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With the development of quantitative ocean color remote sensing, estimation of chlorophyll-a concentration in the coastal waters has aroused increasing attention from researchers. Currently, researches are confronted with difficulty in improving the accuracy of chlorophyll-a concentration estimation for turbid waters. Atmospheric correction, chlorophyll-a concentration modeling, and scale effect have already been identified as three critical factors affecting coastal water remote sensing. The in-depth exploration of them will accelerate the research progress of ocean color remote sensing. The ultimate objective of atmospheric correction and scale effect correction is to accurately estimate active constituents of turbid coastal waters in an optical way. Accordingly, the chlorophyll-a concentration modeling is a basic problem to be resolved, while atmospheric correction is the essential one. The scale effect problem arises during the modeling procedure where unrealistic homogeneous assumption is taken to measure chlorophyll-a concentration from the realistic non-homogeneous pixel. In the coastal remote sensing field, these three problems have become the most important topics in the current researches, and they will remain be the hot topics in the future. 海洋颜色遥感是海洋学和环境科学中的一项重要技术，它通过从人造卫星或其他平台获取的遥感数据来探测和量化海洋表面的特征。近年来，随着量化海洋颜色遥感技术的发展，对于近海区域叶绿素-a浓度的估算已经引起了研究人员越来越大的兴趣。近海区域，即海岸水域，由于其环境的特殊性，如混合了陆地排放的营养物质以及悬浮沉积物等，使得其遥感观测面临许多独特的挑战。叶绿素-a是海洋浮游植物的重要成分，是海洋生产力的直接指标，因此，其浓度估算对于海洋生态系统的健康状况评估、渔场管理、全球碳循环研究等都至关重要。在进行海洋颜色遥感时，研究人员面临三大关键技术问题：大气校正、叶绿素-a浓度建模和尺度效应。大气校正问题。海洋遥感中，传感器接收到的信号不仅包含海水的光学信号，还包括大气中的散射和吸收作用带来的噪声。大气校正是指去除这些大气影响的过程，以获取更准确的海洋水色信息。在清澈的水体中，这一过程相对容易，因为水体的吸收和散射特性对信号的影响占主导地位。然而，在近海区域的浑浊水域，悬浮颗粒物和溶解有机物等的吸收和散射作用也十分强烈，这就使得大气校正变得异常复杂。正确的校正不仅能提高叶绿素-a浓度估算的准确性，还能为其他水体成分（如悬浮泥沙、有色可溶解性有机物）的估算提供可靠的基础。叶绿素-a浓度建模问题。叶绿素-a浓度建模是通过遥感数据来估算海洋表面叶绿素-a浓度的过程。这通常涉及到开发算法，将遥感反射率数据转换为叶绿素-a浓度。在这一过程中，需要考虑多种因素，包括水体光学特性、悬浮物和可溶解性有机物的影响等。由于叶绿素-a在水体中的分布不均一，这一建模过程变得尤为复杂。尺度效应问题。在遥感观测中，通常会将现实世界中的非均质像素简化为均质假设进行叶绿素-a浓度的测量，这与实际的非均质性相矛盾，从而导致尺度效应问题。由于实际海洋环境中的异质性，使得从大尺度的遥感数据中获取准确的海洋参数存在难度，特别是当涉及到小尺度的海洋过程时。这一问题在近海区域尤为突出，因为这里的水体特征变化更为迅速和复杂。这三个问题在当前近海水域遥感研究中已经成为最重要的研究主题，并且在未来仍将是热点话题。解决这些问题需要跨学科的合作，包括海洋学、遥感科学、大气物理学、光学和计算机科学等领域的专家共同努力。研究人员需要开发更先进的遥感仪器，设计更精准的算法，同时也需要不断提高大气校正的精度，改进尺度效应的处理方法，从而更准确地估算浑浊近海水域中的叶绿素-a浓度。通过这些努力，可以促进海洋颜色遥感研究的进步，进而为海洋资源的可持续管理、海洋生态系统的保护和海洋环境变化的监测提供更加科学和有效的手段。

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1

A Review of Some Important Technical Problems in

Respect of Satellite Remote Sensing of Chlorophyll-a

Concentration in Coastal Waters

Jun Chen, Minwei Zhang, Tingwei Cui, and Zhenhe Wen

Abstract—With the development of quantitative ocean c

olor

remote sensing, estimation of chlorophyll-a concentration in the

coastal waters has aroused increasing attention from researchers.

