Hydrological Sciences-Bulletin-des Sciences Hydrologiques, 25, 4, 12/1980

Satellite remote sensing of water turbidity

G E R A L D K . M O O R E US Geological Survey,

EROS Data Center, Sioux Falls, South Dakota 57198, USA

Received 12 February 1980, revised 21 May 1980

Abstract. Remote sensing instruments obtain an optical measure of water colour and turbidity. Colour increases the absorption of light in water and decreases the remotely sensed signal; turbidity increases the backscatter of light. For low concentrations of suspended materials, spectral reflectance is determined mostly by the absorptance characteristics of water; for higher concentrations, the absorptance character­istics of suspended particles are the most important factor. Remote sensing offers considerable advan­tages for the study of large areas, determination of current and circulation patterns, and monitoring of sedimentation, water productivity, and eutrophication.

Sonde de télémesure par satellite de la turbidité de l'eau

Résumé. Les instruments pour la sonde à distance obtiennent une mesure optique de la couleur et de la turbidité de l'eau. La couleur augmente l'absorption de la lumière dans l'eau et diminue le signal obtenu à distance; la turbidité augmente la rétrodiffusion de la lumière. Quand il s'agit des concentrations faibles de matière en suspension on détermine la réflectance spectrale le plus souvent des caractéristiques d'absorptance de l'eau; pour les concentrations plus hautes les caractéristiques d'absorptance des particules en suspension sont le facteur le plus important. La sonde à distance présente des avantages bien considérables quand il s'agit de l'étude des zones assez grandes, de la détermination des configurations des courants et de circulation et du contrôle de la sédmentation, de la productivité de l'eau et de l'eutrophication.

INTRODUCTION

Differences or changes in water appearance can be recorded by a camera or multispectral scanner. However, an assumption that water appearance is quantita­tively related to water quality may not be warranted. Many variables (Table 1) may affect the energy levels measured by satellite instruments. The many variables do not prevent useful interpretation of remote sensing data, and quantitative results are possible under some conditions.

The feasibility of measuring water colour* and turbidity from satellites can be assessed by considering light and water interaction processes and by evaluating the effects of atmospheric and hydrological variables. One purpose of this paper is to describe the principles of these interactions. Another purpose is to evaluate the water quality information in Landsat data. The third purpose of this paper is to

* A characteristic of water appearance, which results from dissolved substances; as opposed to turbidity, which results from colloidal or suspended substances.

0303-6936/80/1200-0407S02.00 © 1980 Blackwell Scientific Publications 407

408 G. K. Moore

TABLE 1. Variables that can affect remote sensing of physical water-quality characteristics

Variable Explanation

Time of year

Sun-elevation angle

Aerosol and molecular content of atmosphere

Water-vapour content of the atmosphere

Specular reflection of skylight from water surface

Roughness of water surface

Film, foam, debris, or floating plants on water surface

Water colour

The Earth receives 7 per cent more energy from the sun on 1 January than on 1 July because of an oval orbit.

More solar energy is specularly reflected from water surfaces at low sun-elevation angles than at high angles. Also,- the path length of solar energy through the atmosphere is longer at low sun-elevation angles, and more solar energy is absorbed and scattered.

These constituents determine the amount of solar energy absorbed and scattered by the atmosphere. Some energy, received by a satel­lite, is backscattered before reaching the water surface.

Water vapour affects energy absorption at near infrared and thermal infrared wavelengths.

Specularly reflected skylight is received by a satellite. The intensity and wavelength distribution of this energy depends on atmospheric scattering, which produces skylight.

A rough surface may produce more or less specular reflection than a smooth surface. At high sun-elevation angles, the area of sun glint may be within the satellite fields of view.

These features may not be resolved on a satellite image, but they contribute to the spectral characteristics of the measured signal.

Dissolved, coloured materials increase absorption of solar energy in

Water turbidity

Reflectance and absorptance characteristics of suspended particles

Multiple reflections and scatter­ing of solar energy in water

Depth of water and reflectance of bottom sediments Submerged or emergent vege­tation

The concentration, size, shape, and refractive index of suspended particles determine turbidity and increase the amount of energy backscattered in water bodies.

