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1 ANovel Algorithm to Estimate Algal BloomCoverage2 to Subpixel Resolution in Lake Taihu3 Yuchao Zhang, Ronghua Ma, Hongtao Duan, Steven A. Loiselle, Jinduo Xu, and Mengxiao Ma

4 Abstract—Remote sensing has often been used to monitor the5 distribution and frequency of floating algae in inland aquatic6 environments. However, due to the spatial resolution of the most7 common satellite sensors, accurate determination of algae coverage8 remains a major technical challenge. Here, a novel algorithm to9 estimate floating algae area to subpixel scales, denominated the

10 algae pixel-growing algorithm (APA), is developed and evaluated11 for a series of image data from the Moderate Resolution Imaging12 Spectroradiometer (MODIS). The algorithm utilizes the Rayleigh-13 corrected reflectance (Rrc) and a floating algae index (FAI) derived14 from Rrc in three spectral bands. Comparison with concurrent15 Landsat TM=ETMþ data indicate that the APA provides more16 accurate estimates of both algal bloom area and algal bloom17 intensity (i.e., floating algae coverage) (RSE ¼ 15:2 km2 and18 RE ¼ 9:9%), compared to other commonly used methods such as19 the linear unmixing algorithm (LA). Furthermore, this study con-20 firms thatFAI is a better indexwith respect to normalized difference21 vegetation index (NDVI) and enhanced vegetation index (EVI) for22 the estimation of algae area coverage, especially when combined23 with the APA. Finally, the study provides a theoretical basis for the24 objective assessment of bloom severity in complex inland25 waterbodies.

26 Index Terms—Algae pixel-growing algorithm (APA), algal27 blooms, floating algae index (FAI), Landsat, linear unmixing28 decomposition, Moderate Resolution Imaging Spectroradiometer29 (MODIS).

30 I. INTRODUCTION

31 I N RECENT decades, many major lakes have experienced32 an increased occurrence of algal blooms, including Lake33 Victoria in Africa, Lake Okeechobee in the United States, and34 Lake Taihu in China [1]–[3]. Compared to traditional in situ

35sampling and laboratory analysis, satellite-derived optical and36thermal data provide synoptic and high frequency information,37useful for early warning, real-time monitoring, and postbloom38evaluations of bloom events [1]. Over the last three decades,39there have been many important developments in the use of40remotely sensed data to estimate phytoplankton biomass con-41centrations and the associated bio-optical properties [4], [5].42This has been particularly important where eutrophication and43floating algal mats have compromised lake functioning.44Information regarding the coverage and thickness of algal mats45provides fundamental information necessary for biomass esti-46mates, which are more appropriate than traditional chlorophyll-a47concentration measurements for decision-making. Choosing an48appropriate algal bloom algorithm is the first step to estimate49bloom coverage.50Single-bands, band ratios, and band differences have been51used in a similar manner to land surface vegetation detection52algorithms [6]–[8]. Indices such as the normalized difference53vegetation index (NDVI), the enhanced vegetation index (EVI),54the normalized difference algae index (NDAI), maximum chlo-55rophyll index (MCI), red tide index (RI), the floating algae index56(FAI), and the ocean surface algal blooms index (OSABI) have57been utilized to identify floating algal blooms [9]–[15]. But in58practical application, NDVI, EVI, and FAI are much more59popular than the other indices. However, it has been found that60NDVI and EVI are particularly sensitive to aerosol characteristics61(type and thickness), sun glint, and solar viewing geometry with62respect to FAI, which has been found to be relative stable under63different environmental and observing conditions [13]. Mini-64mizing the atmospheric effects is vital to time-series study and65characterization on algal blooms and water optical properties in66turbid inland lakes [16], [17]. Thus, FAI appears to be a67reasonable choice to detect and quantify bloom coverage.68However, algalmats can be smaller than the satellite pixel size,69leading to uncertainties in the total bloom coverage estimates.70Indeed, algal blooms may present complex temporal and spatial71characteristics, which may limit the use of satellite data. Most72algorithms use data from the Landsat TM=ETMþ, Moderate73Resolution Imaging Spectroradiometer (MODIS), and Medium74Resolution Imaging Spectrometer (MERIS), with spatial resolu-75tions of 30 m, 250=500=1000 m, and 300=1200 m with tempo-76ral resolutions of 16 days, 1 day, and 3 days, respectively. Among77these, MODIS has the best temporal resolution, but its best78spatial resolution is 250m,whichmay not detect small patches of79floating algae and may lead to uncertainties in area coverage80estimates. The ability to detect and quantify surface features at81subpixel scales would improve the use of satellite systems for82bloom monitoring and management.

