NASA Reference Publication 1012 

Remote Sensing of 

Oceanic Parameters 

hAN tiopy:’ RETLI~TN TO 

AFWL TECHNICAL. LIRWP VY 

Kqw-I,np4n /&r=n, h’ n’ 

the Skylab/Gamefish Experiment 

Kenneth H. Failer 

NOVEMBER 1977 
NASA Reference Publication 1012 

Remote Sensing of 

Oceanic Parameters During 

the Skylab/Gamefish Experiment 

Kenneth H. Failer 

Lyndon B. Johnson Space Center 

Houston, Texas 

National Aeronautics 

and Space Administration. 

sdaullk rwl Tochnlcal 

lnkrmrtkroac. 

1977 

CONTENTS 

Section 

SUMMARY ................................. 

INTRODUCTION ............... :. ...... .'. ..... 

BACKGROUND ............................... 

OBJECTIVES .............................. 

DATA ACQUISITION ............................ 

Surface Data ................... ., ......... 

Aircraft Data ............................ 

Satellite Data .- .......................... 

MEASUREMENT OF WATER SURFACE TEMPERATURES ... ; ........... 

Technical Approach ......................... 

Analysis Results and Evaluation .. ., .................. 

MEASUREMENT OF CHLOROPHYLL-a ..................... 

Technical Approach ........... * ............. 

Analysis Results and Evaluation ................... 

MEASUREMENT OF TURBIDITY ....................... 

Technical Approach .......................... 

Analysis Results and Evaluations .................. 

ANALYSIS APPLICATION .................... ., ..... 

CONCLUSIONS AND PROGRAM EVALUATION .................... 

REFERENCES ..................... : . '. ........ 

Page 

1 

2 

3 

6 

8 

10 

10 

11 

11 

11 

14 

15 

15 

20 

27 

27 

28 

32 

35 

37 

iii 
TABLES 

Table 

.- 

1 

II 

III EVALUATION OF SECCHI TRANSPARENCY MEASUREMENTS . . . . . . . 30 

Figure Page 

1 National Marine Fisheries'Service experimental 

approach . . . . . . . :,. . . . . . . . . . . . . .'. . . . 7 

5 

8 Chlorophyll-& measurements 

Page 

EVALUATION OF REMOTE CHLOROPHYLL MEASUREMENTS . . . -. :. . . 21 

EVALUATION OF REMOTE CHLOROPHYLL MEASUREMENTS USING 

COMPOSITE TECHNIQUE ,. . . . . -. . ,. . . . . . ‘. . . . . . . 22 

FIGURES 

Test area with fishing squares . . . . . . . . . . . . . . . . 7 

Planned flight line and boat station map . . . . . . . . . . 9 

Comparison of remotely sensed temperature as measured by the. 

PRT-5 from an altitude of 6100 meters and derived from 

3000-meter RS-18 measurements . . . . . . . . . . . . . . . . . 12 

Analysis of water temperature data. Values are in degrees 

Celsius; contour interval: 0.25O C 

(a) Remote measurements . . .-. . . . . . . . . . . . . . . 

(b) Surface measurements . . . . . '. . . . . .,. . . . . . . :3' 

Isometric plot of radiance spectra for approximately 11 kilometers (6 nautical miles) along flight line 2 . . . . . . ., 16 

Linear correlation of chlorophyll-a with radiance 

(a) Normalized by radiance at 520 nanometers .. ....... 

(b) Normalized by radiance- at 620 nanometers ......... :i 

(a) Along fliaht line 2 

(b) Along fii;ht line 3 (~o'caiibration'p~ir;t;,~~~t~~s l '. " 

23 

flight line) . . . . . . . . . . . . .- . . . . . . . . 24 

iv 
Figure Page 

9. Analysis of chlorophyll-3 data. Values are in milligrams 

per cubic meter. Contour -interval: 0.5 mg/m3 

(a) Remote measurements . . . . . . . . . . . . . . . . . . 25 

(b) Surface measurements . . . . . . . . . . . . . . . . . . 26 

10 Linear correlation of Secchi transparency .with radiance 

(a) Normalized by mean blue radiance (390 to 

430 nanometers) 

(b) Normalized by mean %&~d'r~d~ak i9il.t; l ' ' ' l l 

1073 nanometers) . . . . . . . . . . . . . . . . . . . 

11 Secchi transparency along flight line 2 . . . . . . . . . . . 

29 

29 

31 

12 Analysis of turbidity data. Values are in meters: contour 

interval: 2.5 meters 

(a) Remote measurements 

(b) Surface measurements i&hi &iir%i&ij : : : : : : : : 
REMOTE SENSING OF OCEANIC PARAMETERS 

'DURING THE SKYLAB/GAMEFISH EXPERIMENT 

Bv Kenneth H. .Faller* 

Lyndoi B. Johnson Space Center 

SUMMARY 

The Earth Resources Laboratory participated in the National Marine 

Fisheries Service Skylab Experiment 240 by assisting in mission planning, 

data acquisition, and analysis of remotely acquired data. The surface 

data were acquired during 3 days in August 1973 in a triangular test area 

that extended southward from near Destin, Florida; aircraft and satellites 

surveyed the test area on the third day of the mission. The focus of the 

work being reported here is the aircraft data, which consist of thermalinfrared measurements, radiance spectra, and supporting photography. 

. 

The analysis was directed toward three basic objectives: remote 

measurement of sea-surface temperature and derivation of chlorophyll-a 

concentrations and of Secchi extinction depths from water color. The-work 

was in .support of the National Marine Fisheries Service effort and was 

pursuant to the continuing program of the Earth Resources Laboratory to 

advance the application of remote-sensing technology to marine resources 

problems. The algorithms used by other investigators for the derivation 

of chlorophyll content and turbidity were evaluated with the data collected 

in this experiment. Good accuracy was obtained for the chlorophyll calculation but only fair accuracy for the turbidity computation. Two statistical 

methods, principal-factor and linear correlation analyses, were used to 

select optimum radiance data for the remote sensing of chlorophyll and turbidity. Chlorophyll-a concentrations were remotely measured with an accuracy 

of 0.4 mg/m3 over a rainge of 0 to 5 mg/m3. Secchi extinction depths were 

derived from spectral radiance measurements with an accuracy of better than 

5 meters over a range of 2 to 30 meters. 

The thermal work consisted of applying atmospheric corrections to the 

aircraft infrared data and combining the data sets from three different 

sensors on two aircraft. A map of the surface temperature was developed 

from this composite data set. Varying atmospheric conditions over the 

test area influenced these data greatly and introduced ,relatively large 

errors that could not be corrected by using current techniques. 

*NASA Earth Resources Laboratory, 1010 Gause Blvd., Slidell, La. 70458. 
INTRODUCTION 

An important goal of the Earth Resources Laboratory (ERL) has been 

the development of remote-sensing technology applicable to marine problems. 

The work descr,ibed in this document is one phase of a cooperative-effort 

of the ERL and the National Marine Fisheries Service (NMFS) National Fisheries Engineering Laboratory to establish the feasibility of using satellite 

imagery for determining the availability and distribution of living marine 

resources, specifically gamefish. The stated objectives of the multiphased 

, experiment, designated the Skylab/Gamefish Experiment, were to 'evaluate the 

use of remotely acquired data from satellite platforms to improve oceanic 

gamefish stock assessment, to enhance the capability of predicting the best 

areas for gamefishing success, and to examine relationships between ocean 

surface data and gamefish distribution., 

The role of the ERL in the experiment was to plan remote data acquisition, to assist in the collection of surface and remote data, to process 

water samples in the laboratory, and to develop and evaluate measurements 

of oceanographic parameters from remotely acquired data. The remote data 

analysis, which was directed toward supplying NMFS with information that 

could be readily applied to the fisheries distribution analysis, consisted 

primarily of the computation of chlorophyll-a content and Secchi extinction 

depth as a measure of turbidity from wavelength-discriminated visible, nearinfrared radiometry, and the computation of sea-surface temperature from 

thermal-infrared radiation measurements. 

