ArticlePDF Available

A system to measure the data quality of spectral remote sensing reflectance of aquatic environments

Journal of Geophysical Research: Oceans

October 2016

DOI:10.1002/2016JC012126

Authors:

Jianwei Wei

NOAA/STAR; Global Science & Technology Inc

Zhongping Lee

University of Massachusetts Boston

Spectral remote sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio-optical properties. Since Rrs from in-situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs datasets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD dataset as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ datasets can improve the development and validation of bio-optical algorithms and its application to ocean color satellite data can improve the short- and long-term products by objectively excluding questionable Rrs data. This article is protected by copyright. All rights reserved.

. Mean Normalized Remote-Sensing Reflectance Spectra (nR rs ) for the 23 Optical Water Types

…

. Coefficient of Variation (%) of the nR rs Spectra and Corresponding R rs Spectra for Each Water Type

…

Mean normalized remote-sensing reflectance spectra characteristics of 23 water types.

…

(a) Frequency distribution of the scores assigned to the simulated R rs data. The scores are given in fractional numbers with the numerators referring to the numbers of good-quality wavebands. (b) Ratio distribution of R rs (412)/R rs (443) against R rs (488)/R rs (547). The corresponding 23 water types are denoted in colors and the spectra identified with low QA scores ( 0.5) are denoted with cross symbols in black.

…

Quality evaluation of the MODISA ocean color data in US East Coasts and Mid-Atlantic Bight. (a) Scores of the satellite ocean color data (September 2015, A2015258180000.L2_LAC). (b) Comparison of the satellite and in situ ocean color matchup in Massachusetts Bay (in situ station is indicated by a '''' symbol in Figure 7a; position: 42.40498N, 270.54698W; sampling time: 17:07 UTC). (c) MODISA measured ''bad'' R rs spectra at the low-score pixels (indicated by '' • '' in Figure 7a).

…

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Content may be subject to copyright.

Content uploaded by Jianwei Wei

Content may be subject to copyright.

RESEARCH ARTICLE

10.1002/2016JC012126

A system to measure the data quality of spectral

remote-sensing reflectance of aquatic environments

Jianwei Wei

, Zhongping Lee

, and Shaoling Shang

Optical Oceanography Laboratory, School for the Environment, University of Massachusetts Boston, Boston,

Massachusetts, USA,

State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China

Abstract Spectral remote-sensing reﬂectance (R

,sr

) is the key for ocean color retrieval of water

bio-optical properties. Since R

from in situ and satellite systems are subject to errors or artifacts,

assessment of the quality of R

data is critical. From a large collection of high quality in situ hyperspectral

data sets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate

the quality of an individual R

spectrum. This QA scheme consists of a unique R

spectral reference and a

score metric. The reference system includes R

spectra of 23 optical water types ranging from purple blue

to yellow waters, with an upper and a lower bound deﬁned for each water type. The scoring system is to

compare any target R

spectrum with the reference and a score between 0 and 1 will be assigned to the

target spectrum, with 1 for perfect R

spectrum and 0 for unusable R

spectrum. The effectiveness of this

QA system is evaluated with both synthetic and in situ R

spectra and it is found to be robust. Further

testing is performed with the NOMAD data set as well as with satellite R

over coastal and oceanic waters,

where questionable or likely erroneous R

spectra are shown to be well identiﬁable with this QA system.

Our results suggest that applications of this QA system to in situ data sets can improve the development

and validation of bio-optical algorithms and its application to ocean color satellite data can improve the

short-term and long-term products by objectively excluding questionable R

data.

1. Introduction

Remote-sensing reﬂectance (R

, units: sr

) is deﬁned as the ratio of water-leaving radiance (L

, units: mW

) to downwelling irradiance just above the surface (E

, units: mWcm

). R

is a critical

optical property for deriving water’s optical and biogeochemical properties that include chlorophyll acon-

centration [O’Reilly et al., 1998], colored dissolved organic material (CDOM) absorption coefﬁcient and partic-

ulate backscattering coefﬁcient [IOCCG, 2006; Lee et al., 2002; Mannino et al., 2008; Wei and Lee, 2015], etc.

Reliable retrieval and appropriate interpretation of these properties, to the ﬁrst order, demand accurate R

data.

cannot be directly measured in the ﬁeld or obtained remotely, rather derived from two properties (L

and E

) obtained independently. Errors in R

data from ﬁeld measurements can be related to instrument

platforms, strategies of deployment, data processing as well as the ambient environments [Bailey et al.,

2008; Hooker et al., 2002; Mueller et al., 2003; Toole et al., 2000; Zibordi et al., 2002]. The in-water approach

measures the in-water downwelling irradiance (E

, units: mWcm

) and upwelling radiance (L

, units:

mWcm

) separately, and then the in-water radiance is propagated to right above the water sur-

face to obtain the water-leaving radiance. The known sources of measurement uncertainty could originate

from instrument calibrations [Bailey et al., 2008; Wei et al., 2012], instrument self-shading effect [Gordon and

Ding, 1992], wave-focusing effects [Wei et al., 2014; Zibordi et al., 2004], propagation of L

(z) to L

[Wei et al.,

2015], and reﬂection or shading noise from the ship hull. On the other hand, R

data from the above-water

approach are likely subject to uncertainties due to sea surface reﬂection [Lee et al., 2010a; Mobley, 1999],

bidirectional reﬂectance distribution function (BRDF) [Lee et al., 2011; Morel and Gentili, 1996; Voss and Morel,

2005], sky cloudiness, sun glints, and white caps [Gordon and Wang, 1994a]. The skylight-blocked approach

(SBA) [Lee et al., 2013; Tanaka and Sasaki, 2006] can directly measure L

(then R

), but to some degree it is

also subject to self-shading error. In addition to these systematic errors, there are likely unidentiﬁed errors

and uncertainties or artifacts during ﬁeld observations resulted from uncontrollable ﬁeld environment.

Key Points:

A QA system is developed for

spectral remote-sensing reﬂectance

The system consists of a reference

and a score metric

It is applicable to both remotely

sensed and in situ ocean color data

Correspondence to:

J. Wei,

jianwei.wei@umb.edu

Citation:

Wei, J., Z. Lee, and S. Shang (2016),

A system to measure the data quality

of spectral remote-sensing reﬂectance

of aquatic environments, J. Geophys.

Res. Oceans,121, doi:10.1002/

2016JC012126.

Received 5 JUL 2016

Accepted 19 OCT 2016

Accepted article online 25 OCT 2016

C2016. American Geophysical Union.

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 1

Journal of Geophysical Research: Oceans

PUBLICATIONS

Obtaining accurate R

spectra from space is even more challenging. The quality of R

spectra derived from a

satellite ocean color sensor is signiﬁcantly impacted by the efﬁcacy of atmospheric corrections [Gordon and

Wang, 1994b; IOCCG, 2010]. For example, at the top of atmosphere (TOA) about 90% of the total radiance

received by a satellite sensor is contributed from the atmosphere. Small errors in the atmospheric correction

will result in large errors in the derived R

. In coastal regions, atmospheric correction of satellite ocean color

images is a daunting task because of the complex atmospheric and water properties [IOCCG, 2010].

Methods to quality control (QC) R

data are diverse and system dependent [Bailey and Werdell, 2006;

McClain et al., 1992; Ruddick et al., 2005; Werdell and Bailey, 2005; Zibordi et al., 2009b]. The in situ R

meas-

urements usually go through certain QC steps during data conversion (e.g., absolute calibration) and subse-

quent data postprocessing (e.g., tilt ﬁltering and data binning); these processes are often embedded in the

processing software provided by the instrument manufacturers. The difﬁculty is, however, that environmen-

tal disturbance to the measurements and artifacts cannot be fully accounted for in these postprocessing

steps. When compiling the NASA bio-Optical Marine Algorithm Dataset (NOMAD), Werdell and Bailey [2005]

deﬁned and utilized rejection criteria to eliminate abnormal and spurious data. For instance, the observed

spectral surface irradiances were compared with modeled clear sky values, and those stations were consid-

ered questionable and discarded when the in situ value exceeded the modeled value by more than 33%.

Ruddick et al. [2005] discussed the QC of above-water R

measurements at near-infrared (NIR) bands based

on the spectral similarity in the NIR domain. Their method is more applicable to coastal turbid waters and

does not provide further information on the quality of R

spectra in the visible domain. In processing the

AERONET-OC data, Zibordi et al. [2009b] proposed a three-level scheme for the normalized water-leaving

radiance (L

, units: mWcm

) with thresholds including, e.g., L

(k)>20.01 mWcm

to ensure an exclusion of large negative values; L

(412) <L

(443) as commonly met in coastal waters

and L

(1020) <0.1 mWcm

to exclude reﬂecting obstacles along the optical path between the

instrument and the water surface; and then inspection of the spectral consistency using statistical methods

[D’Alimonte and Zibordi, 2006]. A QC procedure was also developed for satellite ocean color data [McClain

et al., 1992]. This method consists of a system of processing ﬂags for Level-2 products. A total of 31 ﬂags are

currently employed and applied pixel by pixel, including ATMFAIL, STRAYLIGHT, HIGLINT, COASTZ, etc. [Rob-

inson et al., 2003]. These ﬂags use predetermined thresholds. For example, the glint ﬂag is activated when

the normalized sun glint reﬂectance is found exceeding 0.005. Pixels with stray light contamination from

adjacent, bright sources such as coasts and clouds are ﬂagged in Level-2 products and masked in Level-3

products. Further, to validate satellite R

against in situ data, Bailey and Werdell [2006] introduced a scheme

by considering the spatial uncertainty, coincidence determination, viewing and solar zenith angles, and sta-

tistical conﬁdence of the mean pixel values. To reduce the R

errors from satellite sensor digitization noise,

Hu et al. [2005] recommended the median R

values over a box of 3 33 pixels be obtained to reduce

uncertainties in satellite ocean color products.

