Search

The Chinese Ocean Colour and Temperature Scanner (COCTS), Coastal Zone Imager (CZI) and the novel Ultra-Violet Imager (UVI) which on-board the Chinese Ocean Satellite HY-1C was launched in September 2018. The atmospheric correction of ocean color sensors was a critical step for accurate retrieval of the remote sensing reflectance, and the look-up-tables (LUTs), for the Rayleigh scattering, the aerosol scattering, and the diffuse transmittance, which were built bases on a Successive Order Scattering Vector Radiative Transfer Solver, played an important role in the processing step. Preliminary evaluation has been performed using the SeaWiFS LUTs and the MODIS data, it showed that COCTS can provide accurate ocean color products.

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

The diffuse attenuation of downwelling irradiance, Kd (m−1), is an important property related to light penetration and availability in aquatic ecosystems. The standard Kd(490) product (the diffuse attenuation coefficient at 490 nm) of the global oceans from satellite remote sensing has been produced with an empirical algorithm, which limits its reliability and applicability in coastal regions. More importantly, as an apparent optical property (AOP), Kd is a function of the angular distribution of the light field (e.g., solar zenith angle). The empirically derived product thus contains ambiguities when compared with in situ measurements as there is no specification regarding the corresponding solar zenith angle associated with this Kd(490) product. To overcome these shortcomings, we refined the Kd product with a product termed as the normalized diffuse attenuation coefficient (nKd, m−1), which is equivalent to the Kd in the absence of the atmosphere and with the sun at zenith. Models were developed to get nKd from both in situ measurements and ocean color remote sensing. Evaluations using field measurements indicated that the semi-analytically derived nKd product will not only remove the ambiguities when comparing Kd values of different light fields, but will also improve the quality of such a product, therefore maximizing the value offered by satellite ocean color remote sensing. This article is protected by copyright. All rights reserved.

In this paper, with a revised POLYMER (POLYnomial based approach applied to MERIS data) atmospheric correction model, we present a novel scheme (two-angle atmospheric correction algorithm, termed as TAACA) to remove atmospheric contributions in satellite ocean color measurements for coastal environments, especially when there are absorbing aerosols. TAACA essentially uses the same water properties as a constraint to determine oceanic and atmospheric properties simultaneously using two same-day consecutive satellite images having different sun-sensor geometries. The performance of TAACA is first evaluated with a synthetic dataset, where the retrieved remote-sensing reflectance (Rrs) by TAACA matches very well (the coefficient of determination (R2) ≥ 0.98) with the simulated Rrs for each wavelength, and the unbiased root mean square error (uRMSE) is ∼12.2% for cases of both non-absorbing and strongly absorbing aerosols. When this dataset is handled by POLYMER, for non-absorbing aerosol cases, the R2 and uRMSE values are ∼0.99 and ∼7.5%, respectively, but they are ∼0.92 and ∼39.5% for strongly absorbing aerosols. TAACA is further assessed using co-located VIIRS measurements for waters in Boston Harbor and Massachusetts Bay, and the retrieved Rrs from VIIRS agrees with in situ measurements within ∼27.3% at the visible wavelengths. By contrast, a traditional algorithm resulted in uRMSE as 3890.4% and 58.9% at 410 and 443 nm, respectively, for these measurements. The Rrs products derived from POLYMER also show large deviations from in situ measurements. It is envisioned that more reliable Rrs products in coastal waters could be obtained from satellite ocean color measurements with a scheme like TAACA, especially when there are strongly absorbing aerosols.