This report documents ERL efforts toward the continued development 

and evaluation of techniques for the measurement.. of three oceanic parameters. Using remote and surface data acquired during the Skylab/Gamefish 

Experiment, existing procedures for the calculation of sea-surface temperature were applied, existing algorithms for the computation of chlorophyll-a 

and Secchi transparency measurements of turbidity were applied and evaluatEd, 

and modifications.made to these algorithms.which were then applied and evaluated. The remote measurement of these three oceanic parameters was integral 

to the NMFS effort to evaluate the use of remote sensing for the assessment 

and monitoring of the gamefish resources because of the relationship that 

was found to exist between the distribution of certain species of fish and 

these parameters (ref. 1). 

The author is most grateful for the cooperation of the NMFS in the 

entire Skylab/Gamefish effort, especially that of Kenneth Savastano, who 

was the Principal Investigator for the project, and with whom most of the 

interaction took place. The experiment was initiated by William Stevenson, 

who served as Principal Investigator until his departure midway through the 

experiment. James Weldon, formerly of the Earth Resources Laboratory, was 

principally responsible for the planning of the remote data acquisition mission and of the ERL role in the overall experiment. The surface data acquisition missior? was carried out by NMFS, National Oceanic and Atmospheric 

Administration (NOAA) National Ocean Survey, NASA, General Electric, and 

Lockheed Electronics personnel. 

Jerry Brashier of Lockheed Electronics has been especially helpful in 

all aspects of the operations and documentation phases of the experiment. 

2 
Ronald Holyer, formerly of Lockheed, assisted in the analysis of the data, 

and his assistance in developing the algorithms for computing the oceanic 

parameters from the spectrometric data deserves special mention. 

As an aid to the reader, where necessary the original units of measure 

(except for temperature) have been converted to the equivalent value in the 

Syst'eme International d'Unit& (SI). The SI units are written first, and 

the original units are written parenthetically thereafter. 

BACKGROUND 

The B- to 14-micrometer region of the electromagnetic spectrum is 

considered the thermal-infrared band and corresponds to an atmospheric 

transmission window that has become somewhat dirty because of absorption 

and emission by such atmospheric constituents as water vapor, carbon 

dioxide, ozone, and aerosols. The measurements of infrared radiation must 

be processed to remove the effects of the atmosphere so the radiation 

measurement can be translated into thermodynamic temperature. Boudreau 

(ref. 2) has developed a theoretical model for atmospheric effects that 

can be used to correct the radiation measurements or to estimate the 

effect of different atmospheric conditions on remote measurements. 

Evaluation of the remote thermal measurements has been difficult in 

the past. In 1972, Worthington (ref. 3) performed, extensive analyses on 

thermal data acquired during 13 missions over the Mississippi Sound. 

After correcting surface measurements for the effects of insolation on the 

surface waters between the times of surface and remote sampling, he found 

that the remote measurements were typically within 0.20 C and often within 

0.10 C of the adjusted surface temperatures. Occasional errors as large as 

1.00 C were observed but could not be explained. For example, in the case 

of the most deviant data point of his analysis, it is impossible to explain 

the discrepancy by hypothesizing nonuniform variation of surface temperature 

with time because the measurements were essentially simultaneous. From his 

analysis, Worthington reached no conclusion as to which measurement caused 

the error, and it is possible that the remote measurement may be more 

accurate than the surface reading. Under ideal atmospheric conditions, 

the remote thermal measurement may provide synoptic data that are more 

accurate than surface data acquired over an extended period. 

Remote measurement of chlorophyll content and turbidity is more 

complex than the measurement of surface temperature. The techniques now 

being explored involve interpretation of water color as measured by spectrometers or multispectral scanning radiometers. The physical phenomena 

that determine water color are complicated and involve the interaction of 

many different factors. As a result, extensive efforts have been made to 

empirically relate these two parameters to aerial and satellite measurements of water color. 

Clarke et al. (ref. 4).reported successful discrimination of waters 

having high;,low, and very low chlorophyll content on the basis of spectra 

of.backscattered light from the sea obtained by an airborne spectrometer. 

The results reported can be expected from.the transmission characteristics 

3 
of the pigment; strong absorption in the blue region of the spectrum results 

in a negative correlation between chlorophyll.concentration and relative 

upwelling radiance in the blue region, whereas backscattering by chlorophyllbearing organisms and low absorption in the green region result in a positive correlation of chlorophyll content with relative upwelling radiance 

in that portion of the spectrum. Ana,lysis of the transmission characteristics of chlorophyll and water considered together (ref. 5) explains the 

behavior of the blue and green portions of the spectrum with varying chlorophyll content and the lack of variation in the red region of the spectrum. 

Scattering of light by suspended particles must be considered together with the attenuation by chlorophyll and other pigments. Chlorophyllbearing phytoplankton, exclusive of the effects of any pigments, scatter 

downwelling radiation and thus reduce penetration and simultaneously increase backscattered light. The pigment contained within the cells modifies the scattering, causing it to be wavelength-dependent. The.result is 

a decrease of the scattering and an increase of the absorption of blue and 

red light. It is thus evident that, whereas increasing numbers of phytoplankton per unit volume of seawater will cause increasing attenuation of 

blue light because of the chlorophyll pigment, scattering by the plankton 

will simultaneously increase upwelling light and decrease penetration. Other 

suspended particles, including bacteria, plant fragments, and inorganic 

materials, have a scattering effect that is dependent upon the refractive 

index of the particles and upon the ratio of particle size to wavelength. 

Coincident variation of size, index of refraction, and number of the various 

suspended particles varies the backscattering of incident radiation across 

the spectrum. Scattering from unpigmented particles diminishes the effects 

of varying chlorophyll content on water color. 

Another problem that may seriously affect any calculation based on 

the color of the sea results from specular reflection of incident solar 

radiation and skylight from the water surface. This problem may be rather 

severe, because the radiation emerging from the water (as observed at 

nadir) has been estimated by Cox and Munk (ref. 6) to be approximately 

equal to the reflected skylight in intensity, and the specular reflection 

of sunlight may be an order of magnitude greater. Determination of an 

oceanographic parameter from water color requires the measurement of small 

changes in only a small component of upwelling radiance. 

The approach used in this experiment, to model,the effects of chlorophyll content variation on light backscattered from the sea, is based on 

previous successful statistical studies of the variation of spectra of 

upwelling light with changes in chlorophyll content. Weldon (ref. 7) 

examined experimental data taken at different altitudes over the 

Mississippi Sound and found that, from the difference of the radiance at 

two wavelengths divided by the radiance at a third, a linear function 

could be constructed that describes the chlorophyll content very well in 

the l.O- to 5.0-mg/m3 range. Holyer found that the fourth power of a 

lHolyer, Ronald J. (unpublished report): A Statistical Method for 

Determination of Chlorophyll Concentration From the Remotely Sensed Ocean 

Surface Albedo. Lockheed Electronics Co., Inc., Bay St. Louis, Miss., 

Contract NAS 9-11584, 1973. 

4 
weighted sum of the radiance in 56 bands divided by the radiance in a 57th 

enabled computation of the chlorophyll concentration in a range of 1.2 to 

40 mg/mS with an average error of 75 percent, as opposed to 20 percent 

using Weldon's technique on the same data set. It should be noted that 

these accuracies were determined with the data that were used to develop 

the mode7 and hence do not constitute an independent test. 

The selection of wavelengths for use in chlorophyll prediction is 

discussed briefly by Weldon (ref. 7), who selected 620 and 470 nanometers 

for the wavelengths at which the difference of radiances was to be computed. 

This decision was based on the large difference in radiance values at these 

wavelengths that occurred with changes- in chlorophyll concentration and the 

limitations of the instrument providing the spectral data. He hypothesized 

that 440 nanometers would be better than 470 nanometers because of the peak 

in the chlorophyll absorption spectrum at approximate7y 440 nanometers but, 

because of spectrometer limitations, could not verify this hypothesis. The 

radiance at 520 nanometers was used as the divisor because its variance with 

chlorophyll content was slight. 

Also normalizing the radiance at 520 nanometers, Holyer used a71 data 

available, even though high correlation of radiance at the different wavelengths resulted in redundancy in the data. He found that nearly 80 percent 

of the chlorophyll information was contained in the radiance at 22 wavelengths 

In general, an effort has been made by investigators (refs. 7 to 9) to use 

in the denominator of the ratios in their models a wavelength at which the 

radiance was relatively unaffected by chlorophyll concentration variation 

but was affected by atmospheric variations and changes in surface reflection. 