A common practice to evaluate the accuracy of the R

data obtained from remote-sensing platforms is to

compare R

data with in situ measurements wavelength-by-wavelength. Statistical measures (e.g., R

and

relative error, etc.) are often obtained as an indicator of the accuracy of the R

data obtained remotely.

Such an approach characterizes the overall quality of the remotely obtained R

data and provides an indica-

tion of the systematic performance of the instrument and data-processing system for each spectral band

[Bailey and Werdell, 2006; Zibordi et al., 2009a]. This way of evaluation treats the R

property similarly as

those of biogeochemical properties such as the chlorophyll aconcentration, and considers R

at each wave-

length as an independent property. Such scatter-plots or linear regressions wavelength by wavelength can-

not tell the quality of each R

spectrum, however. Note that for remote-sensing inversions [IOCCG, 2000,

2006], it seldom uses R

at one band to derive in-water properties, rather it uses multiple bands or the

entire R

spectrum. Therefore, it is important and imperative to measure objectively the quality of each R

spectrum obtained from any platforms.

In this study, a novel system is developed for objective quality assurance (QA) of an individual R

spectrum.

Based on in situ R

data obtained from coastal waters and clear oceanic waters, we deﬁne the domain of

variability of the spectral shapes and amplitudes of R

spectra in different water types using a unique opti-

cal water type classiﬁcation scheme. A score metric ranging from 0 (lowest quality) to 1 (highest quality) is

further devised to measure the quality of any target R

spectrum. Examples of applications of this QA sys-

tem to synthetic, in situ, and satellite data are also presented.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 2

2. Development of the QA system

2.1. In situ Measurements

In situ hyperspectral R

spectra were collected from a wide range of marine environments, with chlorophyll

aconcentration ([CHL], units: mg m

) varying from as low as 0.02 mg m

in the subtropical gyres to tens

of mg m

in the coastal regions (Figure 1). The data were retrieved by above-water approach, SBA [Lee

et al., 2013] and depth-proﬁling approach, with a wavelength range of 400–800 nm and 10 nm resolution.

In total, there are 958 R

spectra adopted in subsequent analyses. The R

data from the three approaches

account for about 83%, 11%, and 6%, respectively, of the whole data sets. The aggregate data of these mea-

sured hyperspectral R

spectra are illustrated in Figure 2. In this study, R

of nine visible wavelengths (412,

443, 488, 510, 531, 547, 555, 667, and 678 nm) are extracted from the hyperspectral database, and are

referred to as the ‘‘reference’’ data hereafter. It is noted that many of these wavelengths are already incorpo-

rated in heritage and currently operational ocean color satellite sensors including the Sea-viewing Wide

Field-of-view Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), Medium Resolu-

tion Imaging Spectrometer (MERIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat 8.

2.1.1. R

Data From Above-Water Approach

The primary data for this R

database were obtained from the Gulf of Mexico, Adriatic Sea, South China Sea, North

Paciﬁc Gyre, Mississippi River plume, Yangtze River plume, as well as optically shallow waters in the West Florida

Shelf and around Key West (Florida) (Fig-

ure 1). The range of [CHL] is from 0.02 to

80 mg m

; the range of suspended sed-

iment matter is from the sensors’ lower

detection limit up to 100 g m

;the

range of bottom depth is from 1mto

>1000 m with substrates of seagrass,

sand, and coral reef. The instruments

used are all calibrated commercial radio-

meters from ultraviolet bands to near-

infrared bands including Spectrix (400–

850 nm), GER (350–1050 nm), and RAM-

SES (320–950 nm) and have a ﬁne pixel

dispersion of 2–3 nm with half-value-

bandwidth of 1–1.5 nm. The data-

processing scheme follows the NASA

ocean optics protocol [Muelleretal., 2003]

and the spectral optimization scheme of

Lee et al. [2010a] for reﬁnement.

Figure 1. Locations of ﬁeld observation of remote-sensing reﬂectance.

Figure 2. Spectra of remote-sensing reﬂectance used in this study.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 3

2.1.2. R

Data From Floating Instrument With the SBA

Another source of hyperspectral R

was obtained from a ﬂoating system incorporating the SBA [Lee et al.,

2013] in coastal waters including Massachusetts Bay, northern Gulf of Mexico, and the Caribbean Sea (south

of Puerto Rico) (Figure 1). The SBA system, as its name implies, consists of a hyperspectral radiometric proﬁl-

er (350–800 nm, Satlantic Inc., Halifax, Canada) and a skylight-blocking apparatus attached to the radiance

sensor. It is thus able to directly measure L

in a continuous manner and provides a time series of R

and

allows a reliable evaluation of the R

uncertainty. The time series measurements of R

spectra were further

inspected based on the R

data at a single band of 698 nm following the procedure of Wei et al. [2015].

Basically, a density function was ﬁrst determined for the R

(698) time series, and those deviating by 630%

from the ﬁrst mode of the density function were then removed. This procedure can effectively eliminate

those data potentially contaminated by sea surface reﬂected light when the cone is suspended above the

surface and/or immersion of the radiance sensor head within the water at high seas. After ﬁltering, the

median R

spectrum is derived for the remaining R

data and considered as true R

for that water body.

For the waters encountered and the spectral range (400–700 nm) considered, the instrument self-shading

error is found quite small (less than 5%) based on Gordon and Ding [1992].

2.1.3. R

Data From Hyperspectral Profiler

A third source of hyperspectral R

data was retrieved from the North Paciﬁc Gyre and the South Paciﬁc Gyre

using a free-fall hyperspectral proﬁler (HyperPro, Satlantic Inc., Halifax, Canada). The data-processing proce-

dure follows the NASA ocean optics protocols [Mueller et al., 2003]. Speciﬁcally, the depth proﬁles of upwell-

ing radiance are propagated to right above the water surface to derive the water-leaving radiance L

, using

the determined diffuse attenuation coefﬁcient for upwelling radiance, and the upwelling radiance transmit-

tance [Austin, 1974; Wei et al., 2015]. Then the mean remote-sensing reﬂectance is derived as the ratio of L

to E

. Because of extreme clarity, the self-shading effect of this system for the 400–700 nm range is negligi-

ble in these waters.

2.2. Clustering of Optical Water Types

The reference R

spectra were ﬁrst normalized by their respective root of sum of squares (RSS),

nRrsðkÞ5Rrs ðkÞ

i51

RrsðkiÞ2

1=2(1)

where the index Nrepresents the total number of wavelengths, varying from 1 to 9 and k

corresponds to

the wavelengths of 412, 443, 488, 510, 531, 547, 555, 667, and 678 nm. The nR

spectra vary over the range

between 0 and 1, while it retains the ‘‘shapes’’ pertaining to the original R

spectra, i.e., the band ratios of

(k) remain the same as R

(k).

The number of data clusters kwas evaluated using the gap method [Tibshirani et al., 2001]. The gap value is

deﬁned as:

GAPnðkÞ5E

nlog ðWkÞ½2log ðWkÞ(2)

where nis the sample size, kis the number of clusters being evaluated, and W

is the pooled within-cluster

dispersion measurement, with

Wk5X

r51

2nr

Dr(3)

where n

is the number of data points in cluster r, and D

is the sum of the pair-wise distances for all points

in cluster r. The expected value E

*[log(W

)] is determined by Monte Carlo sampling from a reference distri-

bution, and log(W

) is computed from the sample data. According to the gap method, the optimum cluster

number of the nR

data is determined as 23. Interestingly, this number is nearly the same as that of Forel-

Ule water type classes developed 100 years ago [Arnone et al., 2004].

The unsupervised method, K-means clustering technique, was further used to group the nR

spectra.

K-means clustering, or Lloyd’s algorithm [Lloyd, 1982], is an iterative, data-partitioning algorithm that assigns

nobservations to exactly one of kclusters deﬁned by the centroids, where kis chosen before the algorithm

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 4

starts (as shown above, k523). We note that the cosine distance was used in the clustering, which is one

minus the cosine of the angle between the nR

spectrum x(a reference) and c(a target),

dðx;cÞ512xc0

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

xx0

ðÞcc0

ðÞ

p(4)

Each centroid is the mean of the nR

spectra in that cluster, after normalizing those points to unit Euclidean

length. The error function to be minimized is the total within-cluster sum of squares.

The nR

spectra as clustered into the 23 optical water types are illustrated in Figure 3, with the centroids of

spectra highlighted for each water type. Within each water type, the nR

spectra are very similar to

each other and tightly distributed about the centroids. The centroid nR

spectra of the 23 water types are

illustrated in Figure 4 (with values tabulated in Table 1). These ‘‘mean’’ reference spectra represent a large

Figure 3. Clustering of 23 optical water types. The gray-black curves represent the normalized R

spectra classiﬁed into each water type.

The mean R

spectra are highlight in red, with the error bars characterizing the maximum and minimum values. And in each water type,

the total numbers of available R

spectra are also given.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 5

range of waters with [CHL] varying

from 0.02 mg m

to tens mg m

Corresponding to Figure 3, the upper

limits and lower limits of all nR

spec-

tra at discrete bands have been identi-

ﬁed for each water type and are

tabulated in appendix (see Appendix A

Tables A1 and A2). The identiﬁed nR

mean spectra, together with the upper

boundary spectra and lower boundary

spectra form the core of the QA system

and will be used in the following sec-

tions to perform the QA of an individu-

al R

spectrum.