Currently, researches are confronted with dif

ﬁculty in improving

the accuracy of chlorophyll-a concentration estimation for turbid

waters. Atmospheric correction, chlorophyll-a concentration mod-

eling, and scale effect have already been id

entiﬁed as three critical

factors affecting coastal water remote sensing. The in-depth explo-

ration of them will accelerate the research progress of ocean color

remote sensing. The ultimate o bjecti

ve of atmospheric correction

and scale effect correction is to accurately estimate active con-

stituents of turbid coastal waters in an optical way. Accordingly,

the chlorophyll-a concentration m

odeling is a basic problem to

be resolved, while atmospheric correction is the essential one.

The scale effect problem arises during the modeling procedure

where unrealistic homogeneous a

ssumption is taken to measure

chlorophyll-a concentration from the realistic non-homogeneous

pixel. In the coastal remote sensing ﬁeld, these three problems

have become the most import

ant topics in the current researches,

and they will remain be the hot topics in the future.

Index Terms—Atmospheric Correction, Chlorophyll-a Concen-

tration, Coastal Waters, Remote Sensing, Scale Effect.

I. INTRODUCTION

HE majority of the world’s most productive marine

ecosystems are found within coastal environment and

their productivity, diversity and wealth of life should be at-

tributed to their terrestrial adjacency [1]. These regions are key

to biological activity and generate the biological production

Manuscript received A ugust 06, 2012; revised October 10, 2012; accepted

January 22, 2013. This work was supported by the Science Foundation

for 100 Excellent Youth Geological Scholars of China Geological Survey

(GZH201200036), open fund of Key Laboratory of Marine Hydrocarbon

Resources and Environmental Geology (MRE201109), High-tech R e se arch

and Development Program of China (2007AA092102), and Dragon 3 P roject

(10470).

J, Chen is with the School of Ocean Scienc es, China University of Geo-

sciences (Beijing), Beijing, 100083 China, and also with the Key Laboratory of

Marine Hydrocarbon Resources and Environmental Geology, Qingdao, 266071

China (e-mail:cjun@cgs.cn).

M. W. Zhang is with th e Center for Earth observ ation and Digital Earth, Chi-

nese Academy of Science s, Beijing (e-mail: mwzhang@ceode.ac.cn).

T. W. Cui is with First Institute of Ocea no graphy, State Oceanic Administra-

tion, Qingdao (e-mail: cuitingwei@ﬁo.org.cn).

Z. H. Wen is with the School of Ocean Sciences, China University of Geo-

sciences (Beijing), Beijing, 100083 China, and also with the Key Laboratory of

Marine Hydrocarbon Resources and Environmental Geology, Qingdao, 266071

China (e-mail: wenzhh@sina.com).

Color versions of one or more of the ﬁgures in this paper are available online

at http://ieeexplore.ieee.org.

Dig

ital Object Identiﬁer 10.1109/JSTARS.2013.2242845

that supports 90% of the world’s ﬁsh catches [2]. The coa

stal

zone is also a complex system from the perspectiv

e of ecology:

many unique ecosystems are adapted to high nutr

ient ﬂows

from terrestrial sources and sediment resusp

ension [3 ]. These

productive marine ecosystems are important

habitats for many

ﬁshes and other marine organisms that are no

t only a signiﬁcant

source of food for human consump tion, b ut al

so vital compo -

nents of marine ecosystems. Due to the

contribution to human

welfare, the sustainable managemen

t of these ecosystems

becomes an issue of environmental an

d political concern, and

the development of new strategies f

or the sustainable manage-

ment of coastal waters might preve

nt costly rehabilitation and

treatment of deteriorating wate

rs, eve n the future exploration

of new water sources [4].

Chlorophyll-a

concentrat

ion is a well-known indi-

cator of the ecological healt

h of aquatic environment. It is also

one of the most important fac

tors affecting aquatic environment,

and may result in visible ch

anges in water bodies. The mea-

surement of

concentratio

n may facilitate the trophic state

characterization of an aq

uatic ecosystem through various nu-

merical schem es [5]. Tr

aditionally, its concentration in water is

measured spectrophot

ometrically, after the samples have been

collected, preserve

d and transpo rted to the laboratory. This ap-

proach is well recogn

ized and w idely applied, despite its labor-

intensive and time-

consuming property. However, it does not

allow researchers

to map contemporaneous

concentration

on a large spatial s

cale [6]. Therefore, the method is usually used

to characterize

the spatial variation of the trophic state within a

vast water body

, which requires less in situ m easurement [7].