Radiative transfer modeling of Secchi disk observations has historically been based on conjugated signals of eye response and radiance, where water's attenuation in the entire visible band is included in the attenuation when deciding the Secchi disk depth in water. Aas et al. [Ocean Sci.10(2), 177 (2014)Remote Sens. Environ.169, 139 (2015)] hypothesized that it is actually the attenuation in water's transparent window that matters to the observation of a Secchi disk in water. To test this hypothesis, observations of Secchi disks in blue and green waters were conducted via naked eyes, blue-pass glasses, and green-pass glasses. Measurement results indicate that for blue waters, the observed Secchi depths via naked eyes match the depths obtained with blue-pass glasses and much deeper than the depths with green-pass glasses, although the green-pass glasses match the highest response of human eyes. These observations experimentally support the hypothesis that our eye-brain system uses the contrast information in the transparent window to make a judgement decision regarding sighting a Secchi disk in water.

University of Massachusetts Boston; NASA Ocean Biology and Biogeochemistry and Water and Energy Cycle Programs; JPSS VIIRS Ocean Color Cal/Val Project; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]

NASA; JPSS VIIRS; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]; CNRS; INSU

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (R rs ) and Secchi disk depth (Z SD ) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01–6.8 m−1) from R rs (λ) for the 350–560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly “exact” analytical inversion of an R rs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350–560 nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of Z SD places an extra constraint in the inversion of R rs , no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of Z SD and R rs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

In examining the dependence of the sea surface reflectance of skylight ρs on sky conditions, wind speed, solar zenith angle, and viewing geometry, Mobley [Appl. Opt.38, 7442 (1999).10.1364/AO.38.007442] assumed ρs is independent of wavelength. Lee et al. [Opt. Express18, 26313 (2010).10.1364/OE.18.026313] showed experimentally that ρs does vary spectrally due to the spectral difference of sky radiance coming from different directions, which was ignored in Mobley’s study. We simulated ρs from 350 nm to 1000 nm by explicitly accounting for spectral variations of skylight distribution and Fresnel reflectance. Furthermore, we separated sun glint from sky glint because of significant differences in magnitude, spectrum and polarization state between direct sun light and skylight light. The results confirm that spectral variation of ρs(λ) mainly arises from the spectral distribution of skylight and would vary from slightly blueish due to normal dispersion of the refractive index of water, to neutral and then to reddish with increasing wind speeds and decreasing solar zenith angles. Polarization moderately increases sky glint by 8 – 20% at 400 nm but only by 0 – 10% at 1000 nm. Sun glint is inherently reddish and becomes significant (>10% of sky glint) when the sun is at the zenith with moderate winds or when the sea is roughened (wind speeds > 10 m s-1) with solar zenith angles < 20°. We recommend a two-step procedure by first correcting the glint due to direct sun light, which is unpolarized, followed by removing the glint due to diffused and polarized skylight. The simulated ρs(λ) as a function of wind speeds, sun angles and aerosol concentrations for currently recommended sensor-sun geometry, i.e., zenith angle = 40° and azimuthal angle relative to the sun = 45°, is available upon request.

As a kind of eScience, remote sensing needs numerous computing resources to process data. A lack of computing resources restricts many scientists' work. The advent of cloud computing solves the problem perfectly for its low-cost and highly scalable computing power. This paper introduces the remote sensing algorithm platform running on Windows Azure. Windows Azure provides the relevant algorithm and efficient and extensive computing resources to solve large scale remote sensing image processing computations for myriad researchers. This platform applies the MapReduce model that constructs the parallel data processing module to organize and coordinate work flow among virtual machines. Efficiency tests show that by using the MapReduce model, the remote sensing algorithm platform efficiency in data processing has been dramatically improved. This paper relays the experience of using Windows Azure in eScience scenarios similar to remote sensing for reference in future research.