Particles may be inorganic sediments, phytoplankton, zooplankton, or a combination. When present in high concentrations, particles affect the spectral distribution of backscattered energy.

The spectral results of these processes are difficult to predict, but may not be important.

Water clarity determines the importance of bottom reflectance. Solar energy may not reach bottom in a turbid water.

Vegetation may change bottom reflectance, obscure water surface, or contribute to the spectral characteristics of the measured signal.

consider the present economics of obtaining water turbidity measurements by remote sensing.

PRINCIPLES AND APPLICATIONS

Water parameters that affect the energy levels detected by a camera or scanner are colour and turbidity. Differences in water colour and turbidity affect signals in the visible and very near ultraviolet and infra-red wavelengths. An increase in water colour decreases the energy flux reaching a sensor, because more of the sun's energy is absorbed in the water. An increase in turbidity increases the energy flux reaching a

Satellite remote sensing of water turbidity 409

sensor, because more solar energy is reflected or backscattered* by the particles that produce turbidity. An increased signal also occurs, however, in clear, shallow water where some solar energy is reflected from the bottom.

In order to use remote sensing to measure water colour or turbidity, it is important to understand the principles of light and water interaction. If the solar energy that reaches a water surface is represented by Iç,, the interaction is expressed by:

/O = /SR + /A + /B (1)

where /SR is the solar flux that is specularly reflected at the water surface (the mirror effect), /A is the flux absorbed by the water, and h is the flux backscattered to the water surface and thereby available for remote detection. Specular reflection is equal at all wavelengths, but absorption and backscatter produce distinctive spec­tral signatures.

Specular reflection from a water surface is called sun glint. Details on the water surface can be seen in areas of sun glint, but underwater details are obscured. The percentage of solar energy that is specularly reflected from calm water depends on sun-elevation angle (List, 1971):

Solar elevation angle Per cent reflection Horizon 100

5° 58 10° 35 20° 13 30° 6.0 40° 3.4 50° 2.1 90° 2.0

Sun glint is white light and changes the intensity of a remotely measured flux; the relative spectral signature probably would be only slightly affected. Contamination of remotely measured signals by sun glint has not been noted to be a problem in water turbidity studies, but corrections may be needed for quantitative turbidity calculations.

Some skylight (scattered light) is specularly reflected to a camera or multispectral scanner. The intensity of skylight, however, is typically less than 10 per cent of sunlight (Yost & Wenderoth, 1969, Fig. 22, for example), and reflected skylight typically constitutes less than 5 per cent of the remotely measured signal from water. The specular reflection of skylight generally can be ignored; corrections may be necessary under hazy conditions and for sun-elevation angles of less than 30°.

The solar energy that is not specularly reflected, is refracted downward at the water surface and begins to be affected by absorption and scattering. Energy absorbed by the water is converted to heat, and thus a water body is slowly warmed by the sun. Some wavelengths of light are absorbed more than others. In clear, deep water, most near infra-red light is absorbed within 0.2 m of the surface, and most red light is lost within the upper 2 m.

* Scattering is the process in which directions of light rays are changed. In water, scattering is caused by reflection and refraction of light rays at the surfaces of molecules and suspended particles. Most sunlight is scattered at small forward angles in natural waters and thus continues a downward path; some light is reflected upward and thus is backscattered.

410 G. K. Moore

The light remaining at any wavelength and any depth can be calculated from the equation:

I=h/eKX (2)

where Is is the flux that enters the water surface, Xis depth, and Kis the coefficient of extinction. The coefficient of extinction accounts for both absorption and scatter­ing, and its value varies with the wavelength of light (Dietrich, 1939, p. 411).

Scattering in perfectly clear water is caused by molecules and is strongly wave­length dependent. This is Rayleigh scattering and is similar to the process that produces a blue sky (about 10 times more blue light is scattered than red light).