The technique for inferring turbidity from water color has undergone 

a development similar to that for determining chlorophyll concentrations. 

No applicable physical model for turbidity as measured by the Secchi disk 

has been developed because of the very complicated manner in which numerous 

factors interact to yield this rather superficially simple parameter. Numerous factors, such as size and number of particles per unit volume, index of 

refraction of particles, and dissolved and suspended pigments, all affect 

the Secchi extinction depth; therefore, this turbidity measurement does not 

describe a unique physical situation. The fact that different combinations 

of these factors having the same Secchi transparency may affect the water 

co7or in different ways suggests that water color is not a good source of 

data on turbidity. However, because experience has shown a statistical correlation between the Secchi transparency measurement of turbidity and water 

co7or, one must conclude that in a given geographical area (at 'least during 

a short time) the factors that affect this measurement do combine in a unique 

manner that enables the inference of this parameter from water color. 

Weldon (ref. 7) found that a statistical relationship existed between 

Secchi transparency measurements and the ratio of radiances measured at 550 

5 
and 600 nanometers in 

used a technique simi 

his data obtained from the Mississippi Sound. Holyerl 

ar to that employed in determining chlorophyll content 

. from spectrometer data, using a fourth-power relationship between the 

Secchi extinction depth and a weighted sum of radiances normalized by the. 

radiance averaged over the entire sensed spectrum (390 to 1290 nanometers). 

Working only with the same data set, Holyer found that the average error 

using the nonlinear function of the entire set of spectral data was 

15.6 percent, as opposed to 16.8 percent using the ratio of two sets of 

radiances. This data set was taken in the Mississippi Sound, where Secchi 

extinction depths were in the range of 1.0 to 3.5 meters. 

OBJECTIVES 

The work reported in this document was initiated to assist the NMFS 

in its efforts to demonstrate the applicability of remote-sensing technology to the assessment and monitoring of the gamefish resource. The 

approach taken by NMFS in this demonstration is described in figure 1. A 

direct relationship between the resource and the remotely acquired data 

(A) was not anticipated, although the possibility of finding a statistical 

correlation between the two was not ruled out. Emphasis was placed on 

exploring the relationships that might exist between the oceanographic and 

meteorological parameters and the resource distribution and abundance using 

surface measurements of the parameters (C). Simultaneously, the ERL would 

concentrate on developing remote measurements of oceanographic parameters 

(B). 

Four parameters of interest to NMFS in the gamefish distribution 

analysis had previously been measured with some success from remote data. 

Although Thomann (ref. 10) demonstrated that salinity could be measured 

remotely, the unavailability of instruments prevented remote measurement 

of salinity in this experiment. Chlorophyll-a, turbidity, and surface 

temperature were the remaining parameters thaT could be measured remotely 

and that were applicable to the NMFS effort. Of these, only the remote 

measurement of surface temperature is approaching operational status. 

Measurement of the other parameters required modification and refinement 

of existing procedures that led to an expanded understanding of the relationship between such oceanographic parameters and the optical properties 

of the sea as observed from aircraft and satellites. 

lHolyer, Ronald 3. (unpublished report):. Additional Investigations 

into the Application of Optimal Linear Least-Squares Prediction for 

Measurement of Ocean Parameters From the Remotely Sensed Surface Albedo. 

\;T!heed Electronics Co., Inc., Bay St. Louis, Miss., Contract NAS 9-77584, 

. 

6 
Remote data 

A B 

/ \ 

Resource 

(gamefish) t C Surface data 

(oceanographic and 

meteorological parameters) 

Figure l.- National Marine Fisheries 

Service experimental approach. 

a a7 

I 

Longitude, deg W 

a6 

I 

Gulf of Mexico 

I 

Figure 2.- Test area with fishing.squares. 

7 
The three specific objectives of the work being reported were as 

follows. 

7. To produce a composite thermal map from data acquired at two 

different altitudes at different times with three different instruments 

2. To produce a contour map of chlorophyll-a concentration from 

spectral radiometer data taken at an intermediate-altitude 

3. To produce a contour map of turbidity from spectral radiometer 

data taken at an intermediate altitude 

Tasks leading to the realization of these objectives included 

selection of optimum techniques for the measurement of chlorophyll and 

turbidity from water color data and evaluation of such techniques. The 

oceanic parameter maps produced in meeting the specific objectives were 

designed for the use of NMFS in its efforts to relate the resource data 

with information that could be remotely acquired. 

DATA ACQUISITION 

Fulfillment of the NMFS objective required a large-scale acquisition 

effort that included satellites, aircraft, oceanographic vessels, and 

fishing boats. Woods and Cook (ref. 71) have documented the surface 

operations. 

Figure 2 is a map of the test area showing the sampling squares to 

which all fisheries data were referenced. Also shown in the figure is the 

groundtrack of the Skylab satellite. The location of s.urface observation 

stations at which oceanographic and meteorological data were obtained by 

the oceanographic vessels and the location of flight lines scheduled for 

the two aircraft are shown in figure 3. Oceanographic vessels were equipped 

,with long-range navigation (loran) systems, which enabled location of the 

ships at their stations with an accuracy estimated to be approximately 

900 meters (0.5 nautical mile). .By monitoring a radio beacon on the NASA 

vessel, The ERL, located at the intersection of the flight lines, the aircraft crGXZFe able to closely fly the planned lines. Because the only 

orientation points were The ERL and the intersection of two of three flight 

lines with the coast, po=iXng of the light aircraft data along the 

flight line is subject to error, estimated to be on the order of as much as 

4 kilometers (2.2 nautical miles). Inertial navigation equipment on the 

NC130B enabled much greater accuracy (<900 meters (co.5 nautical mile))in 

positioning of data acquired from that-platform. - 

The first day of scheduled aircraft operations coincided with bad 

weather conditions that prevented acquisition of aerial and satellite data. 

On August 5, 1973, the second day of remote operations, which included the 

Skylab Earth resources experiment package (EREP) pass, conditions were partly cloudy but acceptable for aerial operations. Because cloud cover was 

expected to increase during the late morning and extensive thundershower 

activity was anticipated for the afternoon, aircraft operations were accelerated to begin data collection at approximately 9 a.m. central daylight 

8 
Pensacola\ ‘\ / < 

N 

I + WN 

86”?0 w 

58 

0 

u-l \.” I “1 une L 

t 

L, A’N 

86”W w 

t! 

65 

0 

Figure 3.- Planned flight line and boat station map. 

Ill lllllllllllllllllll 
time. Increasing cloud cover during the morning resulted in the acquisition of data of questionable applicability on portions of the last flight 

line flown by the light aircraft. 

While oceanographic and meteorological data were being collected and 

aircraft and satellites surveyed the test area, local fishermen were collecting the abundance of data on the gamefish. On August 4, 82 fishing 

boats were in the area; on August 5, 69. 

. 

Surface Data 

Scientific personnel onboard nine oceanographic.vessels made and 

recorded the following observations: local time, water temperature, salinity, air temperature, wet-.and dry-bulb thermometer readings, windspeed, 

wind direction, Secchi transparency, depth, and Forel-Ule color. Water 

samples were acquired for laboratory analysis; one sample from each station 

was prepared for chlorophyll extraction and refrigerated. Relative irradiante was measured at some stations, whereas spectroradiometer data were 

collected by two other vessels at different stations. Atmospheric pressure, 

visibility, and cloud cover were also recorded. The filter and suspended 

material that had been filtered from the water samples for chlorophyll analysis were frozen on the vessels and returned to the laboratory for analysis according to a slightly modified version2 of the technique proposed 

by SCOR-UNESCO Group 17 in reference 12. 

Aircraft Data 

Two aircraft were flown in the primary data acquisition effort, the 

light aircraft contracted by the ERL and the NC130B Earth survey aircraft 

operated by the NASA Lyndon B. Johnson Space Center. The light aircraft 

contained four data systems: three cameras, a spectral radiometer, a 

thermal scanner, and a boresighted radiation thermometer. The NC130B was 

equipped with several camera systems, a 24-channel multispectral scanner, 

a boresighted radiation thermometer, environmental sensors, and an inertial navigation system. The light aircraft operated at an altitude of 

3000 meters, whereas the NC130B flew at 6100 meters. 