1939-1404 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Manuscript received September 02, 2013; revised January 22, 2014; acceptedMay 12, 2014. This work was supported in part by the National High TechnologyResearch and Development Program of China (2014AA06A509), in part by theNational Natural Science Foundation of China under Grant 41101316, Grant41171271, and Grant 41171273, in part by Scientific Data Sharing Platform forLake and Watershed, in part by Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences (NIGLAS), in part by the “135” Program ofNIGLAS (NIGLAS2012135010 and NIGLAS2012135014), and in part by theDragon 3 program (10561). MODIS data were provided by the U.S. NASA, andthe Landsat TM=ETMþ data were provided by the U.S. Geological Survey.(Corresponding author: Ronghua Ma.)

Y.Zhang,R.Ma,H.Duan, and J.Xu arewith the StateKeyLaboratory of LakeScience and Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing 210008, China (e-mail: rhma@niglas.ac.cn).

S. A. Loiselle is with the CSGI, University of Siena, Siena 253100, Italy(e-mail: loiselle@unisi.it).

M. Ma is with the State Key Laboratory of Pollution Control & ResourcesReuse, School of the Environment, Nanjing University, Nanjing 210023, China(e-mail: mmxlb369@163.com).

Color versions of one ormore of the figures in this paper are available online athttp://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2327076

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f83 Bloom detection and quantification at subpixel scales depend84 on the feature size (the relative percentage of the algae bloom85 within a pixel) and the sensitivity of the satellite sensor (signal to86 noise ratio). The accuracy of algae coverage estimates is strongly87 influenced by mixed pixels containing both algae and algae free88 (water) surfaces. When the total lake area coverage is small,89 mixed pixels may represent a large portion of the total algae90 pixels. Hu et al. [18] have used a linear unmixing algorithm (LA)91 to estimate the total algae area coverage in Lake Taihu, an92 eutrophic lake in East China. Shanmugam et al. [15] have also93 applied the similar algorithm to detect ocean surface algal94 blooms in subpixel scales. However, this algorithm needs to be95 further improved to allow for the determination of algae coverage96 at subpixel scales and more accurate estimation of threshold97 values for algae and nonalgae areas of optically complex eutro-98 phic lakes.99 In China, satellite-based estimations of algal blooms (cover-

100 age, distribution, and duration) are used by environmental101 management agencies at both state and local levels for a variety102 of purposes, from policy implementation and water management103 (including the physical removal of the algae mats). Coverage104 estimates are generated by different agencies using a variety of105 methods, most of which rely on human interpretation of the106 manually adjusted satellite images (to account for variable107 atmospheric and observing conditions as well as algorithm108 artifacts). The large degree of subjectivity has resulted in incom-109 patible estimates and inconsistent recommendations among110 agencies. There is a clear need for an objective method, which111 would be valid for variable conditions, including the partial algae112 coverage at subpixel scales. An objective approach would allow

113different agencies and groups to generate consistent estimates114based on the same data.115In this study, we developed a new algorithm [algae pixel-116growing algorithm (APA)] to estimate algae coverage at subpixel117scales usingMODIS data.We evaluated the accuracy of theAPA118using concurrent higher resolution (30 m) Landsat TM=ETMþ119data by comparing their spatial distributions and coverage120statistics. In addition, several practical issues in using MODIS121for bloom monitoring were examined, including an automatic122and objective determination of the pure algae threshold as well as123a method to adjust pixels contaminated by striping noise. The124ultimate goal is to provide an objective method to determine125bloom coverage and size to establish a long-term baseline record126and real-time assessment of bloom severity to support manage-127ment decisions.

128II. STUDY AREA AND DATA SOURCES

129A. Study Area

130With an extension of 36800 km2 and encompassing major131population centers such as Shanghai, Suzhou, Wuxi, and132Changzhou, the Lake Taihu basin is one of the most highly133developed areas of China (Fig. 1). At the center of the Basin is134Lake Taihu, China’s third-largest freshwater lake (2400 km2).135The lake provides drinking water for more than 2 million people,136and sustains important fisheries including crabs, carp, and eels137[2]. With the rapid economic development in the last decades,138Lake Taihu has become highly eutrophic with frequent cyano-139bacteria blooms as a consequence of increased nutrient loads

F1:1 Fig. 1. Study site was Lake Taihu, China lying in the Yangtze River Delta. The lake is often divided into several lake segments, which include Zhushan Bay (ZB),F1:2 Meiliang Bay (MB), Gong Bay (GB), West Lake (WL), South Lake (SL), Central Lake (CL), East Lake, and East Taihu. The brown areas represent major cities.