It is necessary to emphasize that the

above clustering procedure is focused

on the spectral shapes, and is distinct

from other exercises of optical water

type clustering which often used the

Euclidean distance of R

spectra [Le

et al., 2011; M

elin and Vantrepotte,

2015; Moore et al., 2009, 2001]. The

Euclidean distance, including transformed and weighted forms such as Mahalanobis and likelihood distan-

ces, is inherently insensitive to the shapes of the spectral pattern [Sohn et al., 1999].

2.3. Water Type Matchup and Comparison

To evaluate or quantitatively measure the data quality of a target reﬂectance spectrum, R

rs, a four-step pro-

cedure is developed:

Step 1 is to match up R

rs(k0) with nR

(k) with regard to the wavelengths. If R

rs has more spectral bands than

that of nR

, we will only choose the same wavelengths with nR

for further analysis. If R

rs has fewer

Figure 4. Mean normalized remote-sensing reﬂectance spectra characteristics of

23 water types.

Table 1. Mean Normalized Remote-Sensing Reﬂectance Spectra (nR

) for the 23 Optical Water Types

Water Type

Wavelength (nm)

CHL (mg m

)

412 443 488 510 531 547 555 667 678

1 0.738 0.535 0.335 0.169 0.112 0.084 0.072 0.007 0.007 0.06

2 0.677 0.534 0.394 0.225 0.156 0.120 0.104 0.011 0.010 0.10

3 0.608 0.521 0.436 0.280 0.204 0.161 0.140 0.016 0.017 0.16

4 0.510 0.478 0.462 0.348 0.279 0.230 0.206 0.029 0.031 0.35

5 0.430 0.436 0.472 0.386 0.326 0.278 0.253 0.038 0.041 0.52

6 0.363 0.387 0.458 0.408 0.368 0.328 0.304 0.042 0.047 0.76

7 0.309 0.355 0.451 0.419 0.392 0.356 0.335 0.048 0.052 0.94

8 0.276 0.315 0.415 0.415 0.414 0.394 0.378 0.062 0.067 1.51

9 0.349 0.335 0.391 0.386 0.387 0.382 0.378 0.090 0.118 1.63

10 0.228 0.275 0.383 0.407 0.430 0.427 0.420 0.079 0.082 2.35

11 0.291 0.276 0.342 0.367 0.401 0.424 0.437 0.129 0.181 3.28

12 0.187 0.241 0.342 0.382 0.427 0.450 0.461 0.147 0.151 3.80

13 0.173 0.220 0.342 0.393 0.447 0.462 0.464 0.093 0.096 4.61

14 0.188 0.235 0.319 0.363 0.412 0.445 0.463 0.215 0.214 4.77

15 0.143 0.191 0.306 0.365 0.434 0.472 0.492 0.170 0.180 6.57

16 0.181 0.200 0.261 0.307 0.365 0.410 0.437 0.359 0.374 7.25

17 0.174 0.203 0.283 0.334 0.399 0.446 0.472 0.272 0.280 7.07

18 0.142 0.169 0.279 0.349 0.439 0.498 0.525 0.121 0.131 10.41

19 0.050 0.126 0.219 0.277 0.340 0.392 0.423 0.452 0.449 10.81

20 0.117 0.153 0.258 0.324 0.412 0.477 0.515 0.243 0.259 12.28

21 0.163 0.175 0.249 0.308 0.400 0.490 0.544 0.190 0.217 16.08

22 0.111 0.135 0.226 0.292 0.385 0.463 0.511 0.310 0.329 17.57

23 0.145 0.133 0.176 0.215 0.286 0.423 0.548 0.341 0.449 34.59

Median chlorophyll aconcentration.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 6

wavelengths than the nR

spectra (i.e., N(k0)<9), a subset of nR

(k0) and associated upper boundary spectra

rsU

(k0) and lower boundary spectra nR

rsL

(k0) will be extracted ﬁrst for k0.

Step 2 is the normalization of R

rs spectra following equation (1). For the case of N(k0)<9, the new nR

(k0)

spectra will be rescaled through the normalization procedure of equation (1) so that the RSS of nR

(k’)is

equal to one. Further, the new upper and lower boundary spectra nR

rsU

(k’) and nR

rsL

(k0) will also be rescaled

by the newly rescaled nR

(k0) spectra as below,

nRrsUðkÞ5nRrs UðkÞ

i51

nRrsðkiÞ2

1=2

nRrsLðkÞ5nRrs LðkÞ

i51

nRrsðkiÞ2

1=2

(5)

Step 3 is to assign a water type to the target spectrum by comparing it with the reference nR

spectra. The

spectral similarity between the target spectrum nR

rs and reference spectra nR

are estimated using a spec-

tral angle mapper (SAM) [Kruse et al., 1993],

cos a5X

i51

nR

rs nRrs



ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

i51

nR

rsðkiÞ



i51

nRrsðkiÞ



s(6)

where ais the angle formed between the reference spectrum nR

and the normalized target spectrum nR

rs.

As a spectral classiﬁer, SAM is able to determine the spectral similarity by treating them as vectors in a space

with dimensionality equal to the number of bands, N. The water type of the target spectrum nR

rs is identi-

ﬁed as the one with the largest cosine values (equivalent to the smallest angles).

Step 4 is the computation of QA scores by comparing the target spectrum nR

rs with the upper and lower

boundaries (nR

rsU

and nR

rsL

) of the corresponding water type. The number of wavelengths where nR

rs falling

within the boundaries is counted, and used to derive the total score (C

tot

) for the nR

rs spectrum,

Ctot5Cðk1Þ1Cðk2Þ11CðkNÞ

N(7)

where C

is the wavelength-speciﬁc score, with Nthe total number of wavelengths for both R

rs and Rref

rs .At

wavelength k

, for example, if nR

*(k

) is found beyond either the upper (nR

rsU

)) or lower (nR

rsL

)) bound-

ary of nR

, a score of 0 will be assigned to this wavelength, i.e., C(k

)50; otherwise, C(k

)51. As suggested

by equation (7), the total score C

tot

will vary within the range of [0, 1]. A higher score indicates higher data

quality.

To account for the measurement uncertainty and possible data-processing errors and likely insufﬁcient data

coverage, the original upper boundary and lower boundary are slightly modiﬁed by 60.5%, nR

rsU

5nR

rsU

(1 10.005) and nR

rsL

5nR

rsL

3(1 20.005), respectively. Note that this added range of 0.5% is one order of

magnitude smaller than the projected accuracy for radiance measurement [Hooker et al., 1992].

3. Evaluation of the QA System

3.1. Synthesized R

Spectra

3.1.1. Measurement-Error-Free Data

The R

spectra were synthesized with a database of inherent optical properties (IOP) presented in the Inter-

national Ocean Color Coordinating Group report [IOCCG, 2006]. The synthetic IOP data cover a wide range

of aquatic environments and have been extensively used for model development and validation [Chen

et al., 2015; Salama and Stein, 2009; Wang et al., 2005; Wei and Lee, 2013, 2015; Wei et al., 2016]. The remote-

sensing reﬂectance was simulated by Hydrolight 5.1 [Mobley and Sundman, 2008]. The inelastic scattering

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WEI ET AL. QUALITY ASSURANCE OF R

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including Raman scattering and ﬂuorescence was invoked and included in the model runs. For chlorophyll

ﬂuorescence, a ﬂuorescence efﬁciency of 0.02 was adopted by default in Hydrolight. The chlorophyll aspe-

ciﬁc absorption spectra a

ph(k) were derived as the ratio of a

(k)/[CHL]. Clear skies were assumed with the

solar zenith angles at 308. Sea surface roughness was relatively mild with wind speed of 5 m/s. The scatter-

ing phase function of particles was assumed to follow Fournier-Forand model, with changing backscattering

ratios. The water absorption coefﬁcient from 400 to 550 nm was adopted from Lee et al. [2015]; while the

measurements by Pope and Fry [1997] were used for the other wavelengths. The scattering coefﬁcients of

pure seawater were provided by Morel [1974].

The data quality of simulated R

spectra at nine wavelengths (412, 443, 488, 510, 531, 547, 555, 667, and

678 nm) is assessed with the-above designed score metric model. As shown in Figure 5a, 65% of the spectra

are identiﬁed with the highest quality with scores of 1. Another 18% of the spectra are scored as 8/9, mean-

ing the individual R

spectrum at one wavelength has gone beyond the domain deﬁned by our metrics.

There are another 10% of the R

data found out of the range at two wavelengths. Figure 5b further charac-

terizes the statistics of simulated R

spectra in terms of the two spectral ratios, R

(412)/R

(443) and

(488)/R

(547). The IOCCG data represent a large range of waters varying from water types 1 to 23. And

the relatively low QA scores (in this case, scores 0.5) are found with the spectra in extremely turbid waters

and a few others in relatively clear oceanic waters. These low-score data points can be partly explained by

the limited number of reference spectra used in particular water types in the model development, such as

water types of 17, 19, 21, and 23 (Figure 3).

3.1.2. Error-Disturbed Data

Since the errors in R

data are hardly predictable, we only discussed two simple cases for assessment of the

performance of the QA system. First, we synthesized random-error-disturbed R

spectra. The wavelengths

were randomly chosen from the nine wavelengths of 412, 443, 488, 510, 531, 547, 555, 667, and 678 nm.