In natural waters, most solar energy is scattered in a forward direction and eventually is absorbed by the water. About 2 per cent of the light flux is backscat-tered in a clear, infinitely deep water (Fig. 1). The part of the backscattered energy that returns to the water surface is the signal for remote sensing. In clear, deep water, 50 per cent of the signal for blue light (0.4-0.5 ^m) comes from a depth of less than about 15 m, but for red light (0.6-0.7 fan), most of the signal comes from a depth of less than about 1.1m.

Dissolved constituents that do not add colour to water have no effect on the absorption and scattering of light, as measured by a spectrophotometer or multi-spectral scanner. Thus, clear sea water has the same spectral signature as distilled

0.4 0.5 0.6 0.7 Wavelength, in micrometres

0.S 0.9 1.0

FIG. 1. Only 1-3 per cent of the solar energy that enters the surface of clear, infinitely deep water is backscattered to the surface. This part of the original flux is the signal for remote sensing.

Satellite remote sensing of water turbidity 411

water. Pollutants generally must affect the colour, turbidity, temperature, or micro­wave emissivity of water to be detectable by remote sensing.

Dissolved coloured materials increase light absorption in water but do not affect light scatter. Nevertheless, the spectral distribution of backscattered energy is affected by additional absorption. A brown water, for example, backscatters much less blue light and slightly less green and red light than a clear water. The average path length for light in a coloured water is the same as in clear water.

There has been little use of remote sensing to measure water colour, because inland waters are generally turbid, and it is difficult to separate the effects of colour and turbidity in remote sensing signatures. Differences in water colour have been used to detect discharge points of pollutants on aerial photographs and images.

Suspended particles increase total scatter, increase backscatter, change the spec­tral distribution of light, and reduce average path length. The most important results of these effects are: (1) a turbid water is more reflective than clear water (Fig. 2) at all visible and near infra-red wavelengths (a lighter tone on aerial photographs

îS&:i\.*r&--

FIG. 2. NASA Landsat images of the Louisiana coast on 5 May 1973. The very turbid water discharging from the Atchafalaya River has light grey tones on MSS band 4 (upper left) and band 5 (upper right), a medium grey tone on band 6 (lower left), and a dark grey tone on band 7. Scale is 1:3 369 000.

412 G. K. Moore

and satellite images) but (2) the remote signal from a turbid water represents only near-surface conditions, and (3) the measured signal at any wavelength interval is dependent on particle size as well as concentration and may be dependent on the absorption (reflection) and refraction characteristics of the suspended material.

Remote sensing can be used to obtain either qualitative or quantitative estimates of water turbidity. In a few cases, it is adequate simply to detect and delineate the area of turbidity. The relative turbidity shown on Landsat images can be estimated from Table 2; similar tables can be used to interpret tones and hues on aerial photographs. Such keys can be used to examine turbidity plumes in lakes and estuaries to determine which of several rivers is most turbid and where sediment is being deposited.

Quantitative remote sensing of water turbidity is possible under some conditions, but a thorough understanding of the effects of all variables (Table 1) is necessary to interpret the remote signal. It is important to understand that remote sensing results in an optical measure of water turbidity (and colour). Although some recent studies have shown a correlation of remotely measured fluxes with concentrations of suspended sediment or chlorophyll and carotenoid pigments, such relationships are secondary; suspended sediment and pigment concentrations may or may not corre­late with water turbidities. Similarly, any correlation of a remote signal with colourless chemical constituents must be related to the distribution of ions sorbed on suspended particles or constituents used in the growth processes of phytoplank-ton.

Suspended particles may occur in sizes that range from colloids to sand. Colloids do not occur in concentrations that are significant for sediment transport studies, so only their effects on the light flux need to be considered. Colloids produce Mie scattering because particle size is about equal to the wavelength of light. This is similar to the atmospheric process where the average scattering coefficient is about 1/A1-3 (A is wavelength): blue light is scattered somewhat more than red light. Colloids increase backscatter and may produce a distinctive spectral signature at high concentrations. The definition of a high concentration depends on grain size, because a fine-grained material contains more particles and produces more scatter than an equal weight of coarse-grained material. A high concentration of fine sand is more than 200 mg/1, but a high concentration of clay and colloids (in a dispersed condition) is 2 mg/1. Low to medium concentrations of any material (and any grain

TABLE 2. Qualitative estimate of relative turbidity of water from Landsat images

Tone of image Hue of Relative colour turbidity Band 4 Band 5 Band 6 Band 7 composite

None Dark Dark Black Black Black Slight Medium Dark Black Black Dark blue Moderate Light Medium Dark Black Medium blue Heavy Light Light Medium Dark Light blue Very heavy Light Light Light Medium White

Satellite remote sensing of water turbidity 413

size) result in a long average path length for light; the shape of the spectral reflectance curve is determined mostly by the absorptance characteristics of water.