Remote sensing

is widely app lied to monitor the tropic state of

coastal water

s and provides important information for the devel-

opment of new

strategies used for the sustainable management

of these ecos

ystems [8], [9].

According to

the optical classiﬁcation by Morel and Plass

[10], the na

tural waters could be divided into two different

types, nam

ely, Case I and Case II waters. Case I waters are

dominated

by only one variable component (chlorophyll and

covaryin

g its pigments) that inﬂuences their optical properties,

while Cas

e II waters are characterized by chloro phy ll-a pig-

ment, to

tal suspended material (TSM), and colored dissolv ed

organi

c matters (CDOM). Coastal waters are dynamically

active

systems due to their tremendou s variation in topography

and ti

dal current, complicated patterns of front an d upwellin g

areas

, rapid changes in concentration caused by the mixing

of fr

esh and salt water, as well as the interaction between

lan

d and sea in a signiﬁcantly varying temporal and spatial

IEEE Proof

Web Versi

2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

scale [11]. Thus, the coastal w aters are referred to as Case II

waters. In these waters, several constituents of different origins

and optical b ehaviors have an inﬂuence on the water color

and vary more or less independently, i.e., the inherent optical

properties are determined not only by phytoplankton, but also

absorption and scattering by TSM and CDOM, which do not

covary with

.[9].Asaresult,thesamewater-leaving

reﬂectance in turbid waters is likely to correspond to different

concentration, because the optical properties of waters

are not only dom inated by

and cov arying detrital pig-

ments, but depend on other substances that don’t covary with

. S uch substances include CDOM, suspended sediments,

coccolithophores, detritus, and bacteria [12]. Consequently, it

is q uite difﬁcult to accurately retrieve

concentration from

turbid coastal waters, and present water color remote sensing

researches focus on how to improve the estimation accuracy of

concentration in coastal waters.

In general, the w ater-leaving reﬂectance detected by satellite

sensor is fairly weak. For example, the component of the mea-

sured reﬂectance, backscattering out of the w ater and transmit-

ting to th e top of the atmosphere, is less than 1 5% in the blue

band, particularly much smaller in the green and near-infrared

ranges [13]. Moore et al. [14] pointed out that approximately

35% of uncertainty could be generated in

concentration es-

timation while the atmospheric correction uncertainty could be

up to 5%. Moreover, as part of future oceanic plans, retrieval of

water-leaving reﬂectance with 5% uncertainty is recommended

to be one of the m ain m ission s of National Aeronautics and

Space Administration (N ASA) [15]. The NASA’s mission has

been achieved in Case I w aters, but may fail in the Case II wa-

ters, because turbid water constituents (TSM, bubbles, etc, or

bottom reﬂectance) can contribute signiﬁcant amounts of radi-

ance to the atmospheric correction bands. [16]–[18]. This will

induce large errors w hen the standard atmospheric correction

scheme is adopted, since the water-leaving radiance is assumed

to be negligible in n ear-infrared part of the spectrum [19]. Given

these factors, it is easy to understand why much attention has

been paid to the atmospheric correction.

Scale is not something new, nor is involved just by geog-

raphy, cartography or geographic information science. Scale

refers generally to the detailing proportion, by which informa-

tion can be observed, represented, analyzed and communicated

[20]. However, this old concept undergoes some fundam ental

new changes as a result of remote sensing techno log y. The

scale represents th e window of perception, the ab ility of ob-

servation, and the limitation on the perception or view of a

phenomenon [21]. Consequently, issu es regarding the scale

should be carefully dealt with in remote sensing. On one hand,

most retrieval models and algorithm s are basically derived at

small scales, implying that the water surface is homogeneous. If

large scales are utilized, certain uncertainty may arise [22]. O n

the other hand, the ecological process occurring in the aquatic

environment m ust be described at a certain scale, implying that

small scale information cannot be sim ply used as a substitute

for local scale inform ation. As a result, the scaling of geo-infor-

mation is inevitable to observe many disciplines. Scale effects

constrain the accuracy of retrieval and limit the development of

remote sensing applications [21]. Consequently, they become

the emerging problem of scientists’ concern in o cean color

remote sensing.

II. A R

EVIEW ON OCEAN COLOR SENSORS FOR

COASTAL WATER S

The prelim inary stage of ocean color sensors’ development

lasts from the late 1950s to early 1970s. It began in 1957 when

the ﬁrst artiﬁcial satellite, Sputnik, was launched by the Soviet

Union. Sputnik marked inn ovation and expansion in Earth ob-

servation as well as space exploration. In 1960, the ﬁrst Soviet

Union meteorological satellite was in orbit, and then a historic

conference was held in 1964 at Woods Hole Oceanographic In-

stitute, to discuss the po ssible ways in which Earth-observation

satellites might contribute to the science of oceanography [23].