Inherent optical properties (IOPs), e.g., absorption, back scattering coefficients, and volume scattering function, are important parameters for radiance transfer simulation. Commercially available instruments (e.g., Wetlabs ACS, BB9, etc, and HOBILabs a-sphere, HS6, etc) basically only measure absorption and back scattering coefficients. In this paper, we used the same IOPs of International Ocean-Colour Coordinating Group (IOCCG) report 5 and Hydrolight to simulate the radiance distribution, however, different phase functions, say, a new phase function derived from the measured data by multispectral volume scattering meter (MVSM) in coastal waters, the widely used Petzold average phase function, and the Fournier-Forand (FF) phase function, were employed in the simulations. The simulation results were used to develop the retrieval algorithm with angular effects correction based on the quasi-analytical algorithm(QAA) developed by Lee et al.. Results showed that not only the back scattering probability, but also the angular shape of phase function are important for ocean color retrieval algorithm. Considering the importance of phase function in ocean color remote sensing, methods to validate the phase function data should be developed.

With the broadening and deepening of GIS applications, the GIS system is beginning to process a much larger amount of spatial data. Further analysis and processing of spatial data depend on data quality to a great extent, i.e. the accuracy, integrity and coherence of the data. Different methods have been developed for the purpose of controlling data quality. In this paper, a novel solution is proposed, which is a workflow-based system design for the online inspection of spatial data combined with the role-task based access control model and the versioning function of ArcSDE on a database. The design is charactered by precise workflow modeling of the online inspection of spatial data. There are two significant advantages about this design: a) A more secure access control mechanism in GIS workflow; b) A more effective solution to support long transactions of GIS workflow. This workflow-based system design has been successfully used in the active fault seismic data which have been collected from more than twenty cities in China and include different disciplines. It is charactered by stronger security and easier maintenance. Moreover, it can also be used for other kinds of online inspection of spatial data with its universal applicability.

Coastal areas of the China Seas are typical the so called case II waters or turbid waters, the retrieval and validation of the ocean color satellites' data in the China Seas have become one of the most difficult field in the ocean color remote sensing community. In this study, the semi-analytical (SA) algorithm for optically shallow water developed by Lee et al. is tuned for the China Seas using numerical simulation technique of the radiative transfer equation based on the field investigation data (China Joint Global Ocean Flux Study, JGOFS and some unpublished data) and literature of the China Seas. It is found that the tuned SA algorithm for the China Seas is better than that of Gordon et al. and Lee et al., the applicability of the tuned SA algorithm is improved in the China Sea. The percentage error between the retrieved chlorophyll concentration by the tuned SA algorithm for the China Seas and the in-situ data is about 28% using the field campaign data in the East China Sea in 1998.

In this study, a simulated-annealing algorithm is combined with well known semi-analytical model for deep water's inherent optical properties (IOPs) parameters. The IOPs is an important factor in considering and evaluating water type, subsurface light field, turbidity and pigment concentration. A simulated annealing (SA) algorithm is a global optimum process that could be applied for non-linear optimization problem in which multiparameter are present. In this work, four parameters are presented: Chl, ag440, S and bbp550. We evaluate our approach with self-computed data and IOCCG data set. The comparison between the retrieved results and the measured coefficients suggests that the differences are close to the accuracy limitations, and the two sets of results are in nice agreement, so the inversion results can be accepted.

During recent years, more and more efforts have been focused on developing new models based on ocean optics theory to retrieve water's bio-geo-chemical parameters or inherent optical properties (IOPs) from either ocean color imagery or in situ measurements. Basically, these models are sophisticated, and hard to invert directly, look up table (LUT) technique or optimization methods are employed to retrieve the unknown parameters, e.g., chlorophyll concentration, CDOM absorption, etc. Many researches prefer to use time-consuming global optimization methods, e.g., genetic or evolutionary algorithm, etc. In this study, different optimization methods, smooth nonlinear optimization (NLP), global optimization (GO), nonsmooth optimization (NSP), are compared based on the sophisticated hyper-spectral semianalytical (SA) algorithm developed by Lee et al., retrieval accuracy and performance are evaluated. It is found that retrieval accuracy don't have much difference, the performance difference, however, is much larger, NLP works very well for the SA model. For a given model, it is better to analyze the model is linear, nonlinear or nonsmooth category problem, sometimes, convex also need to be determined, or linearize some nonsmooth problem caused by if decision, then select the corresponding category optimization methods. Initial values selection is a big issue for optimization, the simple statistical models (e.g., OC2 or OC4) are used to retrieve the unknowns as initial values.