A Texas Instruments RS-18 thermal scanner was operated with a 

trimetal detector sensitive in the 8- to 14-micrometer region of the spectrum. An Exotech 20-D spectroradiometer with a silicon detector element 

provided the spectral analysis of upwelling radiance in the visible and 

near-infrared regions. The data stream from these two radiometers and 

from the Barnes precision radiation thermometer (PRT-5) was recorded on 

analog tape for subsequent processing. 

The camera systems on both aircraft provided good imagery, but, because 

of recording problems, the 24-channel scanner data were not usable. The 

PRT-5 onboard the NC130B provided good data that were used in the analysis 

described subsequently. 

2Jones, J. B.: Personal communication, 1973. 

10 
Satellite Data 

The NOAA-2 satellite and a U.S. Air Force satellite supplied data used 

in the direction of the surface operations. From the satellite imagery, 

it was determined that "blue water," thought to be good for fishing, had 

moved close enough to shore to enable concentration of the fisheries data 

acquisition effort in the area designated as the principal remote data 

acquisition area. Skylab data were collected shortly before 12 m., central 

daylight time, with the Sl90 camera systems, the S191 spectral radiometer, 

the S192 multispectral scanner, and the S194 L-band microwave radiometer. 

The Skylab data collection took place on track 62, orbit 15. The EREP 

sensors were operated on August 5, 1973, from 16:37:28 to 16:41:26 

Greenwich mean time; coverage extended on both sides of the test area. 

Cloud cover rendered the S191 data unusable and sunglint contamination 

precluded use of the S192 scanner data for the water color analysis. 

MEASUREMENT OF WATER SURFACE TEMPERATURES 

Technical Approach 

The RS-18 scanner data composed the principal data set for the surface 

temperature analysis. The calibration was performed according to verified 

algorithms (ref. 13) by which the data are corrected for atmospheric 

effects and displayed in a gray-level and digital form. In one technique, 

which requires an area of isothermal water, a corrective factor is 

constructed by forcing the remotely sensed temperature to agree with surface 

measurements in that area. Alternatively, radiosonde data can be used to 

construct an atmospheric correction term that can then be combined with a 

correction for known instrument errors. The RS-18 data were processed 

according to the first technique because of the atmospheric heterogeneity. 

The PRT-5 measurements for 50 points selected randomly were plotted as a 

function of the corresponding RS-18 temperature, which had been corrected 

for atmospheric effects. The slope and intercept of the best fit lines drawn 

through the two sets of data gave the corrections for the PRT-5 measurements 

of surface temperature for both the 6100- and 3000-meter altitudes. The 

high-altitude PRT-5 data are plotted as a function of corrected RS-18 data 

for one of the flight lines in figure 4. Thermal data from the 24-channel 

scanner were not available. 

The thermal map of the study area was constructed from the three data 

sets. The RS-18 temperature was noted along each flight line at 40-second 

intervals, which correspond to distances of approximately 3.7 kilometers 

*(2 nautical miles) on the surface. Where there appeared to be a momentary 

system malfunction, data from the light-aircraft-mounted PRT-5 were used; 

when clouds obscured the surface or when it appeared that atmospheric 

nonhomogeneities were distorting the measurement, the PRT-5 temperature 

acquired by the NC130B was examined to determine whether the same effects 

were present. If they were not, the temperature measured at the higher 

altitude at a slightly different time was used in constructing the surface 

temperature map. If the same anomalies were present and no clouds were 

visible in either set of coincident photography, it was assumed that the 

water temperature was varying in an unusual manner and the remote data 

11 
25.0 

Figure 4.- Comparison of remotely 

sensed temperature as measured 

by the PRT-5 from an altitude of 

6100 meters and derived from 

3000-meter RS-18 measurements. 

23;99 

w).O 30.5 3i.O 

RS-18 temperature, “C 

Composite of: RS-18 

PRT5 t 

Light aircraft -Moo m 

[ 2s*9 PRT-5 NC130B aircraft - 6100 m 

a-- 

l Panama City L 

(a) Remote measurements. 

Figure 5.- Analysis of water temperature data. Values are in degrees Celsius; 

contour interval: 0.25' C. 

12 
(b) Surface measurements. 

Figure 5.- Concluded. 

13 
were accepted; if clouds were present in either set of coincident photography, neither temperature measurement was accepted because perturbations 

by clouds or atmospheric variations were assumed. From the compilation of 

temperatures along the flight lines, isotherms were hand-drawn in an attempt 

to interpolate between flight lines. 

Because surface data were collected throughout the day and the temperature measurement at a given point was often made at a time different by 

several hours from the aerial survey, a normalizing procedure to account 

for surface warming due to insolation was developed. All the temperatures 

measured at each hour were averaged for the entire test area, and the deviation of this average from the average at a time midway through the aircraft 

mission was computed. These deviations were then used as corrective terms 

to normalize the surface measurements to the central time. 

Analysis Results and Evaluation 

The remote and in situ surface temperature measurements have been 

used to construct thermal contour maps of the test area. Figure 5(a) is 

the contour map drawn from these data, figure 5(b) shows the isotherms 

derived from the surface measurements, which have been normalized for 

warming due to insolation to a time midway through the aerial data acquisition exercise. 

Comparison of figures 5(a) and 5(b) does not reveal a high degree of 

similarity. The surface data were taken along two other transects in addition to the three that coincide with the flight lines, but the distance 

between sample stations along the transects was considerably more than the 

4-kilometer (2.2 nautical mile) sampling distance used in processing the 

remote data. The differences in sampling locations may be the source of 

some of the discrepancy, but the poor matching of even general trends 

could not be attributable to such differences. Another source of error in 

the geographic presentation of the data is error in defining the location 

of the aircraft along the flight lines. For example, expansion of the 

remote data along flight line 2 (the east-west line), which would correct 

for possible underestimation of aircraft velocity, would improve the correspondence of the two data sets. Because of discrepancies observed in the 

overlap of photographic frames, the hypothesis that the actual groundspeed 

of the aircraft was greater than the speed used in the data reduction is 

tenable for the light aircraft, which had no inertial navigation equipment 

and which was the principal source of data for the remote measurement. 

The major problems in interpreting the two sets of temperature measurements are the very small temperature gradients found in the test area and 

the variation of the moisture content of the atmosphere. The corrected in 

situ measurements varied by only 1.250 C, whereas a variation of 1.70 C was 

found in the remote data set. Because of atmospheric heterogeneity, which 

was indicated by surface measurements and the forming cumulus clouds, it 

would be unreasonable to expect the 0.10 C relative accuracy theoretically 

possible with the remote thermal techniques to be achieved in this experiment. 

Only a single correction factor can be used for each flight line, but variations of the required correction factor of as much as 0.230 C can be expected 

over a single line. The magnitude of this variation was determined using 

14 
Boudreau's model (ref. 2) with meteorological data acquired at the surface 

sampling stations and from a radiosonde launched at Eglin Air Force Base. 

This larger error would introduce rather large discrepancies into the remote 

thermal contour map. Another fact that must be considered is the precision 

of the in situ measurements, the error of which can normally be as large as 

0.20 c. An error of this magnitude would distort the contours of either the 

surface or remote map considerably because of the small actual gradients 

involved. 

The extent to which it was necessary to combine the data from the two 

PRT-5 sensors‘ and the RS-18 scanner within the constraints of cloud cover 

and apparent severe atmospheric variation was not as great as had initially 

been anticipated. There were two areas along flight line 1 (the northwestsoutheast line) where banding was present in the RS-18 data; data from the 

PRT-5 borne by the same aircraft as the RS-18 were used in these areas. 

The cause of the banding has not been determined conclusively. In most 

regions affected by clouds, it was possible to find cloud-free areas in the 

scanner imagery away from the center of the flight line. In several areas 

that were clouded across the entire scanner coverage, data from the PRT-5 

flown at a different time were used. Even though the aircraft were flown 

at different times, some of the areas that were obscured by clouds in one 

data set were also at least partly under clouds when the other aircraft 

passed over the areas; thus, the extent to which the data from the two 

flights could be used as supplements was decreased. 