Then the percentage errors were randomly set to d55%, 10%, 20%, 30%, or 40%, which are assumed spec-

trally the same for all chosen wavelengths. So the error-disturbed spectra R

rs at wavelengths k

was repre-

sented as R

)* 5R

)3(1 1d). The data quality of R

rs was then examined with the score metric model

and is illustrated in Figure 6a. When the R

spectra are only subjected to small errors (5%, in this case), very

high scores (8/9) are found for 90% of the spectra; 70% of the R

spectra have found with the highest

scores of 1. As expected, with the increasingly larger errors being added more R

spectra show low scores.

It is also noted that a small portion of R

spectra are found assigned to the highest scores of 1 even though

the percentage errors are as high as up to 30% and 40%; this reﬂects the rare situation where the R

spec-

trum is subjected to same percentage errors at all nine wavelengths.

Unlike the random errors considered above, the R

spectra measured from space and in situ are often sub-

ject to spectrally dependent errors [Bailey and Werdell, 2006; Hooker et al., 2002; Hu et al., 2013; Voss et al.,

2010; Zibordi et al., 2009a]. In blue oceanic waters, for example, the percentage errors of R

are more

Figure 5. (a) Frequency distribution of the scores assigned to the simulated R

data. The scores are given in fractional numbers with the

numerators referring to the numbers of good-quality wavebands. (b) Ratio distribution of R

(412)/R

(443) against R

(488)/R

(547). The cor-

responding 23 water types are denoted in colors and the spectra identiﬁed with low QA scores (0.5) are denoted with cross symbols in

black.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 8

signiﬁcant in longer wavelengths because of their extremely small reﬂectance values [Hu et al., 2013]. Like-

wise, the percentage errors of R

spectra in green waters tend to be larger at shorter wavelengths. We syn-

thesized new R

spectra with spectrally related errors. For the data belonging to water types 1–7, a

relatively smaller error (5%, 10%, 15%, 20%, or 30%) and a larger error (correspondingly, 15%, 30%, 50%,

75%, or 100%) are universally added to the shorter wavelengths (412, 443, 488, 510, and 530 nm) and lon-

ger wavelengths (547, 555, 667, and 678 nm), separately. For other water types, we added a relatively larger

error to the shorter wavelengths (412, 443, and 488 nm), a relatively small error to the intermediate wave-

lengths (510, 531, and 547 nm), and relatively large but different error to the longer wavelengths (555, 667,

and 678 nm). Figure 6b illustrates the frequency distribution of the scores for the blue waters (in this case,

water types 1–7), which has shown an increasing number of low-score spectra identiﬁed with increasing

errors being added to R

spectra. Figure 6c describes the data quality for the green and yellow waters; the

same conclusion can also be reached. In particular, the last group of frequency distribution in Figure 6c

mimics the satellite ocean color in coastal waters when the absorbing aerosols are present. For such cases,

the model of score metrics works effectively in identifying them by ﬁnding low scores.

3.2. Satellite Measured R

Spectra

The MODIS Aqua ocean color images were selected and retrieved from NASA ocean color data archive,

which were calibrated and processed by Ocean Biology Processing Group (OBPG) with the latest processing

(R2014). The R

spectra at six wavelengths of 412, 443, 488, 531, 547, and 667 nm were further obtained on

Figure 6. Frequency distribution of the scores for simulated error-disturbed R

spectra. (a) Same errors are added to randomly selected

wavelengths. (b) Blue water R

spectra with different errors added to shorter wavelengths (412, 443, 488, 510, and 530 nm) and longer

wavelengths (547, 555, 667, and 678 nm), separately; each pair of errors are denoted in the format of fractions in the plot. (c) Similar to

(b) but for green and yellow water R

spectra; three errors are added to shorter wavelengths (412, 443, and 488 nm), intermediate

wavelengths (510, 531, and 547 nm) and longer wavelengths (555, 667, and 678 nm), separately.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

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a per-pixel basis. Quality of the R

spectrum of each pixel was then quantiﬁed by the score metrics, and

examples are provided below.

3.2.1. Level-2 Ocean Color on the U.S. East Coasts

An ocean color image collected over the northeastern U.S. coasts on 15 September 2015 was retrieved from

OBPG, and the sky was clear. As would be expected, the distribution of the QA scores reveals that the quali-

ty of R

data is very much location dependent (Figure 7a). The offshore waters in the Mid-Atlantic Bight and

Southern Atlantic Bight are generally characterized by high QA scores, except for those near the shorelines.

From the New York Bight, Gulf of Maine to St. Lawrence River and further north, however, the R

QA scores

are generally lower than 0.5, suggesting questionable R

data for at least half of the evaluated spectral

bands. In particular, the water masses in the center of the Gulf of Maine are of very low QA scores (less than

0.3), indicating highly questionable R

data for this ocean color image. Indeed, these low-score pixels are

generally ﬂagged in Level-2 ocean color products. For this particular image, the ﬂags of ‘‘production failure

(PRODFAIL)’’ are generally evoked and assigned to the pixels in the vast majority of the waters within the

Gulf of Maine.

On the same day, R

was measured with the SBA system in the southwest of Gulf of Maine to validate the

satellite ocean color data (Figure 7a). Comparison of the matchup data indicates that the MODIS Aqua R

spectrum (which has a QA score of 0.5) deviates from in situ matchup R

spectrum (which has a QA score of 1)

increasingly from 488 nm toward 412 nm (Figure 7b). This ﬁnding conﬁrms and validates the low QA score

of the satellite R

spectrum. Note that the MODIS Aqua R

has apparently been underestimated at the

shorter wavelengths, with negative values found at 412 nm, but is fairly reasonable at green and red bands.

Such discrepancies suggest a failure of atmosphere correction for these measurements. The Gulf of Maine is

generally clear and not subject to signiﬁcant contribution of suspended sediments. A likely cause for the

failure of atmospheric correction is the frequent occurrence of absorbing aerosols in the New England

region [Sierau et al., 2006].

It is interesting, and surprising, that the distribution of the QA score of this MODIS Aqua image is found

related to the mesoscale eddy systems of the Gulf Stream: the clear blue waters in the Sargasso Sea are

observed with relatively lower QA scores (<0.7), with extremely low QA score pixels (<0.1) found for a large

Figure 7. Quality evaluation of the MODISA ocean color data in US East Coasts and Mid-Atlantic Bight. (a) Scores of the satellite ocean col-

or data (September 2015, A2015258180000.L2_LAC). (b) Comparison of the satellite and in situ ocean color matchup in Massachusetts Bay

(in situ station is indicated by a ‘‘䉱’’ symbol in Figure 7a; position: 42.40498N, 270.54698W; sampling time: 17:07 UTC). (c) MODISA mea-

sured ‘‘bad’’ R

spectra at the low-score pixels (indicated by ‘‘•’’ in Figure 7a).

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

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water mass stretching northeastward in the east of North Carolina (Figure 7a). These pixels are ﬂagged in

the Level-2 products as perturbation of moderate glints. We retrieved and plotted the R

spectra from this

region and presented Figure 7c, and found that the R

spectra at shorter wavelengths are generally under-

estimated, likely a result of overcorrection of the atmospheric contributions due to glint effects.

Another MODIS Aqua ocean color image was obtained for east U.S coastal and offshore waters on 11

December 2015. The QA score map is shown in Figure 8a. The score distribution has shown very good data

quality for most of the pixels in this scene. The coastal waters in west of the Atlantic Ocean are generally

assigned to very high scores (>0.9), including lower Chesapeake Bay. In situ R

spectrum was obtained on

the same day at a deep water location (off South Carolina) using the SBA system. A comparison of satellite

with in situ R

conﬁrms the good quality of the satellite data for this pixel (Figure 8b).

On the other hand, low-score pixels are also easily found along the edges of cloud patches (the clouds are

masked out and shown in white, north of 288N in the image). Most of these pixels at the edges are ﬂagged

as ‘‘product failure (PRODFAIL)’’ in the Level-2 products. According to the QA score distribution, however,

the pixels with a distance more than a few pixels away from the cloud edges still show very low QA scores

(<0.5). Figure 8c shows the R

spectra of these pixels, where the R

values at the blue wavelengths are

obviously underestimated while R

(670) is likely overestimated.

3.2.2. Level-3 Ocean Color Data in Global Oceans

As examples, the QA metrics was also applied to global Level-3 MODISA daily (A2002188_L3m_9km) and

monthly R

data (A20021822002212_L3m_9km) (Figure 9). Note all questionable pixels as ﬂagged in Level-

2 products have been removed before the Level-3 composites. As a result, the percentage of pixels with

high scores in the Level-3 R

images is generally very high. Further, the frequency of high-score pixels in

monthly product is slightly higher than that of daily product (for brevity, the ﬁgures are not shown here),

which is a result of the Level-3 data binning procedure.

The satellite R

measurements in the majority of global oceans have QA scores close to 1, with the highest

scores generally found in the center of the subtropical gyres. Not only the deep oceans but most coastal

waters retained high QA scores for these Level-3 products. Exceptions do exist, however. Relatively lower

QA scores (<0.6) are found in the Mediterranean Sea and in the equatorial upwelling regions in west of

Figure 8. Quality evaluation of the MODISA ocean color data in US East Coasts and the Northwest Atlantic Ocean. (a) Scores of the satellite

ocean color data (11 December 2015, A2015345180500.L2_LAC). (b) Comparison of the satellite ocean color and in situ data measured at a

station indicated by ‘‘䉱’’ symbol in Figure 8a (Station number: 20; position: 32.49448N, 277.85818W; sampling time: 17:04 UTC). (c) MODISA

measured ‘‘bad’’ R

spectra from the pixels indicated by ‘‘•’’ in Figure 8a.