Colloidal sized particles of sediments, plants, and animals commonly have more than a 2 mg/1 concentration. Extreme examples are so-called whitings in hard water marl lakes and some estuaries, where concentrations of dispersed and partly aggre­gated colloids are many times greater than those in the river (Stumm & Morgan, 1970). Colloids and other fine sediments may occur in stable suspension across the area of interest or may aggregate and settle out.

In mixed sizes of materials, colloidal particles contribute an amount of backscat-tered light flux that is out of proportion to their concentration. Thus, differences in colloidal content may be a problem in secondary correlations, such as optical turbidity with sediment content or plankton biomass. Also, during analysis of water samples, colloids may pass through the filter pads used to separate the larger particles.

Particles more than 10 times the size of a wavelength of light scatter all wave­lengths equally. This is the atmospheric process that produces white clouds. Low to medium concentrations of these particles increase backscatter but do not produce distinctive spectral signatures. High concentrations of large suspended particles produce a spectral signature that is characteristic of the particles. In this case, the average path length of light is short, and the light flux is strongly affected by the absorption and reflection characteristics of the particles.

The nature of the remotely sensed signal from a turbid water can be studied best by modelling, because few field situations provide a wide range in water turbidity. A crude model has been constructed by representing the suspended particles as a homogeneous monolayer, and by using expressions like Equation 2 to represent the absorption and backscatter of energy by water. Such a model does not account for the results of multiple reflections from the particles or the effects of variations in shape, absorptance, or refractive index.* Also, assumptions must be made about the amount of light entering the water surface, particle size, and the reflectance of the particles. Nevertheless, using assumptions that are believed reasonable, model results (Figs 3-7) provide some insight into the nature of data obtained by remote sensing instruments.

A plot of backscattered energy flux versus wavelength for various concentrations of suspended silt (Fig. 3) shows that total flux increases continuously with increas­ing concentrations of silt. Also, an increasing amount of light is backscattered at near infra-red wavelengths, and peak backscatter shifts to longer wavelengths. Note, however, that the shape of all curves is similar for low to medium concentra­tions of material. Water absorptance is dominant when the path length of light rays is relatively long.

An increase in particle size results in a decrease in reflective area. If everything else is equal, the curves in Fig. 3 can be used to represent fine sand (particle diameter of 0.2 mm) by multiplying the indicated concentrations by 10. Similarly for coarse clay (particle diameter of 0.002 mm), the concentrations shown in Fig. 3 can be divided by 10.

A plot of backscattered irradiance versus concentration of suspended silt for the

* The complex index of refraction of suspended particles affects relative forward and backscatter. Variations in shape and refractive index are believed to have only minor effects on curves predicted by the model.

414 G. K. Moore

0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

Wavelength, in micromètres

FIG. 3. Backscattered flux increases with concentrations of suspended silt (calculated for particle diameter of 0.02 mm). The shape of all curves is similar for low to medium concentrations because of the absorption characteristics of water. At high concentrations, curve shape is determined by absorptance characteristics of the suspended particles.

1 2 5 10 20 50 100 200 500 1000 Concentration of silt ld = 0.02 mm!, in m g / l

FIG. 4. The response of Landsat multispectral scanner to increasing concentrations of suspended silt can be estimated by a simple model. Note the exponential nature of the signal in all four bands. Radiance units are flux per unit solid angle.

four multispectral scanner (MSS) bands of Landsat (Fig. 4) shows the exponential character of remote signals. Two factors must be considered in evaluating this graph: sensitivity of a particular band to changes in turbidity (slope) and total available energy (a higher signal-to-noise ratio). Most recent studies have found that MSS band 5 digital values correlate best with turbidity measurements in inland lakes and reservoirs.