MEASUREMENT OF CHLOROPHYLL-a 

Technical Approach 

As discussed previously, no algorithm based on elementary principles 

exists for the calculation of chlorophyll concentration or turbidity from 

spectral radiance measurements, even though variation of these parameters 

obviously does influence, if not determine, water color. An example 

of the change is seen in figure 6, in which the spectra from approximately 

11 kilometers (6 nautical miles) of flight line 2 reflect a rapid change 

of 0.6 mg/m3 in chlorophyll content and a change of 7 meters in the Secchi 

extinction depth. Weldon (ref. 7) and Holyerl have developed empirical 

models that were successfully applied to data acquired over the Mississippi 

Sound. Weldon's techniques and their variations were applied to the data 

acquired with the Exotech 20-D spectrometer in the open Gulf of Mexico during this experiment and were evaluated comparatively. The variations consisted primarily of optimizing the information contained in the set of 

radiance data used in the models by statistical analysis. 

'Holyer, Ronald J. (unpublished report): A Statistical Method for 

Determination of Chlorophyll Concentration From the Remotely Sensed Ocean 

Surface Albedo. Lockheed Electronics Co., Inc., Bay St. Louis, Miss., 

Contract NAS 9-11584, 1973. 

15 
8 E ‘G d IL Time 

Wavelength 

Figure 6.- Isometric plot of radiance spectra for approxiinately 11 kilometers 

(6 nautical miles) along flight line 2. 

16 
It was evident that the Weldon techniques were not adequate, and the 

number of surface measurements was insufficient to apply Holyer's technique. 

New procedures for determining these parameters from the spectra were 

therefore developed. 

The selection of surface measurements to serve as calibrations for 

the models was primarily based on providing the best models for the remote 

measurement of the oceanographic parameters. All stations that were 

sampled within 3 hours of the aircraft flyover and that were not under 

clouds were selected as calibration points, Several calibrations were 

dropped because data from the spectrometer were erratic in the area of 

the station. This deletion left 9 of the available 18 calibration points 

to use for the chlorophyll models. 

Using the selected surface control points, Weldon's technique (ref. 7) 

was first applied in the chlorophyll study. The coefficients al and a2 

of the equation 

cw = al(R620 - R47o)/R!zo + ai? (1) 

were determined by plotting the normalized difference of radiance values 

(R620 - R47O)/R520 against chlorophyll-a concentration and by measuring 

the slope and intercept of the straight Tine that best fitted the data. 

In equation (1), RX represents radiance at wavelength x in nanometers. 

Because Holyer's technique showed improved accuracy over Weldon's for 

the Mississippi Sound data, it appeared that perhaps information important 

to the chlorophyll content determination was being lost by using only the 

three wavelengths. A technique known as principal-factor analysis was then 

applied to the 520-nanometer normalized radiance data in an effort to obtain 

maximum information content and minimum redundancy. Using a variation of 

this analysis, a set of six wavelengths was selected such that the normalized 

radiance at these wavelengths contained 99.9 percent of the total information 

contained in the entire normalized visible spectrum. The wavelengths selected 

by means of this analysis were 480, 560, 630, 769, 911, and 1073 nanometers. 

A least-squares technique was used to construct a linear function of these 

normalized radiance values for calculating the chlorophyll-a concentration Cp. 

The principal-factor approach maximized the information content of 

the set of radiance measurements being used in the chlorophyll calculation, 

even though some of the information included was not relevant to the chlorophyll content. An effort was then made to optimize rather than maximize the 

information content by examining the linear correlation coefficient of the 

520-nanometer normalized radiance with chlorophyll-a content on a wavelengthby-wavelength basis. Figure 7(a) is a graph of the-correlation coefficient 

as a function of wavelength. This graph indicates that the radiance at 600 

and 470 nanometers (normalized by the radiance at 520 nanometers) is highly 

correlated with the chlorophyll-a concentration. After considering the lower 

correlation between these waveleiigths compared with several other pairs (which 

indicated relatively less redundancy between the two), they were selected as 

the optimum wavelengths for this study. Correlation coefficients were -0.941 

for 470 nanometers, 0.974 for 600 nanometers, and 0.949 for the correlation 

17 
1.0 

.5 

E .$ 

'G E 

8 

z 0 

.- I Ti k 

s 

-.5 

-1.0 2 

1 

II III II, II I 

400 450 500 550 6CCl 650 750 850 950 1050 1150 

I I I I I I Id 

550 600 650 750 850 950 1050 1150 

Wavelength, nm Wavelength, nm 

[a) Normalized by radiance at 520 

nanometers. 

(b) Normalized by radiance at 620 

nanometers. 

Figure 7.- Linear correlation of chlorophyll-&with radiance. 

18 
between the.radiance measurement at the two wavelengths. Because Holyer 

used a fourth-power relationship between chlorophyll content and the 

weighted sum of normalized radiance values, a function of the form 

a3 

cc = al(R600 - R470)/R520 + "2 1 (2) 

was analyzed using a least-squares error technique to determine values for 

al, a2, and a3 to compute the chlorophyll content Cc. The symbol Rx 

represents the radiance measurements at wavelength A. Because the value 

of a3 was found to be very close to 0.5, this parameter was set to 0.5 and 

the coeffictents al and a2 were again computed. 

The values of Cc followed the surface measurements well at low 

chlorophyll levels, whereas C 

f 

matched better at higher values. Because 

the Cc function flattened ou at just above 2.2 mg/m3 a composite function 

was developed that used Cc until it exceeded 2.0 mg/m3, then used Cp. 

Equation (3) represents the composite function. 

Ccomp = 

(1) $/ dlu4jOO -R470)/R520 + a2 if (1) < 2.0 

if (1) > 2.0 

where A,, = 480, 560, 630, 769, 911, and 1073 nanometers and n = 1,6. 

The 520-nanometer radiance was used for normalization because of the 

relative constancy of this radiance with respect to chlorophyll content. 

The 520-nanometer radiance appears to be the point in the spectrum at which 

absorption by chlorophyll-a is offset by scattering by the chlorophyllbearing phytoplankton. ToiYard the shorter wavelength end of the spectrum, 

the absorption by chlorophyll is the dominant effect; toward the longer 

wavelengths, the scattering by the phytoplankton increases the upwelling 

light to more than compensate for the chlorophyll absorption. The chlorophyll molecule has an absorption minimum in the 550- to 625-nanometer 

region of the spectrum (ref. 5); therefore, if it were not for scattering 

effects, this would obviously be the best spectral region for normalization. 

However, the fact that other scatterers besides phytoplankton (e.g., inorganics) are present in the water suggests that the contribution of the 

chlorophyll-bearing organisms 'to the total backscattering function may not 

be dominant and, hence, that the 600-nanometer region of the spectrum might 

provide a normalization because of increased independence of absorption by 

chlorophyll. Consequently, the correlation analysis was repeated using the 

radiance at 620 nanometers for normalization. A ra h of the correlation 

coefficient for this data set is shown in figure 4 (b Y . 

This analysis demonstrated that the blue region of the spectrum was 

again negatively correlated, with a maximum correlation at 490 nanometers; 

a second maximum in the plot of correlation coefficient as a function of 

19 
wavelength is found at 610 nanometers, only 10 nanometers away from the 

normalization wavelength. Predictors were developed that use the ratio of 

radiance at 490 nanometers to that at 620 nanometers, the ratio of radiance 

at 610 nanometers to that at 620 nanometers, and the difference of radiances 

at 490 and 610 nanometers divided by the radiance at 620 nanometers. 

Analysis Results and Fvaluation 

The chlorophyll models based on the 520-nanometer normalization were 

stable and were subjected‘to detailed evaluation. The normalization by 

the radiance at 620 nanometers yielded results that matched the calibration 

points very well, as would be expected from the high linear correlation, 

but the functions developed with this data set were very erratic and frequently yielded large negative values. The cause for the unusual behavior 

of the function was not determined, and the models based on 620+ianometer 

normalization were not subjected to detailed evaluation because of the 

obvious failure to provide a physically acceptable prediction. 

Table I contains the surface measurements (C,) with the remote measurement (C,,C ,C,) given by each of three techniques (with 520-nanometer 

normalization P and the respective deviations (A~,A~,&). It is evident 

that the first set of values given by the Weldon function C, is more 

accurate than the other two sets. 

function for Cc 

Further analysis demgnstrates that the 

(eq. (2)) flattens out above 2.2 mg/m but gives accurate 

results when the predicted chlorophyll concentration is less than 2 

!i 

g/m3. 