The purpose of this research is to study the effect of nonuniform vertical profiles of chlorophyll concentration on apparent optical properties with Radiative transfer model Hydrolight. The vertical profiles of chlorophyll concentration were approximated according to a Gaussian function(Lewis et al, 1983).The simulated AOPs for nonuniform chlorophyll profiles were compared with those for homogenous ocean whose chlorophyll concentration was identical to the background chlorophyll concentration of inhomogenous cases. The results reveal that the subsurface maximal chlorophyll concentration increase remote sensing reflectance in the blue wavelength and decrease it in the green wavelength, and nonuniform vertical profiles of chlorophyll concentration change the diffuse attenuation coefficient profiles and the angular structure of the light field in the seawater.

The East China Sea is a typical case 2 water environment, where concentrations of phytoplankton pigments, suspended matter, and chromophoric dissolved organic matter (CDOM) are all higher than those in the open oceans, because of the discharge from the Yangtze River and the Yellow River. By using a hyperspectral semianalytical model, we simulated a set of remote-sensing reflectance for a variety of chlorophyll, suspended matter, and CDOM concentrations. From this simulated data set, a new algorithm for the retrieval of chlorophyll concentration from remote-sensing reflectance is proposed. For this method, we took into account the 682-nm spectral channel in addition to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) channels. When this algorithm was applied to a field data set, the chlorophyll concentrations retrieved through the new algorithm were consistent with field measurements to within a small error of 18%, in contrast with that of 147% between the SeaWiFS ocean chlorophyll 2 algorithm and the in situ observation.

Ocean environment attracts more and more attention, whereas monitoring ocean environment using ocean color imagery (e.g., MODIS) has become one of the important fields in modern oceanography. After analyzing a few individual modules of the radiative transfer process, an end-to-end numerical model for ocean remote sensing is developed. This model combines MODTRAN and HYDROLIGHT, both are state-of-the-art radiative transfer models for atmosphere and water, respectively. Also, a simple but realistic cloud model is added to it. This combined model calculates the downward radiance on water surface using MODTRAN and the independent cloud model, which replaces the empirical and semi-empirical models used in RADTRAN (Gregg and Carder 1991). Especially, with information of cloud's location and brightness from by the cloud model, the combined model provides more accurate radiance on water surface. Further, the effects of cloud position (from 30 degree to 180 degree for the cloud central azimuth angle) and coverage (from about 10% to 80%) on retrieved chlorophyll concentration by standard MODIS algorithm is analyzed. It is found that the nearer the cloud to the Sun the smaller the effect on the derived chlorophyll.

In this study, measurements and models are used to test the closure between remote-sensing reflectance and IOPs. Measurements include those by AC9 (Wetlabs, Inc.) and HS6 (HOBI labs, Inc.), while models include both empirical models (e.g., Voss' beam attenuation coefficient model) and radiative transfer model (e.g., Hydrolight). It is found that, generally, AC9 works better than HS6 in providing scattering and backscattering coefficients. HS6 need more accurate calibration; absorption coefficients by AC9 are consistent with those by Spectrix or Spectrometer. Good linear relationship is found between AC9 measured beam attenuation coefficients (c) and the Voss model; while those measurements by HS6 needs some adjustments before feeding to HYDROLIGHT.