Satellite remote sensing of water turbidity 415

1 2 5 10 20 Concentration of silt (d=0.02 mm),

50 100 in mg/ l

200 500 1000

FIG. 5. Fifty per cent of the backscattered energy in a turbid water comes from less than the depths shown on this graph. For a concentration of 20 mg/l of suspended silt, for example, 50 per cent of the signal comes from a depth of less than 0.85 m. The concentration of silt that produces a detectable signal in Landsat MSS bands 5 and 6 is shown by arrows. The backscattered flux is detectable in MSS band 4 at all concentrations shown on the graph.

The depth of the hypothetical monolayer in the model represents twice the mean depth of a backscattered light flux (Fig. 5); if the particles are dispersed, 50 per cent of the signal would be from shallower depths, and the rest from deeper levels. The depth of the monolayer depends on particle size and concentration and is deter­mined by calculating the depth where cumulative particle area is equal to a unit area of water.

One of the major problems in calculating turbidity from a remotely detected flux is the difference in absorption and scattering of energy in the atmosphere from one time to another. Over clear, deep water, more than 95 per cent of the signal measured by Landsat (Fig. 6) originates as atmospheric scattering (solar energy backscattered in the atmosphere without reaching water). For turbid water, more of the signal originates as backscatter within the water body, but the problem caused by atmospheric scattering is still serious (Fig. 7).

Another major problem in remote sensing of water quality is that a signal may represent a mix of water colour, bottom reflectance, turbidity produced by phyto-plankton, and turbidity produced by suspended sediments. Changes in the relative amounts of constituents may occur over time as well as changes in the size, shape, and colour of suspended materials. If the ultimate goal is to monitor lake eutrophi-cation or the suspended load of rivers with remote sensing, it is necessary to determine the reasons for change in water turbidity and backscattered flux.

A change in backscattered flux does not always indicate a complex change in

416 G. K. Moore

0 N 2 N 3 N

Aerosol number density, Elterman model

(relative aerosol content)

FIG. 6. Over clear, deep water, more than 95 per cent of the Landsat signal comes from the atmosphere. Adapted from Griggs (1974, Fig. 3).

0 N 2N 3N Aerosol number density, Elterman model (relative aerosol content)

FIG. 7. Differences in aerosol content of the atmosphere can result in a greater difference in the signal measured by Landsat (0.24 mW/cm2) than the signal difference between 10 and 100 milligrams per litre of suspended silt. Atmosphere line from Griggs (1974, Fig. 3).

physical constituents. In many inland lakes, for example, changes in turbidity during the spring to fall months are caused almost entirely by increases and later, decreases in phytoplankton content. Similarly, the changes in turbidity of many reservoirs and estuaries are caused almost entirely by changes in concentration of suspended silt or clay. These facts suggest that with appropriate solar and atmos­pheric corrections, remote sensing could be used successfully to monitor turbidity changes in many cases. A few field samples could be collected to confirm the nature

Satellite remote sensing of water turbidity 417

of the turbidity changes and to correlate these measurements with digital values of water radiance.

There are frequent changes in the source and grain-size distribution of suspended sediments in some rivers; it is unlikely that remote monitoring of suspended load will be possible in these cases. Other rivers have an excellent correlation of instan­taneous turbidity and suspended-sediment concentration; for these streams, the main obstacle to successful remote monitoring is the spatial resolution of Landsat. A 79 m resolution cell is larger than the width of most rivers at low and medium stages.

Various aspects of the principles and problems described in this report have been the subjects of many recent studies. Results of these efforts are scattered widely in the optical, hydrological, atmospheric, and remote sensing literature. Almost all studies show the feasibility of correlating satellite data with concurrent measure­ments of water turbidity. For example, Thoreson et al. (1975) used multiple regression techniques to determine the correlation between various field measure­ments and film densities on Landsat images, aerial photographs, and ground photographs.