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140 [19]–[21]. In 2007, a massive algal bloom led to a drinking water141 shortage for more than 1 million people [2], [22]. Although the142 Chinese government recognized the importance of managing143 nutrient loads and monitoring lake conditions, the long-term144 management of the Lake Taihu Basin is still in its infancy.145 Since 2007, remote sensing has been used to monitor the146 occurrence of algal blooms as part of an early warning system147 with daily communications to national and local decision148 makers.

149 B. Satellite Data

150 TerraMODIS data at 250 and 500m resolutions and Landsat151 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic152 Mapper Plus (ETMþ) data at 30 m resolution were used in this153 study. Landsat TM=ETMþ Level-1 data were obtained from154 the Earth Resources Observation and Science Center (http://155 glovis.usgs.gov).MODIS data were obtained from the LAADS156 website of the U.S. NASA Goddard Space Flight Center.157 Twenty-four pairs of concurrent (i.e., same day within a hour)158 MODIS and TM=ETMþ data (Table I) with low cloud cover-159 age ( <10%) were georeferenced (UTM projection) with an160 error of <0:5 pixel. The 500-m resolution MODIS data at161 1240 nm and 1640 nm were resampled to 250 m resolution (to162 match the 645-nm data). TM=ETMþ data were atmospheri-163 cally corrected using the radiative transfer calculations based164 on the second simulation of the satellite signal in the solar165 spectrum [23]. MODIS data were corrected by removing the166 molecular (Rayleigh) scattering effects, and then converted to167 Rayleigh-corrected reflectance (Rrc) following Hu et al. [24].168 The lake segment of East Lake Taihu was excluded from the169 bloom analysis as this area is characterized by aquatic macro-170 phytes growing from the bottom [25].

171 III. METHODOLOGY

172 A. Floating Algal Index Algorithm

173 The FAI algorithm for MODIS proposed by Hu [13] utilizes a174 baseline subtraction, defined as the difference between reflec-175 tance at 859 nm (vegetation “red edge”) and a linear baseline

176between the red band (645 nm) and short-wave infrared band177(1240 or 1640 nm)

FAIMODIS ¼ Rrcð859Þ � R0rcð859Þ (1)

R0rcð859Þ ¼ Rrcð645Þ þ Rrcð1240Þ � Rrcð645Þ½ �

� ð859� 645Þ=ð1240� 645Þ178where Rrc is defined as the difference between the calibrated179sensor radiance [after adjustment for ozone (Lt

�) and other180gaseous absorption] and Rayleigh reflectance (Rr) [24]

Rrc ¼ �Lt�=F0 cos �0 � Rr (2)

181whereF0 is the extraterrestrial solar irradiance at data acquisition182time and �0 is the solar zenith angle.183FAI has been used to map bloom size to derive long-term184statistics [18]. However, the estimation of bloom size is subject to185large errors if a single threshold is used to determine the bloom186presence or absence. Hu et al. [18] used an LA to estimate partial187coverage at subpixel scales (�pixel) through a statistical analysis188of the 9-year MODIS FAI dataset with FAI thresholds for189100% bloom coverage (FAIalgae) and 0% bloom coverage190(FAInonalgae). When FAInonalgae < FAIpixel � FAIalgae, LA191is expressed as

�pixel ¼ ðFAIpixel � FAInonalgaeÞ=ðFAIalgae � FAInonalgaeÞ:(3)

192B. Algae Pixel-Growing Algorithm (APA)

1931) Theoretical Considerations:Consideringn high-resolution194pixels (Rrc

Hi) that make up a low-resolution pixel (RrcLo), where

195RrcLo is the arithmetic mean of all Rrc

Hi

RrcLo ¼ Rrc

HiðiÞ: (4)

196Similarly, there exists a relationship between the low-197resolution pixel and high-resolution pixels (or subpixels198within the low-resolution pixel) for FAI

FAIpixel ¼ FAIsubpixel: (5)

199If we assume that, in a 3� 3 pixel window, the central pixel is200a function themaximum andminimumFAI values present within201the window (Fig. 2), (5) becomes

FAIcenter ¼ � � FAImax þ 1� �ð ÞFAImin (6)

TABLE IT1:1 ACQUISITION DATE OF TERRA/MODIS AND LANDSAT TM (MARKEDWITH ASTERISK)/T1:2 ETMþ DATA USED IN THIS STUDY

F2:1Fig. 2. In the APA, a 3� 3windowwas chosen randomly from aMODIS image.F2:2Gray-scale shows the FAI value of each pixel. The central pixel was divided intoF2:3two kinds of subpixels which have the same maximum and minimum FAI valuesF2:4of the other pixels present in the 3� 3 window.