The function for Cp is the most accurate of the three above 2 mg/m as 

the function continues to track the surface measurements. The remotely 

acquired chlorophyll-a measurements that were used in the contour map were 

computed using the coiiiposite technique (eq. (3)). Table II contains the 

surface measurements with the corresponding remote measurement as determined 

by this composite technique, which gave a root-mean-square (rms) error of 

0.4 mg/m3 over a range of 0.0 to 5.0 mg/m3 for 18 points, 9 of which were 

used for calibration. Th rms error for the nine points not used for cali- 

-bration was also 0.4 mg/m 5 . 

Using the composite technique, plots of chlorophyll-a content along 

flight lines 2 (east-west) and 3 (north-south) were developed (figs. 8(a) 

and 8(b)). They indicate that, when geographical errors 

the remote measurement may be more accurate than 0.4 mg/m 3 

re considered, 

as indicated by 

the error analysis. The remote measurement of chlorophyll-a content based 

on the composite technique was used to develop the contour iiiap of the test 

area (fig. 9(a)). This map may be compared with a similar map generated 

from surface measurements (fig. 9(b)). 

One very interesting point in the comparison is the small patch of high 

chlorophyll concentration seen at the center of the study area in the remote 

chlorophyll map but not found in the map based on surface measurements. No 

surface measurements were taken in this area; thus, the sharp gradient could 

not appear in those interpolated data. The crew of the oceanographic vessel 

that was stationed immediately south of this area reported passing through 

an area of very green water shortly before arriving on station, where the 

water was considerably more blue. Although the'description of the water 

20 
TABLE I.- 'EVALUATIOti OF REMOTE CHLOROPHYLL MEASUREMENTS 

(Values in milligrams per cubic meter) 

Surface 

measurement 

‘el.3 

1.6 

e.8 

e1.7 

e.3 

e.3 

e.4 

e1.5 

e1.7 

el.6 

2.3 

3.5 

:i 

.4 

1.5 

1.7 

4.7 

.- 

“CW .bAw cCP bAP dCC ‘bA C 

--- __-- 

0.9 0.4 0.3 1.2 0.1 

1.1 .5 

:=: 

.8 .O :7 .4 1.21 :: 

1.8 .l 1.8 :: 1.7 

:34 I:, :2 .l 1. :35 

:F 

.O 

.3 .O .3 

::: ::, .42:;.2 1.0 .5 1.6 :; .l

:-"5 

2:2 

.2 E 

3:6 

1.1 .2 1.5 1.8 .l5 

1.3 .l 2.1 1.4 

. .z :i 1.3 1.2 :i 2 :i 

1.3 1.9 :49 1.8 1.4 .3 1.8 1.3 :: 

1.8 .l 1.8 

:; ::: 

.2 

3.5 1.2 5.0 2.2 

rms deviation 0.52 0.62 0.73 

1073 

aCW = al(hj20 - R47o)/Rxo + w 

b411 = magnitude of deviation of Cm from surface measurement. 

CC 

F1 

= zanRAn/R520 where Xn = 480, 560, 630, 769, 911, and 

na ometers. 

dcc = 4 al(R600 - R470)/R520 + a2. 

e Calibration point. 

21 
TABLE II.- EVALUATION OF REMOTE CHLOROPHYLL MEASUREMENTS 

USING COMPOSITE TECHNIQUE 

(Values in milligrams per cubic meter) 

Surface measurement aCcomp bAcomp 

c1.3 

1.6 

c.8 

Cl.7 

c.3 

c.3 

c.4 

Cl.5 

Cl.7 

cl.6 

2.3 

3.5 

:4 

.4 

1.5 

1.7 

4.7 

1.2 

1.2 

;:: 

.5 

.3 

.3 

1.6 

1.9 

1.5 

1.8 

3.6 

.3 

.4 

1.3 

1.8 

::i 

0.1 

.4 

:; 

.2 

.O 

:; 

.2 

.l 

.5 

.l 

:'1 

:S 

.2 

:; 

rms deviation' 0.38 

aSee equation (3). 

bAcomp = magnitude of deviation of Ccomp from surface measurement. 

CCalibration point. 

22 
” 

0 

\ 

ZO’fll, 

- Remote measurement 

0 Surface measurement 

0 Surface measurement 

used for calibration 

0 

J 

41 (22) 61’ (33) 

I --A _~~. 1 I I I 

81 (44) 102 155) 122 (66) 143 (77) 163 (88) 183 (99) 

Distance. km fn. mi.) 

[a) Along flight line 2. 

Figure 8.- Chlorophyll-& measurements. 

23 
0 

- Remote measurement 

0 Surface measurement 

I I 

69 i36) 89’(481 111’ (60) 

I I 

22 02) 44 (24) 133 (72) 155 (84) 

Distance, km (n. mi.) 

(b) Along flight line 3 (no calibration points on this flight line). 

Figure 8.- Concluded. 

24 
- 

[a) Remote measurements. 

Figure 9.- Analysis of chlorophyll-a data. Values are in milligrams per cubic 

meter. Contour interval: 0.5 mg/m3* 

25 
(b) Surface measurements. 

Figure 9.- Concluded. 

26 
color does not verify the high chlorophyll concentration measured 

remotely, it does lend credibility to the measurements. 

The accuracy of the chlorophyll-a measurements that were used to 

evaluate and calibrate the remote measirrements must also be considered. 

The laboratory analysis of the sample is the most readily evaluated phase 

of the surface measurement of chlorophyll and has been found to be repeat-. 

able with an error of 5 percent. Anothei,'factor that affects the error 

analysis is variation of pigment in a given sample with time. The average 

error estimated for the entire process, from sample acquisition through 

spectroscopic analysis’, is JO to 15 percent. 

In evaluating the success of this attempt to remotely measure 

chlorophyll-a content, one must consider a very important qualification on 

the analysis: The experiment was conducted in an area that appeared to be 

h6mogeneousJy free of large concentrations of dissolved organic pigments. 

Although no direct measurements of such pigmects were made, such a conclusion may be drawn from the fact that the waters were generally described as 

very blue. The presence of dissolved organic pigments, known as Gelbstoffe, 

would have caused the water to be green. Had there been a high Gelbstofte 

concentration or if it had varied significantly over the test area, the 

accuracy of the remote measurement would have been degraded. It is possible 

that the area indicated in the remote data set to have a high chlorophyll 

content, in fact, had a high concentration of Gelbstoffe rather than 

chlorophyll. 

MEASUREMENT OF TURBIDITY 

Technical Approach 

The approach used for calculating Secchi transparency measurements of 

turbidity was similar to the one used for determining chlorophyll concentration. Weldon's technique was attempted first, followed by principalfactor analysis and linear correlation analysis to select wavelengths at 

. which the radiance would be used in a linear model. Several normalization 

procedures were used. The criteria for selecting the calibration points 

were the same as those used in the chlorophyll study and eliminated 11 of 

19 possible surface measurements; 

Weldon (ref. 7) found that the ratio of radiance at 550 nanometers to 

that at 600 nanometers provided an accurate calculation of the Secchi extinction depth in the Mississippi Sound. Analysis showed that this algorithm was 

not applicable to the data obtained over the Gulf of Mexico, because it was 

impossible to define the necessary coefficients because of the scatter of the 

point*.' The ratio of radiance at 420 nanometers to that at 570 nanometers 

correlated well with the turbidity measurements; therefore, a model using 

this simple ratio method tias constructed according to the following equation. 

SR = aJR420/R570 + a2 

27 

(4) 
The fact that Holyer's calculation of turbidity was more accurate than 

Weldon's-calculation again led to the hypothesis that not a?1 relevant information contained in the spectral radiance data was being used in the simple 

two-wavelength ratio technique. Principal-factor analysis was again used 

to maximize the information content of a selected set of wavelengths. For. 

this analysis, the radiance was normalized using the average radiance over 

the entire visible spectrum, the same normalization procedure used by Holyer. 

Six wavelengths that contained 99.95 percent of the information of the entire 

normalized visible spectrum were selected. A linear combination of the radiance at these wavelengths was then fitted to the surface measurements by. the 

least-squares technique to give the model for Sp. The wavelengths selected 

for this analysis were 440, 490, 560, 630, 748, and 867 nanometers. 