Most studies have shown only a fair correlation of remotely measured flux and water turbidity. Correlation coefficients typically range from 0.7 to 0.9. In deep or turbid water, poor correlation may be caused mostly by lateral and vertical varia­tions in water turbidity. A water sample represents only one point in the 6200 m2

area viewed by the Landsat multispectral scanner at one instant in time. Also, most water samples have not been collected by depth integrating samplers; this can result in errors if vertical mixing is poor, as may be the case where turbidity is produced by phytoplankton.

Duntley (1963, p. 220) made comparisons of scattering data (at several wave­length intervals) obtained by investigators in seven different parts of the world. He concluded that forward scatter is white light and indicative of a single volume scattering coefficient, regardless of wavelength.* He also concluded, however, that significant differences occur in the character of backscatter. Existing turbidimeters that depend on forward scatter may not provide readings that form a linear correlation with backscattered flux.

Secchi disks are easily and commonly used as a measure of water colour and turbidity. As noted by McCluney (1975, p. 262), however, Secchi depths are influenced greatly by disruption of the image, caused by surface waves. There also can be significant differences in readings between observers and times of observa­tion (consider the effects of a rising sun along with other variables in Table 1).

At present there is not an ideal method of measuring water sample turbidity for correlation with the backscattered light flux recorded by satellites. Logically, how­ever, correlations should be improved by first relating turbidimeter readings to those from a hand-held spectral radiometer. By including radiometer readings of

* This statement appears to contradict previous comments on Raleigh scattering by water molecules and Mie scattering by colloids. However, research by Scripps Institute of Oceanography (Duntley, 1963, p. 217) has shown that scattering in the open sea 'is predominantly due to transparent biological organisms and particles large compared with the wavelength of light'. Raleigh scattering 'is so heavily masked by nonselective scattering due to large particles that total scattering in the sea is virtually independent of wavelength'. A similar phenomenon probably occurs in inland waters, but Mie scattering by high concentrations of colloids must be taken into account, as must the absorptance (and reflectance) characteristics of larger particles, when present in high concentrations.

418 G. K. Moore

this type in a programme of sample collection and analysis, it should at least be possible to determine the reasons for data scatter and poor correlations.

EVALUATION OF CONCEPTS

Factors that must be evaluated in deciding upon satellite surveillance of water quality are technical feasibility, costs, and value of the information. The goal is feasible if (1) qualitative or relative results are adequate, (2) remotely measured signals are correlated with results of water sample analyses during each satellite orbit, or (3) adequate solar and atmospheric corrections can be made to the satellite data. In this, as in most other applications of remote sensing, the data are best used to obtain information over large areas and to monitor changes with time.

If the problems introduced by changing sun angle and atmospheric condition can be solved, water turbidity could be calculated directly from the backscattered flux measured at a satellite. Thus there is an important need to confirm the adequacy of simple corrections, such as dark-object-subtraction and band ratios. Corrections of this type have been developed and patented by Calspan Corporation (as discussed in Walker et al., 1977). It should be noted that if atmospheric corrections can be devised, all Landsat data (since the launch of Landsat-1 in 1972) will have signifi­cance and value as a record of water colour and turbidity.

For some purposes satellite data and processing costs are high, compared with the costs of conventional procedures. A Landsat digital tape (of a scene covering about 34 000 km2) costs US $200; several hundred to several thousand additional dollars would be needed for computer processing to extract digital values of radiance in water areas. This amount of money would pay for quite a bit of field sampling and analysis. Nevertheless, relative costs may change, and remote sensing offers a partial alternative to field sampling in renlote areas, limited manpower situations, and large study areas.

The considerable advantage offered by satellite data in large study areas is best shown by an example, determination of causes for siltation in the Charleston harbor, South Carolina. About 135 km upstream from Charleston, the Wateree and Congaree Rivers join to form the Santee River, which flows into Lake Marion (a reservoir about 60 km long and created by the Santee dam). At the downstream end of Lake Marion, water is diverted (instead of flowing into the natural downstream channel of Santee River) first into Lake Moultrie and then into Cooper River, which enters the Atlantic Ocean at Charleston harbor. Continuing navigation problems caused by siltation in Charleston harbor have been attributed by some people to very turbid water in the Congaree River, far upstream.