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f202 where � is the decomposition parameter of the 3� 3window that203 is determined based on the relationship between the known FAI204 values (center, max, and min).205 In a mixed pixel, the algae coverage is defined as the propor-206 tion (�) of the pixel covered by floating algae such that

FAI ¼ � � FAIthreshalgae þ ð1� �Þ � FAIthreshwater

¼ FAIthreshalgae � FAIthreshwater

� �� �

þ FAIthreshwater (7)

207 where FAIthreshalgae and FAIthreshwater are the FAI thresholds for pure208 algae (100%) and nonalgae water (0%) pixels, respectively.209 Assuming thatFAIthreshalgae andFAIthreshwater are constant in a 3� 3210 window, the FAI of central pixel could be expressed as follows:

FAIcenter ¼ FAIthreshalgae � FAIthreshwater

� �� �center

þ FAIIthreshwater : (8)

211 The FAI of max and min pixels in a 3� 3 window could also212 be similarly expressed in the same way. Based on (6)–(8), FAI213 has a linear relationship with the floating algae coverage � in the214 mixed pixel

�center ¼ � � �max þ 1� �ð Þ � �min (9)

215 where �max and �min are the algae coverage of pixels with216 maximum and minimum FAI values in a 3� 3 window,217 respectively.

2182) APA Implementation and Data Processing: There are three219iterative steps in applying the APA to MODIS Rrc data (Fig. 3).2201) The preprocessing step identifies the location of the maximum221and minimum FAI value in a roaming 3� 3 window, and then222calculates � for the central pixel using (6). 2) The “seed” pixels223are then identified in twoways: a) if local pixels are found to have224their FAI values > FAIthreshalgae , these pixels are taken as the

225seed pixels and their algae coverage are defined as 1.0 (100%);226b) if no local pixels have FAI values > FAIthreshalgae , then local

227center pixels with maximum FAI values in the 3� 3window are228taken as the seed pixels. The coverage is determined via (3) using229a pure-algae endmember threshold of FAIthreshalgae . The coverage

230of other pixels is assigned 0.0 in the initial estimation. 3) Thefinal231algae coverage at each pixel is determined iteratively using (9)232whenever the maximum coverage in each 3� 3 window is not233zero. The iteration is terminated when two consecutive calcu-234lations yield similar results. In practice, three iterations were235necessary before the termination (see below). Through iterations,236the algal bloom coverage expands from the initial pure algae

F3:1 Fig. 3. Flowchart of the APA process.

F4:1Fig. 4. Changes in the MODIS-derived algae coverage area with increasingF4:2iterations (steps) of the APA. The stars mark the true algal bloom area identifiedF4:3from the corresponding Landsat TM=ETMþ data.

F5:1Fig. 5. Estimation of floating algae coverage using the APA (the aquaticF5:2macrophytes area marked with gray color), from the initial estimation (step 1),F5:3the determination of the growing points (step 2), and the final estimation ofF5:4algal coverage after 3 iterations (step 3). A and B are the ends of a transect withinF5:5the lake.

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237 pixels or high-coverage pixels to low-coverage pixels, according238 to the relationship between adjacent pixels in a 3� 3 pixel239 window. For very thin algae layers, the measured reflectance240 may always be confused with surface waters no algal bloom241 but with high concentrations of chlorophyll-a. However, by242 including the constraints related to adjacent pixels with high-243 algae coverage, this error is reduced unless these conditions244 are present over a large geographic area. If such conditions are245 not present, the problematic pixel is assigned as nonalgae246 pixel. Total algae coverage is determined by the no zero algae247 coverage.248 3) FAI Threshold for Pure Algae Coverage: The APA results249 are sensitive to the choice of theFAIthreshalgae and to the termination

250 conditions of the iterative calculation. The FAIthreshalgae was

251 determined from concurrent higher resolution TM=ETMþ252 observations, but could also be estimated using in situ253 reflectance spectra of the pure algae. Due to the lack of in situ254 reflectance data covering the spectral range of > 1000 nm, the255 former method was used. With a higher spatial resolution,256 Landsat TM=ETMþ data provide a better resolution of the257 pure algae pixels. We assumed that there were only pure algae258 pixels and pure nonalgae pixels existing in LandsatTM=ETMþ259 images. A pixel’s gradient was defined as the FAI difference260 from the adjacent pixels in a 3� 3window. It was found that the261 pixels associated with the maximum gradient determined mode262 could be used to separate floating algae from other nonbloom263 waters very well [18]. Using the large gradient in FAI values264 across the algae and nonalgae boundary, the mean value of all265 pixels associated with the gradient was used to represent the266 TM=ETMþ threshold value for pure algae. After resizing from267 30 to 250 m (to match the MODIS resolution), the partial algae268 coverage in the new low-resolution pixel could be determined.269 If all TM=ETMþ pixels within the low-resolution pixel had270 100% algae coverage, the low-resolution pixel could be iden-271 tified as the pure algae pixel. From all these low-resolution pure272 algae pixels, the FAI value (�2� standard deviations) was273 used to represent the threshold value for pure (100%) algae274 coverage (FAIthreshalgae ).