A third algorithm was applied to optimize, rather than maximize, the 

information content relative to the Secchi transparency measurement. Using 

a procedure analogous to that used with chlorophyll, this optimization was 

performed selecting wavelengths that showed high correlation between radiance and turbidity and relatively low correlation between the respective 

radiance measurements. Unnormalized radiance data, radiance normalized by 

a wide band in the blue (390 to 430 nanometer) region of the spectrum, and 

radiance normalized by a wide band in the infrared (911 to 1073 nanometer) 

region were examined in the correlation analysis. Figures JO(a) and JO(b) 

are graphs of the linear correlation coefficients from the last two analyses 

plotted as a function of wavelength. With correlation coefficients of -0.554, 

0.622, and -0.942, wavelengths of 410, 440, and 550 nanometers were selected 

from the blue normalized data to construct a linear function to calculate the 

remote turbidity measurements. Correlation of 410 to 440 nanometers was only 

-0.172, whereas the 410- to 570-nanometer correlation was 0.817 and the 440- 

to 570-nanometer correlation was 0.902. The unnormalized and infrared 

normalized analyses indicated very little correlation. 

Analysis Results and Evaluations 

As had been anticipated, the remote measurement of turbidity corresponding to the Secchi transparency proved to be more difficult than the 

chlorophyll-a measurement, and the results were less satisfactory. The 

three differ%t turbidity calculations with their widely divergent coefficients yielded very similar results. All followed the surface measurements 

well except in areas where Secchi extinction depths of 25 to 30 meters were 

reported. 

Of the three sets of calculations, the linear function of the simple 

ratio of radiance (eq. (4)) gave the best results, with an rms error of 

4.5 meters over the range of 3.2 to 28 meters for 19 points, of which 8. 

served as calibrations for the function. For the 11 points not used in 

calibration, the rms error was 5.6 meters. This value should be used with 

caution because of nonrandom selection of points for calibration and for 

evaluation. Table III contains the surface measurements, the three sets 

of remote measurements, and the respective deviations. 

Figure 11 is a plot of the Secchi extinction depth along flight line 

2 (east-west) and includes the surface measurements. The turbidity was 

computed using the simple ratio technique described by equation (4). The 

28 
Wavelength, nm 

1.0 

-. 5 

-1.0 I I I I I I I I I I I 

1 400 450 500 550 6KI 650 750 850 950 1050 1150 

Wavelength, nm 

(a) Normalized by mean blue radiance 

(390 to 430 nanometers). 

(b) Normalized by mean infrared radiante (911 to 1073 nanometers). 

Figure JO.- Linear correlation of Secchi transparency with radiance. 

29 
TABLE III.- EVALUATION OF SECCHI TRANSPARENCY MEASUREMENTS 

(Values in meters) 

Suiface 

measurement 

aSR bAR CS P bAP dSC bAC 

20 

eJ3 

9 

e4 

eJ8 

eJ7 

e12 

28 

e8 

e4 

e99 

6 

5 

:; 

8 

5 

17 

16 

4.6 

5 

12 

16 

8 

3 

14 

12 

1.8 

3:; 

3.4 

2.7 

i-i 

7:6 

.6 

1.5 

.3.4 

1.5 

.6 

15 

4.9 

0 

1.5 

P:i 

27 

22 

12 

15 

14 

5 

16 

19 

I.1 

9 

12 

8 

JO 

15 

11 

5 

18 

19 

2.4 

5:: 

.6 

ii:: 

8.8 

5:; .9 

1.5 

6.1 

2.7 

16.8 

5.8 

3.0 

0 

2: 

20 

ii 

5 

1’: 

16 

19 

9 

8 

6 

8 

12 

16 

; 

14 

12 

0.3 

5:; 

.9 

.9 

K 

818 

1.5 

1.8 

1.2 

0 

3.4 

14.3 

4.9 

:: 

5:: 

rms error 4.5 5.6 4.8 

asR = a JR420/R570 + a2* 

b/+,, = magnitude of deviation of Sm from surface measurements. 

6 

CS P = CanRAn/<R> (Xn determined by principal-factor analysis). 

corre;d%i;nt~~~~~~~~~?~~. 

30> (An determined by examination of linear 

ecalibration point. 

30 
0 

- Remote measurement 

0 Surface measurement 

0 Surface measurement 

used for calibration 

-I.-- 

41’fZ. 61 ‘f331 kd44, 

I I I I 

20 (11) 102 I551 122 (66) 143 (77) 163 (88) 

Distance, km In. mi.) 

Figure JJ.- Secchi transparency along flight line 2. 

31 
values computed using this function were then applied to generate a turbidity contour map of the test area (fig. 12(a)). A contour map based on 

surface measurements of the same parameter (fig. 12(b)) is included for 

comparison. Although basic similarities exist, the absence of the very 

low turbidity values (high Secchi transparencies) in the remote data has 

drastically distorted the appearance of the remote map. 

There are‘two likely explanations for discrepancies, apart from the 

most basic problem of the complexity of the phenomena reflected in inaccuracy of the model. Because of the criteria used in selecting calibration 

points for the remote measurements, which included a maximum time difference 

of 3 hours between surface and remote sampling, no calibration points had 

Secchi depths greater than 17 meters. This fact introduces an uncertainty 

of unknown magnitude into measurements outside the calibrated range. The 

rms error for all points having Secchi depths less than or equal to 20 meters 

is 2.3 meters. Also, reports from the surface observers indicated 'that water 

conditions were changing over the test area. It is thus possible that the 

sea conditions changed during the time between surface and remote sampling. 

The Secchi measurement is a subjective parameter that is influenced by 

factors external to the water turbidity. These factors include cloud cover, 

Sun elevation, surface roughness , ,shading of the water surface or Secchi 

disk, and the eyes of the observer. In an analysis of Secchi measurements 

made during two scientific cruises, Tyler (ref. 14) observed that field 

conditions may exert "an undue influence on the Secchi disk experiement." 

The use of Secchi extinction depths as a measure of turbidity therefore 

necessitates the tolerance of undetermined externpl influences on the 

turbidity measurement. 

A factor that must be considered in examining the surface measurements 

is the basic measurement procedure. The instructions for making the Secchi 

measurement specified that it be made on the shady side of the boat. Although 

there is some controversy over this question, Tyler (ref. 14) points out that 

measurements made on the shady side of the boat are influenced by the shadow 

of the boat below the surface. Discontinuities in the measurement occur at 

the point at which the extinction depth drops below the shadow of the vessel. 

Obviously, elevation of the Sun, orientation of the surface vessel with 

respect to the Sun, and size of the vessel all introduce perturbations into 

the measurements of turbidity when the reading is made on the shady side of 

the boat. Furthermore, discussion with some of those making the measurements 

during the experi.ment reveals that the detailed procedure for taking the reading was not strictly followed - that some measurements were made on the sunny 

side of the boat and some on the shady side. 

ANALYSIS APPLICATION 

The motivation for this work was principally to support the National 

Marine Fisheries Service in their SkyJab/Gamefish Experiment. The 

chlorophyll-a concentration measurements, the turbidity estimation, and the 

surface tempzrature measurements made from aircraft were therefore promptly 

transmitted to the Principal Investigator for the experiment. The data were 

used by him in a white marlin distribution model that had been developed 

32 
(a> Remote measurements. 

Figure 12.- Analysis of turbidity data. Values are in meters; contour 

interval : 2.5 meters. 

33 

IllI llllll 
(b) Surface measurements (Secchi extinction). 

Figure 12.- Concluded. 

34 
from surface data,taken the day before the remote data acquisition mission. 

The prediction of the model for white marlin distribution the day of the 

mission, using these remote data in addition to other parameters that were 

not remotely sensed, was much less successful than the predictions made using 

only the surface measurements. Reasons for this include decreased precision 

of the measurements, more extensive -interpolation between flight lines, and 

the "instantaneous" nature of the remote measurements as opposed to the full 

day of fishing and surface sampling. The most important reason may be that 

the number of samples which could be used in the model with the remote data 

was very small for performance of a statistical analysis yielding significant 

results. 