Indeed it is surprising that the tremendous development of ocean

remote sensing in the last quarter of the 2 0th century was already

envisaged by a previous generatio n of oceanograp hers. Right

from the beginning it was recognized that the vantage point of

Earth orbit offered new spatial view of the ocean, and would re-

lease oceanograph ers from the sampling lim itation s of ship s and

buoys, since they had been restricted to considering only “prob-

lems that we re derived from the vertical distribution of proper-

ties at stations widely separated in space and time” [24]. During

the late 1960s and 1970s, exp erimental Earth-observing satel-

lites provided technological experience, and the meteorological

satellites began to play a part in operational forecasting [23]. In

this stage, m ost of the observations from space came in pho-

tographic form or televisio n picture, and the d igital image pro-

cessing technology was gradually applied in the remote sensin g

to analyze the largely qualitative information measured in space.

The period from the late 1970s to early 19 90s is a rapid

development stage. In 1978, two satellites were launched by

NASA with the aim not only to prove the concepts proposed

in the 1960s, b ut also to deliver quan titativ e, calibrated mea-

surements of the ocean. Two sensors, N ational Oceanic and

Atmospheric Administration (NOAA) sensor and Advanced

Very High Resolution Radiometer (AVHRR), were boarded

on th ese satellites. Clark et al. [25] demonstrated that the

concentration in the surface waters of the ocean could

be deduced from the spectrum of upw elling light from the

ocean, the “ocean color”. Subsequently, NASA launched the

Coastal Zone Color Scanner (CZCS) on Nimbus-7 in late 1978.

The CZCS w as a proof-of-concept mission with the goal of

measuring

concentration from space [13]. Unfortunately,

just as ocean scientists began to realize in the 1980s that

sensors in space were not sim ply interesting novelties but

essential for the development of their research on a g lobal

scale, the generous funding for space technology had gone

down th e drain [23]. In the late 1980s, new organ izations

and institutions joined ocean observation plans. For example,

both Europe and Japan inaugurated series of Earth-observing

satellites, which have been running until now, and yielded d ata

of rich value to oceanic researchers. In this stage, measurement

speciﬁcations such as “ocean optical protocols for satellite

color sensor validation” were established and implemented.

The “clear water” atmospheric correction algorith m and global

concentration estimation al gorithm s were constructed and

developed for retrieving

concentration on a global scale.

IEEE Proof

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CHEN et al.: SATELLITE REMOTE SENSING OF CHLOROPHYLL-A CONCENTRATION IN COASTAL WATERS 3

These achievements had a great impact on our knowledge of

ocean and the application of ocean color remote sensing was

dramatically increased in the early 1990s. Owin g to the im -

mature techn olo gies of sensor calibration in orbit, rad iometric

calibration, atmospheric correction, the accuracy of

con-

centration estimation was still n ot very high. Moreover, it was

difﬁcult to m eet the accuracy requirements in som e application

ﬁelds, such as the role of oceanic photosynthesis in coastal

waters. However, the achievement and experience were of great

signiﬁcance to the water color remote sensing and laid a solid

foundation for future researches.

The ocean color sensors related technology had taken its orig-

inal shape but was still immature during the period from the late

1990s to late 2000s. Since the launch of the Sea-viewing Wide

Field-of-view Sensor (SeaWiFS) on September 18, 1997, glob al

ocean color data had been available in near-real time to the

science community [26]. SeaWiFS was on a sun-synchronous

satellite with a 2801 km swath width, providing 2-day coverage

of the global o cean with a nadir resolution of

per pixel

[27]. These data were critical for und erstan din g the temporal

variability of marine ecosystems, especially with regard to

events such as El Niqo and La N iqa, and the role of oceanic

photosynthesis and primary productivity in th e Earth’s carbon

budget and climate [28]. The successful launching of SeaWiFS

had an epoch-marking signiﬁcance in marine science. Until

then [29], the SeaWiFS provided water-leaving reﬂectance with

uncertainty and concentration with 35% uncertainty

in Case I waters on a global scale. Shortly afterwards, tw o

Moderate Resolution Imaging Spectroradiometers (MODIS),

including Terra MODIS (since 1999) and Aqua MODIS (since

2002), were launched by NASA. These satellite sensors pro-

vided near-daily coverage of the global ocean. MO DIS was

typically 2–3 times m ore sensitive than SeaWiFS, which in turn

was approximately twice as sensitiv e as CZCS [13]. Analysis

suggested that the pigmen t concentration could be derived from

the radiance ratio of MODIS with an uncertainty of

[30]