275 4) The Condition to Terminate Iterations: For each MODIS276 pixel, the algae coverage is a function of the FAI maximum and277 minimum in each 3� 3 window. For each iteration, the278 estimated coverage increases until it reaches a stable value279 (Fig. 4). The termination condition allows the calculation to

280conclude when the absolute difference or relative difference of281consecutive steps reaches a minimum value. For variable bloom282areas (from several hundreds to >1000 km2) with millions of283algae pixels, the absolute difference or relative difference284inconsecutive steps can be significantly different between285images. Importantly, it was found that the smallest relative286difference between MODIS and TM=ETMþ data occurred287after three iterations in all 24 concurrent MODIS/TM image288pairs (Fig. 4). This is a natural consequence of the fact that mixed289pixels are usually found adjacent to pure-algae pixels or high290algae-coverage pixels. The APA approach builds algae coverage291from pure-algae pixels with respect to nonalgae pixels (or from292high-coverage pixels to low-coverage pixels).293An example of these calculation steps is presented for a lake294transect characterized by pure algae to nonalgae water in Lake295Taihu on September 24, 2011 (Fig. 5). The algae coverage of296some pixels varied at every iteration. In general, the algae297coverage of mixed pixels increased gradually, as the algae298coverage of the FAI maximum pixel or FAI minimum pixel299changed in each 3� 3 pixel. From the algae coverage distribu-300tion of each iteration, the algal bloom coverage expanded from301the pure algae pixels or high-coverage pixels to low-coverage302pixels, based on the condition of adjacent pixels in a 3� 3 pixel303window. In total, the algae coverage will be ignored if there is no304algal bloom existing in adjacent pixels.

F6:1 Fig. 6. Comparison of algal bloom coverage derived from several different methods ( coverage interval ¼ 0:005): the full line marks the true bloom coverage usingF6:2 30 m TM=ETMþ data resized to 250 m; the dashed lines show the estimate of bloom coverage from the concurrent MODIS image using the APA; the dotted linesF6:3 represent the estimate of bloom coverage from the same MODIS image using an LA.

F7:1Fig. 7. North south profile of estimated algae coverage (0%–100%) from resizedF7:2ETMþ pixels (all at a 250-m resolution) andMODIS using the APA and an LA,F7:3along a hypothetical transect fromMeiliang Bay to Tiaoxi River (white line in theF7:4inset image). Each point represents an arithmetic mean of 3� 3 pixels along theF7:5transect.

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f305 IV. RESULTS AND VALIDATION

306 A. Algal Bloom Coverage Estimations

307 Algal bloom coverage identified from the 24 TM=ETMþ308 images ranged from 14.8 to 505:7 km2. The relative standard309 error (RSE) between MODIS estimates and TM=ETMþ esti-310 mates was 15.2 and 24:8 km2, respectively, for the APA and LA311 methods, with their corresponding relative error (RE) of 9.9%312 and 17.3%. The results of APA and LA presented here should be313 interpreted as MODIS pixels with both 100% and partial algae314 bloom coverage.315 If we ignored the partial algae bloom coverage and took the316 mean FAI value of pixel with 10% algae coverage as the317 threshold of algae pixel and nonalgae pixel, algae coverage area318 fromMODIS images is increased by >40%, and its correspond-319 ing RE betweenMODIS estimates andTM=ETMþ estimates is320 more than 30%. Due to the partial coverage provided by APA321 and LA, their errors are much smaller (as gauged by the high-322 resolution TM=ETMþ data). APA provided more accurate esti-323 mates than the LA, with the already small errors nearly halved.324 A comparison of MODIS algal bloom coverage with paired325 TM=ETMþ coverage, resized from 30 to 250 m, gave similar326 results (Fig. 6). When the pure algae pixels of MODIS were set327 with resized TM=ETMþ, LA over-estimated the number of328 pixels with partial algae coverage. The over-estimation indicated329 that the nonalgae threshold was low. The APA determination