For many oceanic studies, it is changes horizontally with constituents in the water required to know the distribution of visible solar (1, 5), but also changes with depth for any water (6, radiation (EPAR) in the upper water column. 7). One way to reach this is by remote sensing. This To represent the steeper than exponential includes two components: First, EPAR at surface reduction of EpAR with depth, multiple exponential is calculated based on atmosphere properties terms (6, 7) were usually adopted, with an along with the position of the Sun. Second, the attenuation coefficient (or attenuation depth) vertical attenuation of EPAR (KPAR) is derived assigned for each term. These attenuation from products of ocean-color remote sensing. coefficients are kept vertically constant, but Currently, KPAR is estimated based on horizontally vary with Jerlov (5) water types. chlorophyll concentration ((C)) from ocean Recently, simple and explicit models have been color. This kind of approach works well for developed to incorporate satellite-derived waters where all optical properties can be chlorophyll concentrations ((C)) into the adequately described by values of (C), but will description of the attenuation of EpAR. When (C) result in large uncertainties for coastal waters values are provided via satellite observations of where (C) alone cannot accurately describe the ocean color (8, 9), the partition factors and optical properties. In this paper, we present an attenuation coefficients of the terms could be innovative model that describes KPAR as a calculated (4). function of water's inherent optical properties Such kind of approach works for Case-i (lOP).

[1] The diffuse attenuation coefficient for downwelling irradiance (Kd) is an important parameter for ocean studies. For the vast ocean the only feasible means to get fine-scale measurements of Kd is by ocean color remote sensing. At present, values of Kd from remote sensing are estimated using empirical algorithms. Such an approach is insufficient to provide an understanding regarding the variation of Kd and contains large uncertainties in the derived values. In this study a semianalytical model for Kd is developed based on the radiative transfer equation, with values of the model parameters derived from Hydrolight simulations using the averaged particle phase function. The model is further tested with data simulated using significantly different particle phase functions, and the modeled Kd are found matching Hydrolight Kd very well (∼2% average error and ∼12% maximum error). Such a model provides an improved interpretation about the variation of Kd and a basis to more accurately determine Kd (especially using data from remote sensing).

[1] For studies of heat transfer in the upper ocean, the vertical distribution of solar radiation (ESR) in the shortwave domain plays an important role. In earlier studies, a sum of multiple exponentials was used to describe the vertical transmittance of ESR. For those exponential terms, an attenuation coefficient for each term is assigned, and those attenuation coefficients are assumed to be vertically constant. Furthermore, those attenuation coefficients are empirically modeled as functions of chlorophyll concentration (Chl) to cope with varying water properties of the oceans. Since attenuation coefficients of ocean waters are generally determined by components more than Chl alone, we developed a generalized model bypassing the use of Chl on the basis of extensive numerical simulations. In this new model, vertical transmittance of ESR is separated into two exponential terms, with one for the contributions of wavelengths below 700 nm (the visible domain, EVIS) and one for wavelengths above 700 nm (the infrared domain, EIR). An attenuation coefficient is assigned for each of the two exponential terms. Unlike earlier models, these attenuation coefficients vary with depth as expected. Furthermore, the attenuation coefficient for the visible domain is modeled as a function of water's absorption and backscattering coefficients. Since absorption and backscattering coefficients of the world oceans can be adequately derived from observations of ocean color, the model we developed provides an effective and adequate means to describe the three-dimensional variation of both EVIS and ESR in the upper layer of the world oceans.

Using three different particle phase functions, subsurface remote-sensing reflectance (rrs) are simulated by Hydrolight, with the Sun positioned at 10deg, 30deg, and 60deg from zenith, respectively. The values of rrs at six angles are compared with that at nadir, so do the spectral ratios of rrs(440)/r rs(560). It is found that, generally, rrs values differ the most when the Sun is at 60deg and the sensor is partially facing the Sun. For all sun angles, observation angles, and particle phase functions in this study, however, the ratios of rrs(440)/rrs(560) only show limited variations. These results suggest that remote-sensing algorithms based on such kind of ratios are generally applicable to rrs observed in the remote-sensing domain