A Landsat scene (Fig. 8), 3 April 1976, shows the entire area of interest, from above the junction of the Wateree and Congaree Rivers to the clear Atlantic Ocean, offshore from Charleston harbor. These Landsat data were digitally enhanced, analysed, and interpreted, and the following tentative conclusions were formed: (1) at this particular time, very turbid water from the Congaree River flows into Lake Marion, but the suspended materials settle out about halfway down the lake; (2) an area of moderate turbidity near the lower end of Lake Marion probably is caused by wave action in an area of shallow water; (3) the moderately turbid water from Lake Marion produces only a small turbidity plume in Lake Moultrie; (4) clear or nearly

Satellite remote sensing of water turbidity 419

N

Wateree River swamp

4 -Santee River

* -Si.r.iee R.ver swama Congaree River ^

Lake Mario.-*''

s£ «is

^ 3«;-tf P.„-e'»--»' :-".33sidSS

'. * S ' C *": » -*> • y -.-'̂ *.tl^§^'

. ^

FIG. 8. Recurring siltation in Charleston harbor results in navigation problems. The entire drainage area can be seen on this NASA band 5 Landsat image for 3 April 1973. At this time there is very little siltation, and the source of any siltation is erosion in the Cooper River. Scale is about 1:2 000 000 (Atlantic Ocean label is equivalent to 40 km).

clear water enters the Cooper River drainage system; (5) very little siltation is occurring in Charleston harbor at this time; and (6) the source of any siltation in Charleston harbor at this time is erosion within the Cooper River drainage area.

Only a very few water samples would have been needed to confirm the tentative conclusions formed from interpretation of Landsat data in the Charleston area, if these Landsat data were not available, however, a much larger number of water samples would have been needed to determine water turbidity patterns in this region; additional water samples then might have been needed to confirm interpre­tations drawn from the patterns. Costs for the Landsat interpretation were: (1) digital tape of Landsat scene, US $200, (2) digital processing, 5 h at US $ 100/h = $500, and (3) salary, 1\ h at US $20/h = $ 150.

The final factor to be evaluated before making a decision on the use of satellite data is the information content of the data. Turbidity and colour are only two of many constituents and characteristics of terrestrial water. Turbidity can be a measure or index of sediment and plankton concentration; it may therefore be a

420 G. K. Moore

useful measurement in studies of sediment transport, lake and reservoir filling, environmental conditions, and lake eutrophication. Turbidity also increases treat­ment costs for public water supplies. Colour generally is significant only because it is aesthetically undesirable in public water supplies and may increase water treatment costs. Turbidity and colour are not significant factors in many other types of water quality studies and problems. From at least this one viewpoint, satellite remote sensing does not compare favourably with conventional procedures, wherein a single water sample can be analysed for many constituents.

A major benefit of satellite remote sensing might be obtained by periodic evalu­ation of water productivity in major oceanic fishing grounds, estuaries, lakes, and reservoirs. Optical turbidity of water in many such areas should be related to plankton type (absorption characteristics), size, shape, form (dispersed or clumped; colony size and shape), and distribution in the vertical column. Plankton increase backscatter, but for high concentrations the range of variation in spectral signatures is poorly known. Also, because of the typically irregular distribution of plankton with depth, there may be a nonlinear relationship between total plankton content and the average path length of light. Nevertheless, it may be possible in many localities to establish a secondary correlation (after atmospheric corrections) of remote signals with relative degree of eutrophy and productivity. This approach was used by Boland (1976).

Another major benefit of remote sensing may come from analysis of water current and circulation patterns, as shown by variations in turbidity and colour. Optical turbidity generally is related to water velocity, both because larger particles settle out in slow-moving water, and because colloids and other fine-grained sediments are weakly cohesive; dispersal is partly dependent on current velocity. Mean turbidity thus may be an index of average water velocity, and local variations in turbidity may indicate the direction and size of local currents.