330provided a similar frequency distribution for MODIS data with331resized TM=ETMþ images.332In order to examine the algae coverage distributions derived333from different methods, an example of the MODIS results on334September 24, 2011 is shown in Fig. 7. An artificial transect from335Meiliang Bay (North Lake) to the mouth of Tiaoxi River (South336Lake) was chosen to show a range of algae coverage. In order to337minimize the impact of image noise and geometric correction338error, the average coverage of the 3� 3 window was used to339represent the central pixel along the transect. Both APA and LA340yielded reasonable results when compared to ETMþ. However,341bothAPA and LAunderestimated algae coveragewhen the algae342coverage was >40% in the mixed pixels, and overestimated343coverage when the percent coverage was very low. On average,344the difference betweenMODIS andETMþwas 13.7%when the345APA was used and 15.4% when the LA was used.

346B. Sensitivity Analysis

347In this study, the MODIS threshold of pure algae was deter-348mined from concurrent TM=ETMþ observations. To examine349how sensitive the MODIS results were to the threshold estima-350tion, three MODIS images in different years (April 6, 2007;351October 20, 2009; May 5, 2012) with different bloom coverage352were compared with TM=ETMþ (Fig. 8). The bloom area was353found to increase sharply when the threshold for pure algae354coverage was lower than the mean minus the standard deviation.355The results confirm that both the APA and LA significantly356overestimated the algae coverage if the pure algae threshold was357low. A higher threshold (mean plus standard deviation) resulted358in a slight under estimation, which was less sensitive to further359changes of the threshold.360An artificial sensitivity test was also conducted by varying the361pure algae threshold by �10% to þ10%. The total algae area362from the APA estimates changed þ15:1% and �13:5% with363respect to TM=ETMþ values, whereas the area from the LA364estimates changedþ9:7% and�8:3%. In general, the change in365the total algae area estimates was proportional to changes in the366threshold values.

367V. DISCUSSIONS

368A. Lake Specific FAI Thresholds

369There are two options to determine FAI thresholds for long-370term applications: 1) threshold based on multi-image statistics

F8:1 Fig. 8. Comparison of the floating algae area estimated fromTM=ETMþ andMODIS using the two algorithms [the APA and an (LA)] when different FAI thresholdF8:2 values are used to represent pure (100%) algae coverage. u and δ represent the mean and standard deviation of the threshold values.

F9:1 Fig. 9. Linear relationship between MODIS Rrcð1240Þ and Rrcð1650Þ fromF9:2 several MODIS images.

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f371 [18]; and 2) threshold based on the individual images. Because372 environmental factors such as air temperature, wind speed and373 direction, and hydrological conditions all influence algal aggre-374 gation characteristics (i.e., algal bloom thickness) [26], the pure375 algae threshold (as well as the pure water threshold) may vary376 between images. Therefore, an image-specific threshold is more377 accurate. In this study, this was achieved by comparing concur-378 rentMODIS andTM=ETMþ image pairs. However, for routine379 monitoring, TM=ETMþ data are not available at the MODIS380 temporal frequency and the pure algae threshold needs to be381 determined in alternative ways. Because the red-edge effect382 would lead to elevated reflectance in the near-IR and shortwave383 IR, the pure algae pixel was determined when Rrc (1240)384 was greater than Rrc (645) and FAI was great than 0. Com-385 parison with the thresholds determined from the MODIS and386 TM=ETMþ pairs showed consistency, allowing for operational387 use of APA with MODIS data in absence of TM=ETMþ.

388 B. MODIS Striping Noise

389 One of theMODIS bands used to derive the FAI, the 1240-nm390 band on MODIS Terra, suffers from significant striping noise.391 This would seriously affect the FAI value andAPA performance.392 In this study, the noise-contaminated data were replaced with393 coincident the 1640-nm data, using the linear relationship be-394 tween the two bands derived from the high-quality (undisturbed)395 pixels of the same image. A significant linear relationship396 between Rrcð1240Þ and Rrcð1640Þ, especially in algae-covered

397areas was evident for all the images examined (Fig. 9). Although398the relationship varies among images, an image-specific rela-399tionship to derive Rrcð1240Þ for noise-contaminated pixels can400be used in FAI calculations. The results in indicate that such a401correction yields different statistics in algae area coverage402(Fig. 10).