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WEI ET AL. QUALITY ASSURANCE OF R

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Africa (see Figure 9). Surprisingly, the extremely clear oceanic waters in the North Atlantic Gyre (NAG) do

not show high QA scores either. It should be emphasized this phenomenon of low-score Level-3 R

data in

NAG and the equatorial upwelling region in west of Africa persists almost throughout the year (for brevity,

the ﬁgures for other months are omitted here). This low score could be due to underrepresentation of R

NAG in the reference database, where the spectral shapes of these R

might be different from that of other

oligotrophic waters because of likely impacts of the Saharan dusts [e.g., Claustre et al., 2002]. It is necessary

to carry out detailed in situ measurements over this region to pinpoint the exact reasons of the low QA

scores of these clear waters.

3.3. In Situ Measured R

Spectra

The NOMAD data set was compiled from a large online data depository SeaBASS [Werdell and Bailey, 2005],

and is a widely used for bio-optical model development and validations [Hu et al., 2012; O’Reilly et al., 1998;

Szeto et al., 2011]. Currently the database consists of more than 4000 measurements. For testing and appli-

cation purposes, we only chose the R

spectra with available measurements at wavelengths of 412, 443,

488, 555, and 667 nm, and obtained a total of 2358 R

spectra (Figure 10). Among these spectra, about one-

third was measured in clear oceanic waters with [CHL] <0.25 mg m

; the rest were from turbid coastal

waters. Two-thirds of the spectra were measured by the in-water approach, the rest was obtained from the

above-water approach. After applying the QA system to these selected R

, we found low-quality R

spectra

Figure 9. Scores of the MODISA R

data in global oceans. (a) Level-3 daily data (A2002188_L3m_9km). (b) Level-3 monthly data

(A20021822002212_L3m_9km) for July 2002.

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WEI ET AL. QUALITY ASSURANCE OF R

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accidentally included in the NOMAD

data set. For illustration, a few ques-

tionable R

spectra (QA scores <0.5)

are highlighted in Figure 10. In addi-

tion, we found that the QA scores are

not dependent on instruments or

[CHL], and R

with low QA appeared in

every ocean basins (For brevity, the

distribution map is not shown).

The inclusion of low-quality R

data in

NOMAD may impact the related ocean

color algorithm development and sub-

sequent model validations. For exam-

ple, if we only use the R

spectra

with the highest scores (QA score 51)

for the development of [CHL] algo-

rithm, the coefﬁcients of the new algo-

rithm (here, called OC3m-QA) will be

quite different than the original OC3m

algorithm, with improved model accu-

racy (see Figure 11).

4. Discussion

4.1. Characteristics of the New Water Type Classification

The score metrics developed in this study relies on cosine-based classiﬁcation scheme, which groups the R

spectra with similar spectral shapes. This optical classiﬁcation is distinctive from other practices [Le et al.,

2011; M

elin and Vantrepotte, 2015; Moore et al., 2009, 2001]. The spectral angle mapper [Kruse et al., 1993] is

widely used in land remote-sensing community and its extension to ocean color application deserves more

discussion.

First, it suppresses the disturbances of

absolute amplitudes of R

spectra to

the clustering, and only takes in

account the spectral shapes of R

direct consequence is that only the

waters with similar ‘‘water color’’ can

be grouped together. For example, the

blue water is by no accident combined

with green or yellow color waters

because of their contrasting R

spec-

tral shapes (Figure 3).

Second, the normalization in equation

(1) basically rescales the R

spectra to

the domain of (0,1), rendering the nR

spectra comparable with respect to

their ‘‘amplitudes.’’ Moreover, the nor-

malization retains the same spectral

ratios for nR

with the corresponding

spectra, while at the same time has

constrained the clustered nR

spectra

within a very narrow range. To illus-

trate, the coefﬁcient of variation (CV)

of nR

and R

is compared for each

Figure 10. R

spectra extracted from NOMAD database for QA testing (n52358).

The spectra highlighted with symbols (circles, diamonds, square, and triangles)

exemplify the bad R

spectra present in NOMAD.

Figure 11. Comparison of estimated [CHL] with in situ measured [CHL]. The origi-

nal (not quality controlled) data are compared to OC3m modeled data (in green

open circles). The quality controlled data (in pink dots, with the highest scores)

are compared to OC3m-QA estimated data. OC3m-QA has new model coefﬁcients

developed from these highest-score data (N51059, excluding those from the Arc-

tic and Southern Oceans) only. The OC3m-QA has the new model coefﬁcients as

0.2053, 22.0159, 1.1159, 21.3885, and 0.5474.

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WEI ET AL. QUALITY ASSURANCE OF R

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water type in Table 2. The CV’s of nR

spectra are much smaller than the CV’s of the corresponding R

spec-

tra in the visible domain. In blue waters, such a tendency is more evident at short wavelengths because of

higher R

values in this spectral domain. In contrast, it is more obvious at longer wavelengths in the green

or yellowish waters.

4.2. Applications to Satellite Ocean Color Validations

A common practice in evaluating the satellite ocean color data quality is through comparison with in situ

data set; with the latter assumed as the ‘‘ground truth’’ [Bailey and Werdell, 2006] by default. Graphically, the

ocean color data matchups can be illustrated in a manner of scatter-plots. Various error statistics can be

derived for the matchups, such as the relative percentage error (e), unbiased absolute percentage error (d),

and the root-mean-square error (RMSE). These estimators convey important information on the overall qual-

ity of various ocean color observations [IOCCG, 2006; Zibordi et al., 2009a]. However, the practices of scatter-

plots alone do not explicitly convey the quality of an individual R

spectrum. The limitation is further illus-

trated in Figure 12 with R

spectra. The newly developed QA system complements this common validation

practice in ocean color products, and assesses the data quality for individual R

spectra.

The ocean color data measured by different sensors have different number of spectral bands and different

central wavelengths, including MODIS Aqua (412, 443, 488, 531, 547, 667, and 678 nm), SNPP VIIRS (412,

443, 488, 555, and 667 nm), SeaWiFS (412, 443, 488, 510, 531, 555, and 667 nm), MERIS (412, 443, 488, 510,

555, 667, and 678 nm), and Landsat 8 (443, 482, 561, and 655 nm). The QA score system developed here

includes nine spectral bands, which have more bands than any of these satellite ocean color sensors. It is

important to evaluate whether the developed QA system with nine wavelengths can be reliably applied to

data from sources with lower number of spectral bands. To test this impact, we extracted four subsets of

data from the reference data set used in section 2, according to different wavelengths of MODIS Aqua,

SeaWiFS, VIIRS and Landsat 8 (for Landsat 8, we assumed 443, 488, 555, and 667 nm as the central wave-

lengths). We ﬁrst tested the repeatability of identiﬁed water types. It is found that the water type clustering

is fairly accurate, even though different spectral bands are employed. The statistics of the scores deter-

mined for every sensor are presented in Figure 13. For each case, about 90% of the evaluation spectra are

assigned to high QA scores of >0.8. The best performance is found with SeaWiFS data, then MODIS Aqua,

VIIRs and Landsat 8 data (Figure 13b). This is probably related to the fact that SeaWiFS has the highest num-

ber of wavelengths, while the simulated Landsat 8 data only have four bands being used in the QA system.

Note that in the comparisons we ignored the potential effects due to the differences in the spectral

Table 2. Coefﬁcient of Variation (%) of the nR

Spectra and Corresponding R

Spectra for Each Water Type

Water Type

(k)R

(k)

412 nm 443 nm 555 nm 667 nm 412 nm 443 nm 555 nm 667 nm

1 3 2 218625241377

2 3 1 114319181851

3 4 2 115521202078

4 6 4 8 34 27 26 25 46

5 7 4 8 38 37 38 44 96

6 8 3 5 20 43 44 44 48

7 8 4 4 29 60 60 60 66

8 9 5 4 21 61 63 63 64

9 9 4 6 29 82 95 91 145

10 13 6 4 25 60 66 64 87

11 11 8 6 16 108 119 103 129

12 13 7 3 14 58 61 58 65

13 18 11 3 25 39 40 39 44

14 13 8 3 11 42 46 39 46

15 18 9 2 10 52 54 48 53

16 15 11 4 6 105 111 108 111

17 14 9 4 7 86 92 84 91

18 22 13 3 24 55 51 41 50

19 21 17 3 5 58 49 47 46

20 25 15 4 10 52 53 44 48

21 23 14 4 13 51 46 40 46

22 28 16 5 8 67 71 74 74

23 20 18 6 7 46 46 32 36

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WEI ET AL. QUALITY ASSURANCE OF R

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bandwidth (FWHM) of satellite sensors. Landsat 8 has a much wider bandwidth (15–60 nm) than the other

ocean color satellite sensors (10–20 nm) in the visible domain.

On the other hand, for hyperspectral data, a modiﬁcation or expansion of this QA system can be readily

applicable.

4.3. Bio-Optical Implications and Significance

The remote-sensing reﬂectance is probably the most commonly used apparent optical property (AOP) in

ocean color remote sensing. For example, the widely used band ratio [O’Reilly et al., 1998] or band differ-

ence [Hu et al., 2012] algorithms for [CHL] rely on R

data at both blue and green wavelengths. Semianalyti-

cal bio-optical algorithms require the R

spectrum at more wavelengths as inputs [Ciotti et al., 1999; IOCCG,

2000, 2006; Wei and Lee, 2015]. Thus, the QA of every individual R

spectrum is fundamental for accurate

remote sensing of water-column properties. When questionable or erroneous R

spectra are involved, the

bio-optical retrievals from the ocean color will mostly likely be undermined. Our preliminary analysis dem-

onstrates the signiﬁcance of R

data quality to estimates of [CHL] (Figures 10 and 11). Errors or uncertainties

of R

measurements will propagate to the retrievals of inherent optical retrievals as well [Lee et al., 2010b].

