, constituents of the water column, and benthic cover types over large areas and/or in remote locations from remote sensing data have been discussed for over two decades, with significant attention paid to empirical approaches (Clark et al 1987;Lyzenga 1981;Philpot 1989) that may be location-or data-limited (Lee et al 2001;Stumpf 1987). Semi-analytical and radiative transfer-based forward models and semi-analytical (SA) or look-up table based spectrum matching (LUT) inverse-models have several advantages for mapping shallow water environments, presuming that a remote sensing image has already been corrected for atmospheric and air/water interface effects ): 1.…”

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confidence: 99%

Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments

Dekker

Phinn

Anstee

et al. 2011

Limnology & Ocean Methods

261

232

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Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accurately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inversion algorithms are available, there has been limited assessment of performance and no work has been published comparing their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational efficiencies of one empirical and five radiative-transfer-based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative-transfer-based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re-analysis of these same or additional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches.Optically shallow waters, those where the bottom is visible from the water surface and measurably influences the waterleaving radiance, include inland waters through to estuarine and tropical coral reef and temperate coastal ecosystems. Over

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“…By adding the atmospheric path radiance (La) to all BOA radiance terms, one gets the Top Of the Atmosphere (TOA) spectral radiances at the sensor LsB, Ls and Lsw in equation (3). 2K is an operational two-way diffuse attenuation coefficient, in units of m-1, equals to the down-welling and up-welling term together (2K = Kd + Ku) and Z is the actual depth of the seabed.…”

Section: Radiative Transfer Equationmentioning

confidence: 99%

4SM Method Tested in the Gulf of California Suggests Field Data are Not Needed to Derive Satellite Bathymetry

Favoretto¹,

Morel²,

A³

et al. 2017

Preprint

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Satellite derived bathymetry methods over coastal areas were born to deliver basic and useful information like bathymetry. However, the process is not straightforward, the main limitation being the need of field data. The Self-calibrated Spectral Supervised Shallow-water Modeler (4SM) method was tested to obtain coastal bathymetry without the use of any field data. Using LANDSAT-8 multispectral images from 2013 to 2016, a bathymetric time series was produced. Groundtruthed depths and an alternative method, Stumpf's Band Ratio Algorithm, were used to verify the results. Retrieved (4SM) vs groundtruthed depths scored an average r2 (0.90), and a low error (RMSE = 1.47 m). Also 4SM showed, over the whole time series, the same average accuracy of the control method (40%). Advantages, limitations and operability under complex atmosphere and water column conditions, and high and low-albedo bottom processing capabilities of 4SM are discussed. In conclusion, the findings suggest that 4SM is equally accurate as the commonly used Stumpf's method, the only difference being the independence of 4SM to previous field data, and the potential to deliver bottom spectral characteristics for further modelling. 4SM represents a significative advance in coastal remote sensing potential to obtain bathymetry and optical properties of the marine bottom.

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Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption

Cited by 60 publications

References 3 publications

Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery

Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery

Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments

4SM Method Tested in the Gulf of California Suggests Field Data are Not Needed to Derive Satellite Bathymetry

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