403C. Comparison of FAI, NDVI, and EVI

404FAI has been used to calculate algae coverage because of its405reduced sensitivity to variable environmental and observational406conditions [18]. However, other indexes have also been widely407utilized. Among these are NDVI [27] and EVI [28], defined as

NDV I ¼ RNIR � RREDð Þ= RNIR þRREDð Þ (10)

EV I ¼ G � RNIR � RREDð Þ=RNIR þ C1 � RRED � C2 � RBLUE þ C3ð Þ (11)

408where RNIR, RRED, and RBLUE are the reflectance in the near-409infrared (NIR), red and blue bands; G is the gain factor, and C1,410C2, and C3 are the pixel-independent coefficients to compensate411for aerosol effects and vegetation background. For MODIS data,412G ¼ 2:5,C1 ¼ 6,C2 ¼ 7:5, andC3 ¼ 1. Given these alternative413choices on the vegetation indexes, we examined if FAI is the best414choice to derive the algae coverage with the APA.415A MODIS Terra image for August 10, 2013 was used to416evaluate algae coverage using FAI, NDVI, and EVI, where sun417glint influences part of the image (Fig. 11). The FAI image

F10:1 Fig. 10. Histogram distributions of Rrcð1240Þ before and after removal of the Rrcð1240Þ striping noise.

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f418 displayed a near homogeneous water background regardless of419 the sun glint. In contrast, the NDVI image revealed higher420 variability in the water background in areas where sun glint was421 significant. Sun glint led to a significant increase in the NDVI422 values, making it more difficult to differentiate algae pixels from423 the water background. The performance of EVI was much better424 than NDVI, but still showed a higher sensitivity to sun glint425 contamination with respect to FAI. The algae areas determined426 from the three images, all using the APA, were 178.04, 353.68,427 and 236:26 km2, respectively. Thus, when sun glint was signifi-428 cant, bothNDVI and EVI overestimated algae coverage. Because429 of the low-latitude (�31N) location of Lake Taihu, most images430 during the summer months contain sun glint. Thus, FAI is a431 preferred index for its tolerance to this as well as to the interfer-432 ence of thick aerosols (not shown here) for long-term, routine433 monitoring of the bloom coverage.

434 D. MODIS-Based Data Record and Event-Driven Response

435 The APA approach, together with the statistically determined436 FAI thresholds, provides an objective method to estimate the437 bloom severity. This can be used to estimate bloom spatial extent438 and effective algae coverage as well as to compare current bloom439 conditions against historical baselines determined using the same440 objective method. This will lead to more accurate estimates of441 the bloom severity in Lake Taihu in near real-time aswell as pave442 the way to obtain consistent answers from various research443 groups and management agencies.

444VI. CONCLUSION

445Remote sensing has beenwidely used to assess algal blooms in446Lake Taihu, but the methods and parameterization varied sub-447stantially between different users, making it difficult to compare448results and agree on a common action. The present approach to449develop and validate a more objective method to characterize450the bloom severity provides a novel to meet this challenge. The451results, based on the MODIS FAI data, showed improved452performance over other methods or indices. The APA approach453serves as an objective and more accurate method to determine454bloom severity in both near real-time monitoring and historical455analysis, thereby improving the capacity of decision makers to456manage Lake Taihu and its basin.

457ACKNOWLEDGMENT

458The constructive comments from two anonymous reviewers459are greatly appreciated.

460REFERENCES

461[1] A. Reinart and T. Kutser, “Comparison of different satellite sensors in462detecting cyanobacterial bloom events in the Baltic Sea,” Remote Sens.463Environ., vol. 102, no. 1–2, pp. 74–85, 2006.464[2] L. Guo, “Ecology-doing battle with the green monster of Taihu Lake,”465Science, vol. 317, no. 5842, p. 1166, 2007.466[3] S. Q. Zhao et al., “The 7-decade degradation of a large freshwater lake in467central Yangtze River, China,” Environ. Sci. Technol., vol. 39, no. 2,468pp. 431–436, 2005.

F11:1 Fig. 11. Comparison among MODIS FAI, NDVI, and EVI values for MODIS data obtained on August 10, 2013 and corresponding algae coverage distributionsF11:2 (derived from the APA). Areas with aquatic macrophytes are marked in gray.