The ‘‘low’’ data quality of MODIS R

found in the North Atlantic Gyre and in the Mediterranean Sea (Figure 9)

may be partly related to an insufﬁcient representativeness of R

spectra in the database for these waters.

Indeed, the oligotrophic waters in North Atlantic and marginal seas could be optically different than that in

the Paciﬁc or South Atlantic, because the former are likely subject to the impacts of dusts transported from

Sahara in the easterly trades and Asian dusts from the west [Uno et al., 2009]. The absorptive ﬁne mineral

particles present in surface waters can attenuate the blue light, addition to the contribution of

Figure 12. Application and limitation of scatter-plot practice in evaluating the data quality of R

spectra. The R

rs0

spectrum (in green) rep-

resents true values. The R

rs1

and R

rs2

spectra refer to two example measurements. The scatter-plot of Figure 12b represents the measure-

ment scenario in Figure 12a, where the R

rs1

and R

rs2

spectra retain the same shapes as R

rs0

. Figure 12c represents another situation when

the R

rs1

and R

rs2

spectra are erroneously measured (for brevity, we simply switched R

values at some bands between R

rs1

and R

rs2

spectra).

Interestingly, the resulted new scatter-plot in Figure 12d is exactly the same with Figure 12b, with the same error statistics.

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

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phytoplankton absorption and CDOM and detritus [Claustre et al., 2002]. On the other hand, the presence of

absorptive particles in the atmosphere poses a challenge to atmospheric correction, resulting in under-

estimated R

at blue bands in these waters [Schollaert et al., 2003]. The latter situation could have occurred

in the satellite retrieved ocean color data. A comprehensive investigation with accurate ﬁeld measurement

of R

is required to completely understand the low QA scores of MODIS R

in those waters.

Nevertheless, it is imperative to QC and quality assure an individual R

spectrum measured from both in

situ and satellite platforms, in order to produce multisensor optical and biogeochemical products with high

quality.

5. Conclusions

We have developed a QA system to objectively measure the quality of each individual R

spectrum. The QA

system is based on classiﬁcation of R

spectral similarity determined by the cosine distance, which is further

measured by the upper and lower boundaries obtained from a wide range of ﬁeld observations. This scheme

and the spectral constraints together constitute a robust and quantitative QA system. Basically the metric sys-

tem provides information of likely reliability of a target R

spectrum. If a score of 0 is reached, it indicates the

target R

is highly questionable, although it could be true for that speciﬁc environment. The performance of

this QA scheme was tested with synthesized, in situ measured, and satellite obtained R

spectra and has

shown great reliability and applicability, where ‘‘bad’’ or highly questionable R

spectra were well identiﬁed.

The QA metric system does not require a priori knowledge of the optical properties of waters under study,

and it is applicable to multispectral or hyperspectral R

data obtained from any platform. Since it is based

on in situ R

measurements from a wide range of water types, the innovative QA system can be universally

used for QC of ocean color data from global oceans.

Figure 13. Testing results for the accuracy of score metrics model when applied to satellite ocean color data. (a) MODIS Aqua: 412, 443,

488, 531, 547, and 667 nm; (b) SeaWiFS: 412, 443, 488, 510, 531, 555, and 667 nm; (c) VIIRS: 412, 443, 488, 555, and 667 nm; (d) Landsat

8 (assumed 443, 488, 555, and 667 nm).

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WEI ET AL. QUALITY ASSURANCE OF R

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Because all ﬁeld measurements are discrete, it is likely that the database used here does not cover every

water types and/or there are situations where the range of R

variability goes beyond the domains deﬁned

here. Such a limitation can be updated or revised when more high-quality in situ measurements are avail-

able. A MATLAB

script is made available (http://oceanoptics.umb.edu/score_metric/) to facilitate the evalu-

ation and reﬁnement of the score metrics. Nevertheless, this QA scheme provides an easily applicable

system to quantitatively evaluate the quality of individual R

spectra.

APPENDIX A

Table A1. Upper Boundary of the Normalized Remote-Sensing Reﬂectance Spectra for the 23 Water Types

OWT 412 443 488 510 531 54750 555 667 678

1 0.780 0.559 0.367 0.203 0.138 0.109 0.096 0.046 0.047

2 0.711 0.555 0.424 0.254 0.182 0.141 0.126 0.028 0.027

3 0.646 0.540 0.471 0.322 0.243 0.197 0.173 0.067 0.062

4 0.570 0.515 0.528 0.374 0.312 0.265 0.240 0.062 0.062

5 0.478 0.488 0.548 0.418 0.352 0.314 0.301 0.099 0.098

6 0.423 0.416 0.506 0.427 0.390 0.358 0.345 0.065 0.071

7 0.362 0.386 0.485 0.439 0.413 0.378 0.360 0.090 0.096

8 0.328 0.343 0.464 0.449 0.441 0.418 0.412 0.094 0.140

9 0.429 0.369 0.434 0.413 0.412 0.403 0.410 0.166 0.175

10 0.283 0.318 0.471 0.451 0.451 0.454 0.452 0.128 0.125

11 0.360 0.319 0.373 0.400 0.427 0.451 0.477 0.170 0.284

12 0.253 0.287 0.374 0.405 0.439 0.475 0.507 0.183 0.188

13 0.235 0.253 0.392 0.424 0.473 0.486 0.488 0.128 0.134

14 0.263 0.263 0.350 0.382 0.429 0.461 0.507 0.262 0.276

15 0.202 0.219 0.333 0.381 0.448 0.493 0.521 0.203 0.224

16 0.230 0.224 0.296 0.339 0.382 0.432 0.465 0.393 0.419

17 0.232 0.244 0.316 0.355 0.415 0.463 0.503 0.302 0.313

18 0.202 0.204 0.309 0.376 0.455 0.522 0.560 0.163 0.170

19 0.066 0.147 0.236 0.296 0.367 0.415 0.439 0.479 0.493

20 0.159 0.184 0.296 0.356 0.429 0.500 0.571 0.290 0.293

21 0.235 0.237 0.293 0.336 0.443 0.515 0.605 0.241 0.286

22 0.159 0.167 0.251 0.318 0.408 0.482 0.573 0.351 0.383

23 0.180 0.167 0.198 0.233 0.310 0.452 0.578 0.379 0.509

Table A2. Lower Boundary of the Normalized Remote-Sensing Reﬂectance Spectra for the 23 Water Types

OWT 412 443 488 510 531 547 555 667 678

1 0.709 0.512 0.271 0.119 0.073 0.053 0.044 0.002 0.002

2 0.638 0.509 0.364 0.198 0.132 0.100 0.084 0.003 0.003

3 0.553 0.497 0.412 0.246 0.179 0.140 0.119 0.007 0.007

4 0.436 0.438 0.419 0.310 0.241 0.193 0.169 0.010 0.011

5 0.365 0.390 0.417 0.366 0.287 0.232 0.202 0.016 0.015

6 0.307 0.360 0.405 0.387 0.347 0.297 0.272 0.029 0.028

7 0.251 0.315 0.415 0.403 0.373 0.334 0.306 0.016 0.021

8 0.195 0.266 0.375 0.386 0.390 0.371 0.345 0.023 0.025

9 0.295 0.316 0.367 0.362 0.359 0.352 0.341 0.058 0.066

10 0.131 0.234 0.336 0.381 0.407 0.390 0.376 0.022 0.032

11 0.247 0.240 0.311 0.345 0.366 0.370 0.377 0.085 0.118

12 0.148 0.207 0.302 0.336 0.409 0.425 0.427 0.110 0.115

13 0.092 0.161 0.313 0.375 0.423 0.438 0.436 0.024 0.023

14 0.158 0.200 0.265 0.311 0.382 0.427 0.438 0.154 0.179

15 0.066 0.149 0.273 0.334 0.418 0.455 0.466 0.135 0.143

16 0.156 0.161 0.226 0.282 0.356 0.394 0.417 0.328 0.332

17 0.137 0.176 0.252 0.310 0.388 0.418 0.437 0.244 0.243

18 0.058 0.116 0.249 0.321 0.419 0.480 0.499 0.050 0.054

19 0.032 0.080 0.183 0.246 0.324 0.378 0.411 0.417 0.409

20 0.036 0.096 0.218 0.293 0.395 0.464 0.490 0.204 0.217

21 0.107 0.141 0.199 0.246 0.347 0.464 0.508 0.149 0.171

22 0.073 0.098 0.200 0.249 0.330 0.450 0.485 0.264 0.292

23 0.093 0.095 0.146 0.194 0.265 0.382 0.485 0.301 0.383

Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 17

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Acknowledgments

This study is funded by the National

Oceanic and Atmospheric

Administration (NOAA) JPSS VIIRS

Ocean Color Cal/Val Project (Lee), the

National Key Research and

Development Program of China

(2016YFC1400900, Shang), the

National Aeronautic and Space

Administration (NASA) Ocean Biology

and Biogeochemistry and Water and

Energy Cycle Programs (Lee), and the

Chinese Ministry of Science and

Technology (2016YFA0601201, Shang),

and the NSF-China (41576169, Shang).

We are indebted to Giuseppe Zibordi

(Joint Research Center, Italy),

Chuanmin Hu (University of South

Florida), Marlon Lewis (Dalhousie

University, Canada) for sharing in situ

radiometric measurements and many

investigators who submitted data to

SEABASS (data used in this effort are

downloadable from http://oceanoptics.

umb.edu/score_metric). We appreciate

the ﬁne work of Kelly Luis in the

correction of the English text. We

thank an anonymous reviewer and

Giuseppe Zibordi for constructive

comments which have improved the

manuscript.