That some of the parameters of the white marlin distribution model 

can be sensed remotely from aircraft flying at 3000 meters is significant 

to the fisheries investigation. At this relatively low altitude, the 

usefulness of the data in monitoring applications would be restricted by 

limited coverage, but if the techniques described here can be applied to 

synoptic data acquired by satellites, the utility of the remote measurement 

of these parameters will be greatly enhanced. 

Savastano (ref. 1) found that a model based directly on the water 

color as measured with the 5192 multispectral scanner was very successful 

in predicting the locations of the white marlin. If the modeling performed 

by the National Marine Fisheries Service for white marlin distribution 

proves to be valid and if similar models can be developed for other gamefish, satellite-acquired oceanographic data may be used in living marine 

resource-management applications. 

CONCLUSIONS AND PROGRAM EVALUATION 

The following capabilities have been demonstrated by the work reported. 

1. Thermal radiance data acquired with different types of sensors on 

platforms at different altitudes can be combined to generate a composite 

surface temperature map. 

2. Chlorophyll-a concentration can be calculatid from spectral radi- 

-ante measurements witF an accuracy of 0.4 mg/m3 over a range of 0 to 5 mg/m3. 

3. Secchi extinction depth can be derived from spectral radiance 

measurements. An accuracy of better than 5 meters over a range of 2 to 

30 meters was achieved with this data set. 

The results of the remote thermal mapping and turbidity measurement are 

not as good as had been desired, whereas the remote chlorophyll-a measurement 

surpassed the accuracies achieved in previous experiments. The sijccess of the 

statistical approach to the remote measurement of chlorophyll ,indicates that, 

if an ocean system to .be studied is sufficiently free from interfering factors 

such as Gelbstoffe or greatly varying concentrations of sediment or plant 

fragments, such an approach should be considered because of its simplicity. 

35 
- . . .-.-.._ ._- ..-.. ..-_.. . . 

., ~~ pgP 

The thermal measurement suffered from atmospheric variation over the 

study area, and the results of this analysis show that the accuracy of 

this technique is limited by this variation. The turbidity measurement 

indicates that a more thorough analysis of the optics of the water system 

is in order before pursuing the problem further. A statistical model would 

probably be appropriate if it could be formulated in terms of the physical. 

phenomena,. as opposed to the approach taken here. The Secchi extinction 

depth measurement itself is probably not the best measurement of the turbidity of the sea. It would probably be better to use measurements of the . 

beam attenuation coefficient and the diffuse attenuation coefficient. AJthough requiring much more complex measurements, the volume scattering coefficient has good potential for application in remote measurement studies. 

Factors such as the distribution of indices of refraction of suspended 

particles, particle size distribution, concentration of particles, and pigmentation of particles should be studied with respect to remote water color 

analysis. Such detailed experimental work will bring a better understanding 

of the influence of basic physical phenomena on water color and facilitate 

the measurement of some form of the turbidity parameter by remote techniques. 

Lyndon B. Johnson Space Center 

National Aeronautics and Space Administration 

Houston, Texas, August 23, 1977 

640-03-00-00-72 

36 
REFERENCES 

1. Sdvastano, Kenneth; Pastula, E.; Woods, E. G.; and Failer, K. H.: 

Preliminary Results of Fisheries Investigation Associated With 

Skylab-3. Proceedings of the Ninth International Symposium on 

Remote Sensing of Environment, vol. 2, National Oceanic and 

Atmospheric Administration, 1974, pp. 1013-1032. 

2. Boudreau, Robert D.: A Radiation Model for Calculating Atmospheric 

Corrections to Remotely Sensed Infrared Measurements, Version 2. 

NASA TM X-69939, 1974. 

3. Worthington, H. T.: .Remote Measurement of Surface Water Temperatures 

in Coastal Waters. NASA Earth Resources Laboratory Report No. 085, 

Mississippi Test Facility (Bay St. Louis, Miss.), 1973. 

4. Clarke, George L.; Ewing, G. C.; and Lorenzen, C. J.: Spectra of 

Backscattered Light From the Sea Obtained From Aircraft as a Measure 

of Chloroph 11 Concentration. 

1970, pp. f 19-1121. 

Science, vol. 167, no. 3920, Feb. 13, 

1 

5. Yentsch, C. S.: The Influence of Phytoplankton Pigments on the Color 

of Sea Water. Deep-Sea Research, vol. 7, 1960, pp. J-9. 

6. Cox, Charles; and Munk, W. : Slopes of the Sea Surface Deduced From 

Photographs of Sun Glitter. Bull. Scripps Institute of Oceanography, vol. 6, 1956, pp. 401-487. 

7. Weldon, J. W.: Remote Measurement of Water Color in Coastal Waters. 

NASA TM X-69784, 1974. 

8. Arvesen, John C.; Millard, J. P.; and Weaver, E. C.: Remote Sensing 

of Chlorophyll and Temperature in Marine and Fresh Waters. Astron. 

Acta, vol. 18, 1973, pp. 229-239. 

9. Hovis, Warren A.; Forman, M. L.; and Blaine, L. R.: Detection of 

Ocean Color Changes From High Altitude. NASA TM X-70559, 1974. 

JO. Thomann, Gary C.: Remote Measurement of Salinity in an Estuarine 

Environment. NASA lM X-73049, 1975. 

11. Woods, E, G.; and Cook, P. C.: Skylab Oceanographic Gamefish 

Project. National Marine Fisheries Service, Mississippi Test 

Facility (Bay St. Louis, Miss.), 1973. 

12. Determination of Photosynthetic Pigments in Sea Water. SCOR-UNESCO, 

New York, N.Y., 1969. 

13. Daughtrey, Kenneth R.: Techniques and Procedures for Quantitative 

Surface Water Temperature Surveys Using Airborne Sensors. NASA 

Earth Resources Laboratory Report No. 080, Mississippi Test 

Facility (Bay St. Louis, Miss.), 1973. 

37 
14. Tyler, John E.: The Secchi Disc. Limnology and Oceanography, 

vol. XIII, no. 1, Jan. 1968, pp. l-6. 

38 
1. Report No. 2. Government Accession No. 

> L: 

3. Recipient’s Cat&g No. 

-NASA RP-1012 

4. Title and Subtitle 

- 

.5. Report D& 

REMOTE SENSING OF OCEANIC PARAMETERS. . .Novemb& 1977 : 

DURING THE SKYLAB/GAMEFISH EXPERIMENT 6. Performing Organization &de 

JSC- 11563 

7. Author(s) 8. Performing Organization Rcpat No. 

Kenneth H. Faller JSC S-468 

10. Work Unit No. 

9. &t-forming Organization Name and Address 

Lyndon B. Johnson Space Center 

Houston, Texas 77058 

]] 

12. Sponsoring Agency Name and Address Reference Publication 

National Aeronautics and Space Administration 14. Sponsoring Agency Co& 

Washington, D. C. 20546 

15. Supplementary Notes 

IS. Abavaa 

Efforts to demonstrate the feasibility of using remotely acquired information to 

assess and monitor the distribution of oceanic gamefish are described. Data 

supplied by Skylab and two aircraft flying at different altitudes surveying an 

area in the Gulf of Mexico with thermal and optical radiometers and cameras 

were used in conjunction with oceanographic data provided by surface vessels to 

explore a relationship between oceanographic parameters and remotely acquired 

data. Thermal scanner imagery and precision radiometric thermometer data 

obtained by the two aircraft are combined to provide a composite surface temperature 

map of the test area, Spectral radiometer data were studied in conjunction with 

surface measurements of chlorophyll-a and turbidity, and several models were 

developed which predicted these two oceanic parameters from the radiance data. 

Results compare favorably with those obtained with similar models and data 

collected over the Mississippi Sound. Contour maps of the chlorophyll-a content 

and turbidity were developed from the best chlorophyll and turbidity models and 

from surface measurements. Basic problems concerning the remote measurement 

of the Secchi extinction depth are discussed, and suggestions are made for improving the remote measurement turbidity. 

17. Key Words (Suggested by Author(s)) 18. Distribution Statement 

Remote- sensing technology 

0ceani.c parameters STAR Subject Category: 

Chlorophyll- a 48 (Oceanography) 

Secchi extinction depth 

Turbidity -w 

19. Ssurity Ciassif. (of this report) 

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Unclassified Unclassified 43 $4.00 

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