Water currents determine erosion, sedimentation, and ecological conditions (by affecting light penetration, nutrient availability, pollutant concentration, and water stagnation). Under the best, cloud-free conditions, Landsat imagery shows only near-surface turbidity patterns, at only one time of day, and only once every 9 or 18 days (depending on whether one or two satellites are operating), but this is informa­tion that was impractical or impossible to obtain previously. A large number of current measurements and water analyses are needed in a short period of time to define circulation patterns in large river systems, lakes, and estuaries by conven­tional procedures. The chances of obtaining a Landsat image that shows a single, short-lived hydrological event are poor, but the chances of obtaining images that are representative of recurring hydrological events are good.

CONCLUSIONS

Remote sensing obtains an optical measure of water colour and turbidity; pollu­tants must affect colour or turbidity to be detectable. Dissolved coloured materials increase the absorption of light in water and decrease the remote signal, whereas suspended materials increase the backscatter of light and increase the remote signal.

One of the major problems in calculating water turbidity from a remotely measured flux is the difference in absorption and scattering of light in the atmos-

Satellite remote sensing of water turbidity 421

phere from one time to another. Because there are many variables, it may not be practical to determine an atmospheric correction directly, but a combination of dark-object-subtraction and band ratios may provide an adequate correction.

The other major problem is that a backscattered flux may represent a mix of water colour, bottom reflectance, turbidity produced by plankton, and turbidity caused by suspended sediment. In many cases, however, a change in flux is caused simply by a change in the concentration of one constituent.

A number of studies have shown only a fair correlation of remotely measured flux and water turbidity. The main reason probably is lateral and vertical variations in water turbidity, but deviations also may be introduced by the manner in which turbidity is measured on the ground. A remotely measured energy level may be a more accurate representation of average, near-surface turbidity than laboratory analysis of a point sample.

Factors that must be evaluated in deciding upon satellite surveillance of water quality are feasibility, cost, and value of information content. Turbidity and colour are only two of many constituents and characteristics of terrestrial water. From this viewpoint, satellite remote sensing may not compare favourably with conventional procedures, wherein a single water sample can be analysed for many constituents. However, remote sensing offers considerable advantages for the study of large areas, determination of current and circulation patterns, and monitoring of sedi­mentation, water productivity, and eutrophication.

REFERENCES

Boland, D.H.P. (1976) Trophic classification of lakes using Landsat-1 multispectral scanner data. US Environmental Protection Agency Ecological Research Series rpt. EPA-600J3-76-037.

Dietrich, G. (1939) Die Absorption der Strahlung in reinen Wasser und im reinen Meerwasser. Ann. d. Hydrolgr., U. Mar. Meteor., 67, 41-417.

Duntley, S.Q. (1963) Light in the sea. Optical Sot: Am. J. 53, 214-233. Griggs, M. (1974) Determination of the aerosol content in the atmosphere from ERTS-1 (Landsat 1)

data. In: International Symposium on Remote Sensing of Environment, Michigan, 9th, 1974, Proceed­ings, Ann Arbor, Michigan, Environmental Research Institute of Michigan, 1, 471—481.

List, R.J. (Ed.) (1971) Smithsonian Meteorological Tables, 6th edn. Smithsonian Institute, Washington, D.C.

McCluney, W.R. (1975) Radiometry of water turbidity measurements. Wat. Poll. Control Fed. J., 47, no. 2, 252-266.

Stumm, W. & Morgan, J.J. (1970) Aquatic Chemistry, p. 510. John Wiley & Sons, New York. Thoreson, B.D., Moore, D.G. & Haertel, L. (1975) Remote sensing of water quality in prairie lakes.

Brookings, S. Dak., S. Dakota State Univ., Remote Sensing Institute rpt. SDSU-RSI-75-12. Walker, J.E., Gallagher, T.W. & Schott, J. (1977) Forest damage assessment system (FORDAS) study.

Calspan Corp. rpt. to U.S. Forest Service, Northeastern Forest Experiment Station. Yost, E. & Wenderoth, S. (1969) Agricultural and océanographie applications of multispectral color

photography. In: International Symposium on Remote Sensing of Environment, Michigan, 6th, 1969, Proceedings, Ann Arbor, Mich., Michigan Univ. 1, 145-173.