This research focuses on the study of crop scene visualization based on crop simulation and knowledge engineering techniques. Corn data are measured in the field of Luancheng, Mebei Province and Shunyi, Beijing City. In the study of 3D simulation of crop realistic structure, the object-oriented knowledge Database of crop realistic structure is in further development. The data structure of realistic scene used here can organize the whole visualization process well. With the appearance of the realistic structure model of the quantity remote sensing, the field of crop visualization study is more and more important. During the process of studying visualization, the object-oriented 3D visualization data model arises, which is used as the formation of the crop realistic structure. 3D visualization data model can be applied to the description of crop structure successfully by object-oriented design and confirming the geometrical and logical relation. In this paper, we have the description and computer simulation of the scene of vegetation as a main clue. We are working on the data organization by design principle of Objected-Oriented DBMS. We use the corn scene data from the experiments in Shunyi, Beijing City, 2001 as an example to build the remote sensing data model. Then, we produce the 3D model using L-system, and present 3D scene of summer corn on the computer to show the visualization

HYPERspectral Tethered Spectral Radiometer Buoy (Hyper-TSRB, Satlantic Inc.) has 123 channels from 400 nm to 800 nm to measure downwelling irradiance (Ed) and upwelling radiance (Lu ). The supplied software for Hyper-TSRB data processing (AKA, PROSOFT) is based on Case I and/or empirical algorithms. In this paper, a new semi-analytical method is proposed for Level 3 data processing. Basically, for open oceans case I waters, no large differences are found between the new method and PROSOFT (~10%); while for coastal case II waters, the new method is much better than PROSOFT in computed remote sensing reflectance (~10%-350%). The effect of phase function to the new method is also analyzed, it is shown that the new method can work stably for a wide phase function ranges

Remote-sensing reflectance (R(rs)), which is defined as the ratio of water-leaving radiance (L(w)) to downwelling irradiance just above the surface (E(d)(0⁺)), varies with both water constituents (including bottom properties of optically-shallow waters) and angular geometry. L(w) is commonly measured in the field or by satellite sensors at convenient angles, while E(d)(0⁺) can be measured in the field or estimated based on atmospheric properties. To isolate the variations of R(rs) (or L(w)) resulting from a change of water constituents, the angular effects of R(rs) (or L(w)) need to be removed. This is also a necessity for the calibration and validation of satellite ocean color measurements. To reach this objective, for optically-deep waters where bottom contribution is negligible, we present a system centered on water's inherent optical properties (IOPs). It can be used to derive IOPs from angular Rsubrs/suband offers an alternative to the system centered on the concentration of chlorophyll. This system is applicable to oceanic and coastal waters as well as to multiband and hyperspectral sensors. This IOP-centered system is applied to both numerically simulated data and in situ measurements to test and evaluate its performance. The good results obtained suggest that the system can be applied to angular R(rs) to retrieve IOPs and to remove the angular variation of R(rs).

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f'/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.

Secchi disk depth ( Z SD ) is a measure of water transparency, whose interpretation has wide applications from diver visibility to studies of climate change. This transparency has been explained in the past 60 + years with the underwater visibility theory, the branch of the general visibility theory for visual ranging in water. However, through a thorough review of the physical processes involved in visual ranging in water, we show that this theory may not exactly represent the sighting of a Secchi disk by a human eye. Further, we update the Law of Contrast Reduction, a key concept in visibility theory, and develop a new theoretical model to interpret Z SD . Unlike the classical model that relies strongly on the beam attenuation coefficient, the new model relies only on the diffuse attenuation coefficient at a wavelength corresponding to the maximum transparency for such interpretations. This model is subsequently validated using a large (N = 338) dataset of independent measurements covering oceanic, coastal, and lake waters, with results showing excellent agreement (~ 18% average absolute difference, R 2 = 0.96) between measured and theoretically predicted Z SD ranging from 30 m without regional tuning of any model parameters. This study provides a more generalized view of visual ranging, and the mechanistic model is expected to significantly improve the current capacity in monitoring water transparency of the global aquatic environments via satellite remote sensing.