in Case I w aters, a d ecrease of

of uncertainty compared

with SeaWiFS. With the later launching o f Nation al Polar-Or-

biting Op e ration al Environm ental Satellite System (NPOESS)

which carried the Visible/Infrared Imager/Radiometer Suite

(VIIRS) instruments, continuity in coverage was expected to

achieve throug h the NPO E SS Preparatory Mission (NPP). In

the last few years, China, Canada, India, and several other

countries have launched satellites providing marine science

data, besides, joint Sino-Euro dragon program has been very

successful [31]. Up to now, a large am ount of satellite data has

been available for remote sensing of

concentration.

However, it is still difﬁcult to ﬁn d out the satellite data with

optimal spectral and spatial resolution for

concentra-

tion estim ation in coastal regions, and this issue has aroused

people’s increasing attention. Even though some oceanic

observation satellite sensors, such as SeaWiFS and MODIS,

have been designed to measure water-leaving radiance related

concentration, these datasets cannot provide detailed

informatio n about the s patial distribution of

concentration

in coastal waters [32]. They could only be used to m onitor

on a small- or meso-scale, because of their coarse spatial

resolution

. Instead, some terrestrial observation

satellites with moderate spatial resolutions, such as Landsat,

Système Pour l’Observation de la Terre (SPOT), and Indian

Remote Sensor (IRS), are used for monitoring

concen-

tration in coastal aquatic environment. One of the common

characteristics for these sensors is that they possess high spatial

resolution

but with low signal-to-noise ratio (SNR ,

) and coarse sp ectral resolution [33]. Thus, the

applicability of these sensors in coastal aquatic environment

may be limited by two factors. First, it has long been known

that the bandwidth of absorption trough or scattering peak

of spectral curve related to pigment particle is very narrow

. The coarse spectral resolution makes broadband

sensors insensitive to the absorption or scattering features of

pigment particles, resulting in signiﬁcant errors; second, the

water-leaving radiance accounts for

or less of the total

radiance measured by the sensor in the blue band and 5% in

the red band. This characteristic requires that the water color

remote sensor must have the high SNR, in order to make the

water-leaving signal detectable theoretically. A lth oug h these

sensors may be considered as the potential sensors for remo te

quantiﬁcation of

concentration in coastal regions, further

study is still needed to demonstrate the app licability of these

sensors in coastal regions for

concentration estim ation.

It is well known that the coastal waters are the dynamic

aquatic ecosystem s where the bio-chemical event and process

evolve over short temporal scales. In order to detect, monitor,

and predict short term and regional oceanic phenom ena such as

red tides, yellow dust, ﬁshing ground information, sign iﬁcantly

improved SNR performance and spatial and temporal reso-

lutions are essential for ocean color remote sensing satellite.

Fortunately, Geostationary Ocean Color Im ager (GOCI) has

been developed to provide a monitoring of ocean color around

the Korean Peninsula from geostationary platforms. GOCI, the

ﬁrst ocean color imager to operate from geostationary orbit,

is designed to prov ide multi-spectral data to detect, monitor,

quantify, and predict short-tem changes of coastal ocean envi-

ronment for marine science research and application purpose

[34]. Its mission is to signiﬁcantly improve ocean observation

from low orbit serv ice by providing high frequency coverage.

Comparing with other ocean color satellites such as MODIS,

GOCI has a unique capability to observe the ocean and coastal

waters with high sp atial resolution (500 m) and temporal resolu-

tion (refresh rate: 1 hour). However, such advantages also meet

some technical challenges. The main challenges are radiom etry

and environment constraints. For ex amp le, geo stationary orbit

is much further from target than low orbit, reducing available

signal more than 500 times [35]. Thus, much more attention

stillshouldbepaidonGOCIinthefuture.

III. A

TMOSPHERIC CORRECTION FOR COASTAL WAT ER S

Generally, the water-leaving reﬂectance detected by satellite

sensor is fairly weak. For example, the component of the mea-

sured reﬂectance, backscattering out of the w ater and transmit-

ting to the top of the atmosphere is less than 10% in th e blue

band, and typically much smaller in the green and near-infrared

bands [36]. In order to acquire accurate

concentration

using remote sensing images, the impact of atmospheric scat-

tering and absorption should be accurately removed. According

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