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Journal of Geophysical Research: Oceans 10.1002/2016JC012126

WEI ET AL. QUALITY ASSURANCE OF R

SPECTRA 19

Python code for calculating the Rrs spectra quality assurance (QA) scores (v2.0)

Code

December 2020

Jianwei Wei · Dirk Aurin

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Jul 2024
ISPRS J PHOTOGRAMM

Automated above-water hyperspectral observations are often subject to inaccuracies caused by instrument malfunction and environmental conditions. This study evaluates the influence of atmospheric and water surface conditions on above-water hyperspectral measurements through statistical methods and Radiative Transfer (RT) modelling. Initially, we developed a general quality control method based on statistical assessment to detect the suspicious spectra. Subsequently, Radiative Transfer (RT) models were used to assess low light conditions, distortions in the spectral shape of above-water solar downwelling irradiance (ES(λ), mW m−2 nm−1) particularly those caused by intense atmospheric scattering and/or reddish hue of dusk or dawn radiation, the effect of atmospheric humidity and precipitation on the intensity and shape of spectra, and the influence of sun glint and surface perturbations on sky (LS(λ), mW m−2 nm−1 sr−1) and water surface (LT(λ), mW m−2 nm−1 sr−1) radiances. The proposed methods were applied to the entire archive of automated above-water hyperspectral measurements collected every ten minutes from 2020 to 2022 at the Royal Netherland Institute for Sea Research (NIOZ) at Jetty Station (NJS) located in the Marsdiep tidal inlet of the Duch Wadden Sea, the Netherlands. The findings demonstrate that low light conditions are characterized by ES(λ)max ≤ 25 mW m−2 nm−1. Red-shifted or distorted spectra are indicated by a ratio of ES(4 8 0)/ES(6 8 0) ≤ 1.0 and ES(λmax)/ ES(8 6 5) ≤ 1.25. High humidity/precipitation conditions are identified by the ratio of ES(9 4 0)/ES(8 6 5), which varies with the Solar Zenith Angle (SZA). Furthermore, significant sun glint and surface perturbations, such as whitecaps and foam, are indicated when the minimum ratio of LT(800 nm-950 nm)/ES(800 nm-950 nm) > 0.025 sr−1, and the ratio of LT(850 nm)/ES(850 nm) ≥ 0.025 sr−1.

Remotely Sensed Water Quality in Qatari Coastal Waters Between 2002 and 2022

Article

Full-text available

Sep 2024

Over the past two decades, Qatar has undergone significant economic growth and development, yet little information is available on long-term trends in seawater quality around the Qatar Peninsula. This study analyzed spatiotemporal variations of remotely sensed optical water quality (OWQ) parameters in Qatari coastal waters between 2002 and 2022. These OWQ parameters, including chlorophyll-a concentration (Chla), turbidity (Turb), and Secchi disk depth (SDD), along with sea surface temperature, were derived from MODIS/Aqua observations after applying an optically shallow-water mask. Additionally, changes in floating algae scum density, an indicator of Harmful algal blooms (HABs), were derived from MSI observations. Strong nearshore-offshore gradients were generally observed for all OWQ parameters (multiannual mean Chla ∼ 0.6 – 3 mg m ^-3 ; Turb ∼ 0.2 – 3 FNU; SDD ∼ 5 – 12 m). SDD was typically greatest in late-spring and summer when Chla and Turb were relatively low. OWQ variability in the main territorial sea was mainly driven by suspended sediments, while in the broader Exclusive Economic Zone was driven by algal blooms. HABs dominated by Margalefidinium polykrikoides, Noctiluca scintillans, and Trichodesmium spp. were frequently observed in deeper (>20 m) waters. Despite Qatar's massive economic development in recent years, declines in Chla and Turb and increased SDD were observed. Qatari coastal waters, though, are warming at a rate of 0.64°C/decade, ∼2 – 3 times faster than neighboring Red Sea and Northern Arabian Sea waters and ∼8 times faster than the global oceans. This thermal stress may pose future challenges for marine ecosystems and the services they provide.

Holistic optical water type classification for ocean, coastal, and inland waters

Article

Full-text available

Jun 2024
LIMNOL OCEANOGR

Water constituents exhibit diverse optical properties across ocean, coastal, and inland waters, which alter their remote‐sensing reflectance obtained via satellites. Optical water type (OWT) classifications utilized in satellite data processing aim to mitigate optical complexity by identifying fitting ocean color algorithms tailored to each water type. This facilitates comprehension of biogeochemical cycles ranging from local to global scales. Previous OWT frameworks have focused narrowly on either oceanic or inland waters and have relied too heavily on specific data collections. We propose a novel holistic OWT framework applicable to all natural waters, based on state‐of‐the‐art bio‐geo‐optical modeling and radiative transfer simulations that encompass different phytoplankton groups. This framework employs a “knowledge‐driven” paradigm, combining domain knowledge and insights from previous studies to simulate the reflectance spectrum from water constituent concentrations and inherent optical properties. Our method extracts optical variables to represent the full spectrum of reflectance, consolidating both spectral shape and magnitude. We apply the framework utilizing diverse in situ, synthetic, and satellite data (Sentinel‐3 OLCI) and demonstrate its better classifiability than other frameworks. This framework lays the foundation for comprehensive global monitoring of natural waters.

Remote retrieval of dissolved organic carbon in rivers using a hyperspectral drone system

Article

Full-text available

May 2024

Rivers act as the principal channels for transporting terrigenous dissolved organic carbon (DOC) to lakes and reservoirs. Satellite remote sensing-based river monitoring is difficult due to the narrow river form and high spatiotemporal heterogeneity of DOC components. The unique advantages of unmanned aerial vehicles (UAVs) facilitate river DOC concentration monitoring. The DOC concentration in 8 major tributaries (average width: 109.62 m) and shoreside of the Lake Chaohu Basin were retrieved via a hyperspectral UAV. The results showed that (1) the DOC concentration was significantly correlated with the water remote sensing reflectance (

R_{rs}

Rrs) of 402, 429-438, 440–451 and 458–462 nm in the blue band (r²: 0.11 to 0.13; p<0.05), and 620–621 and 623–693 nm in the red band (r²: 0.12 to 0.20; p<0.05). The water quality parameters chlorophyll-a (Chl-a) and suspended particulate matter (SPM) and environmental parameters wind speed and temperature 3 days delay sampling date, also showed a significant correlation. (2) The random forest regression (RFR) model attained the best performance (r²: 0.64; RMSE: 0.30 mg/L; MAPE: 7.02%). (3) DOC concentration in Lake Chaohu Basin was highest in the northeast (8.19 mg/L), followed by the northwest and west (7.13 mg/L), and it was lowest in the south (6.70 mg/L).

Intelligent Radiometric Calibration System for Ocean Color Satellite Sensors

Article

Full-text available

Sep 2024

To achieve the integration of multiple ocean color (OC) sensors’ radiometric calibration tasks into a single system, we have developed an intelligent on-orbit radiometric calibration system called the Generalized Radiometric Calibration Entity for Ocean Color (Grace-OC). The system features real-time data downloading capabilities and integrates three calibration methods: onboard calibration, system vicarious calibration and cross calibration, enabling intelligent selection of on-orbit radiometric calibration methods tailored to the calibration sensors. Compared to other calibration systems, we have improved the universality and efficiency of the system by establishing high-spectral aerosols and Rayleigh lookup tables (LUTs) which are verified consistency through a comparative analysis with operational LUTs releasing by National Aeronautics and Space Administration (NASA) in this paper. Building upon this foundation, we have integrated a comprehensive analysis function for calibration coefficients to automatically construct degradation models of the radiometric measurement performance, and to achieve mutual verification between different calibration methods. We applied Grace-OC to HY1C/D and verified the feasibility of the intelligent selection calibration methods and the stability of the calibration system, achieving a calibration accuracy of up to 0.5%. Simultaneously, the precision of degradation models of the radiometric measurement performance is confirmed through Grace-OC, and the on-orbit radiometric calibration task was ultimately completed within 6 minutes for each per scene. Based on the above applications, Grace-OC has demonstrated its universality for various OC sensors, as well as the stability of on-orbit radiometric calibration tasks and the efficiency of operational speed.

Mapping ocean surface algal blooms with SWIR-derived satellite remote sensing reflectance

Article

Full-text available

Jul 2024

This study exploits the potential of satellite remote sensing reflectance spectra (Rrs(λ)) for detecting ocean surface algal blooms. Three types of floating surface algae are examined: Sargassum, Ulva, and Trichodesmium. The satellite images are processed with a shortwave infrared (SWIR)-based atmospheric correction processor from the Visible Infrared Imaging Radiometer Suite (VIIRS). We calculate the red-edge reflectance anomaly from the Rrs(λ) data to delineate the notable spectral difference at 671 and 862 nm. The new data have generated floating algal maps comparable with historical methods relying on Rayleigh-corrected reflectance data. The Rrs(λ) spectra are found to have high quality scores over Sargassum and Trichodesmium waters, suggesting less uncertainty from atmospheric correction. With problems to be addressed, this preliminary study finds it promising to use the VIIRS reflectance products for floating algae detection and mapping. Continuous efforts are highly recommended as the new data and approach can not only facilitate a retrospective analysis over global oceans but also benefit a greater application to next-generation hyperspectral satellites.

Seamless observations of chlorophyll-a from OLCI and VIIRS measurements in inland lakes

Article

Nov 2024
WATER RES

The Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) are two main instruments for the ocean color community to observe the global lake environment in the following decades. Despite their applications to retrieve various water optical parameters, the spatial and temporal resolutions of individual sensors cannot meet the requirements for lake monitoring effectively. To date, the possibility of complementary observations through the OLCI-VIIRS data to lake aquatic environments remains unclear. Here, we evaluated the agreement between OLCI and VIIRS-derived remote sensing reflectance (Rrs(λ)) and chlorophyll-a (Chl-a) in Chinese lakes spanning a variety of lake characteristics. We find that OLCI Rrs(λ) data generated by the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) system perform satisfactory accuracy in 20 Chinese lakes with less than 30 % uncertainty from 490 nm to 865 nm and show good agreements with VIIRS Rrs(λ) in more than 200 large lakes in China (> 0.90 correlation). The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl-a estimates from OLCI images (23 % bias). The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20 % difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes. With the OLCI data and well-validated algorithm, we revealed the high-resolution maps of Chl-a in 681 lakes of larger than 10 km2 in China, which significantly filled the results in small-medium lakes where VIIRS did not observe before. This study demonstrates the reasonable agreement of OLCI-VIIRS observations in lakes and proposes an initiative to generate seamless data records in inland lakes through OLCI-VIIRS data.

Cross-Satellite Atmospheric Correction for Consistent Remote Sensing Reflectance from Multiple Ocean Color Satellites: Concept and Demonstrations

Article

Full-text available

Nov 2024

Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites, thereby enhancing spatial coverage and extending temporal spans. However, due to factors such as sensor specifics and separate data processing algorithms, bio-optical properties (e.g., remote sensing reflectance, Rrs ) from different ocean color missions exhibit varying discrepancies in oceanic, coastal, and inland waters. Here, we introduce a cross-satellite atmospheric correction (CSAC) scheme, which could greatly improve the consistency of Rrs products between MODIS-Aqua and other satellite ocean color missions. Specifically, using an inclusive high-quality Rrs dataset of oceanic waters obtained from MODIS-Aqua as the reference, and as an example, top-of-atmosphere reflectance from SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) is directly processed to MODIS-Aqua-equivalent Rrs ( R rs MA − eqv ) via CSAC. As a demonstration, for independent space-time matched measurements between MODIS-Aqua and SeaWiFS, the mean absolute percent difference (MAPD) between R rs MA − eqv and MODIS-Aqua Rrs ranges from 5.9% to 22.2% across wavelengths from 412 to 667 nm. In contrast, the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua Rrs products range from 10.1% to 55.1% for the same spectral bands. These results highlight the potential of CSAC in obtaining consistent Rrs products and, subsequently, Rrs -derived bio-optical properties, from various ocean color satellites, facilitating extensive and long-term ocean color observations of the global ocean.

Spectral slopes of the absorption coefficient of colored dissolved and detrital material inverted from UV-visible remote sensing reflectance

Article

Full-text available

Mar 2016

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), Scdm (units: nm−1), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multi-band ocean color in the visible domain. In this study, we explore the potential of semi-analytically retrieving Scdm with added ocean color information in the ultraviolet (UV) range between 360-400 nm. Unique features of hyperspectral remote sensing reflectance in the UV-visible wavelengths (360-500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized dataset to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of Scdm from remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors. This article is protected by copyright. All rights reserved.

Masks and flags updates

Article

Full-text available

May 2003

The flags and masks used for the SeaWiFS level-2 and level-3 processing were updated for the recent fourth reprocessing. This chapter discusses the changes and why they were made. In many cases, underlying algorithms were changed. Some flags changed their states to either flagging (noting a condition), or masking (denoting data excluded from the product) to allow more data to be kept or to improve its quality. New flags were either introduced as a part of new algorithms or to denote the status of the data more clearly. The flag and mask changes significantly contributed to the improvement in the data quality and increased the amount of data retrieved.

Ocean color chlorophyll algorithms for SeaWiFS

Article

Full-text available

Oct 1998

A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 μg L-1. Most of the observations are from Case I nonpolar waters, and ~20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal: noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.

Deforestation in North-Central Yucatan (1985-1995): Mapping secondary succession of forest and agricultural land use in Sotuta using the cosine of the angle concept

Article

Full-text available

Aug 1999
PHOTOGRAMM ENG REM S

A new spectral pattern matching approach that utilizes the spectral angle (the cosine of the angle) concept was used for mapping deforestation and successional stages of forest regrowth in Sotuta in the state of Yucatan, Mexico. By calculating spectral angles between finely defined spectral clusters and known reference signatures, and assigning each spectral cluster to one of the reference classes based on the minimum spectral angle rule, we were able to map forest regrowth stages and agricultural land-use classes. Our research shows that, by adapting a spectral pattern matching approach demonstrated in this paper, spectral clusters can be assigned into information classes precisely and objectively, and the inconsistency involved in visual interpretations can be avoided. The conceptual difference between the spectral distance and spectral angle in feature space is also reviewed. In the study area, the rate of deforestation is high and agricultural land use is intensifying increasingly. The limited amount of land granted to ejidos and rapid population growth seem to be major causes of deforestation in the study area.

Radiance transmittance measured at the ocean surface

Article

Full-text available

Apr 2015
OPT EXPRESS

The radiance transmittance (Tr) is the ratio of the water-leaving radiance (Lw(0⁺)) to the sub-surface upwelling radiance (Lu(0⁻)), which is an important optical parameter for ocean optics and ocean color remote sensing. Historically, a constant value (~0.54) based on theoretical presumptions has been adopted for Tr and is widely used. This optical parameter, however, has never been measured in the aquatic environments. With a robust setup to measure both Lu(0⁻) and Lw(0⁺) simultaneously in the field, this study presents Tr in the zenith direction between 350 and 700 nm measured in a wide range of oceanic waters. It is found that the measured Tr values are generally consistent with the long-standing theoretical value of 0.54, with mean relative difference less than 10%. In particular, the agreement within the spectral domain of 400-600 nm is found to be the best (with the averaged difference less than 5%). The largest difference is observed for wavelengths longer than 600 nm with the average difference less than 15%, which is related to the generally very small values in both Lu(0⁻) and Lw(0⁺) and rough environmental conditions. These results provide a validation of the setup for simultaneous measurements of upwelling radiance and water-leaving radiance and confidence in the theoretical Tr value used in ocean optics studies at least for oceanic waters.

How optically diverse is the coastal ocean?

Article

Full-text available

Feb 2015
REMOTE SENS ENVIRON

Coastal regions are a resource for societies while being under severe pressure from a variety of factors. They also show a large diversity of optical characteristics, and the potential to optically classify these waters and distinguish similarities between regions is a fruitful application for satellite ocean color. Recognizing the specificities and complexity of coastal waters in terms of optical properties, a training data set is assembled for coastal regions and marginal seas using full resolution SeaWiFS global remote sensing reflectance RRS data that maximize the geographic coverage and seasonal sampling of the domain. An unsupervised clustering technique is operated on the training data set to derive a set of 16 classes that cover conditions from very turbid to oligotrophic. When applied to a global seven-year SeaWiFS data set, this set of optical water types allows an efficient classification of coastal regions, marginal seas and large inland water bodies. Classes associated with more turbid conditions show relative dominance close to shore and in the mid-latitudes. A geographic partition of the global coastal ocean serves to distinguish general optical similarities between regions. The local optical variability is quantified by the number of classes selected as dominant across the period, averaging 5.2 classes if the cases accounting for 90% of the data days are considered. Optical diversity is more specifically analyzed with a Shannon index computed with the class memberships. Regions with low optical diversity are the most turbid waters as well as closed seas and inland water bodies. Oligotrophic waters also show a relatively low diversity, while intermediate regions between coastal domain and open ocean are associated with the highest diversity, which has interesting connections with ecological features.

Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements

Article

Jan 2013
REMOTE SENS ENVIRON

Least squares quantization in PCM

Article

Jan 2006

S. Lloyd

REMOTE SENSING OF SPECTRAL RADIANCE FROM BELOW THE OCEAN SURFACE.

Article

Roswell Austin

Since the advent of manned spaceflight, there have been many interesting examples of photography obtained from space over ocean and coastal regions. The potential applications of such space photography to the solution of oceanographic problems and the even greater potential of directly-obtained spectroradiometric data are manifestly obvious. The visible region of the electromagnetic spectrum is unique in that it allows information to be carried from below the ocean surface to a remote sensor. More specifically, it is only in the narrower spectral region from about 400 to 600 nanometers that sensible data can be obtained from water depths greater than a few meters. Beyond this wavelength, the remote sensing technique becomes more useful for the appraisal of surface than of subsurface phenomena. At the shorter wavelengths, that is below about 400 nm, the water absorption also increases rapidly in all but the clearest of oceanic waters. Perhaps even more significant at the shorter wavelengths is the rapid increase of the veiling effects of the atmosphere. As the wavelength decreases that portion of the radiation emanating from the ocean which reaches the remote sensor becomes insignificant when compared to the radiance of the scattered daylight in the path of sight. The remote optical sensor utilizes the spectral, spatial and temporal information encoded in the radiance signal which it receives. Inferences about such variables as the water depth, turbidity, or chlorophyll level are drawn after this encoded information has been processed in some manner. Unfortunately, the apparent signal with which the remote sensor must work usually differs markedly from the inherent signal which exists at or just below the water surface. As it is this latter signal which contains the desired information about the water or the ocean bottom, it is important to understand how it is attenuated and modified in travelling upward through the atmosphere to the sensor.

The Evolution of Optical Water Mass Classification

Article