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469 [4] S. Sathyendranath, “Remote sensing of ocean colour in coastal, and other470 optically-complex waters,” International Ocean-Colour Coordinating471 Group, pp. 47–76, 2000.472 [5] Z. P. Lee, “Remote sensing of inherent optical properties: Fundamentals,473 tests of algorithms, and applications,” International Ocean-Colour474 Coordinating Group, pp. 43–93, 2006.475 [6] P. S. Richard and C. T. Michelle, “Remote sensing of harmful algal476 blooms,” Remote Sens. Coastal Aquat. Environ., vol. 7, pp. 277–296, 2005.477 [7] H. T.Duan, S.X.Mang, andY. Z.Mang, “Cyanobacteria bloommonitoring478 with remote sensing in Lake Taihu (in Chinese with English abstract),”479 J. Lake Sci., vol. 20, no. 2, pp. 145–152, 2008.480 [8] R. H. Ma et al., “Spatio-temporal distribution of cyanobacterial blooms481 based on satellite imageries in Lake Taihu, China (in Chinese with English482 abstract),” J. Lake Sci., vol. 20, no. 6, pp. 687–694, 2008.483 [9] J. F. R. Gower, “Red tide monitoring using AVHRR HRPT imagery484 from a local receiver,” Remote Sens. Environ., vol. 48, no. 3,485 pp. 309–318, 1994.486 [10] J.Gower, S.King,G.Borstad, andL.Brown, “Detection of intense plankton487 blooms using the 709 nm band of theMERIS imaging spectrometer,” Int. J.488 Remote Sens., vol. 26, no. 9, pp. 2005–2012, 2005.489 [11] Y. H. Ahn and P. Shanmugam, “Detecting the red tide algal blooms from490 satellite ocean color observations in optically complex Northeast-Asia491 Coastal waters,” Remote Sens. Environ., vol. 103, no. 4, pp. 419–437, 2006.492 [12] C. M. Hu and M. X. He, “Origin and offshore extent of floating algae in493 olympic sailing area,” EOS, Trans. Amer. Geophys. Union, vol. 89, no. 33,494 pp. 302–303, 2008.495 [13] C. M. Hu, “A novel ocean color index to detect floating algae in the global496 oceans,” Remote Sens. Environ., vol. 113, no. 10, pp. 2118–2129, 2009.497 [14] W. Shi andM.Wang, “Green macroalgae blooms in the Yellow Sea during498 the spring and summer of 2008,” J. Geophys. Res., vol. 114, no. C12,499 pp. 1–10, 2009.500 [15] P. Shanmugam, M. Suresh, and B. Sundarabalan, “OSABT: An innovative501 algorithm to detect and characterize ocean surface algal blooms,” IEEE J.502 Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 4, pp. 1879–1892,503 Aug. 2013.504 [16] J. Chen, W. T. Quan, M. W. Zhang, and T. W. Cui, “A simple atmospheric505 correction algorithm for MODIS in shallow turbid waters: A case study in506 Taihu Lake,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6,507 no. 4, pp. 1825–1833, Aug. 2013.508 [17] M. Wang, S. Son, Y. Zhang, and W. Shi, “Remote sensing of water optical509 property for China’s inland Lake Taihu using the SWIR atmospheric510 correction with 1640 and 2130 nm bands,” IEEE J. Sel. Topics Appl. Earth511 Observ. Remote Sens., vol. 6, no. 6, pp. 2505–2516, Dec. 2013.512 [18] C. M. Hu et al., “Moderate Resolution Imaging Spectroradiometer513 (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,”514 J. Geophys. Res.-Oceans, vol. 115, C04002, 2010, doi: 10.1029/515 2009JC005511.516 [19] Q. M. Cai, X. Y. Gao, Y. W. Chen, S. W. Ma, and M. Dokulil, “Dynamic517 variations of water quality in Lake Taihu and multivariate analysis of its518 influential factors,” Chin. Geogr. Sci., vol. 6, no. 4, pp. 364–374, 1996.519 [20] Y. W. Chen, B. Q. Qin, K. Teubner, and M. T. Dokulil, “Long-term520 dynamics of phytoplankton assemblages: Microcystis-domination in Lake521 Taihu, a large shallow lake in China,” J. Plankton Res., vol. 25, no. 4,522 pp. 445–453, 2003.523 [21] J. Chen and W. T. Quan, “Using Landsat/TM imagery to estimate nitrogen524 and phosphorus concentration in Taihu Lake, China,” IEEE J. Sel. Topics525 Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 273–280, Feb. 2012.526 [22] H. T. Duan et al., “Two-decade reconstruction of algal blooms in China’s527 Lake Taihu,” Environ. Sci. Technol., vol. 43, no. 10, pp. 3522–3528, 2009.528 [23] E. F. Vermote et al., “Atmospheric correction of visible to middle-infrared529 EOS-MODIS data over land surfaces: Background, operational algorithm530 andvalidation,” J.Geophys. Res.-Atmos., vol. 102, no.D14, pp. 17131–17141,531 1997.532 [24] C. Hu et al., “Assessment of estuarine water-quality indicators using533 MODIS medium-resolution bands: Initial results from Tampa Bay,534 Florida,” Remote Sens. Environ., vol. 93, no. 3, pp. 423–441, 2004.535 [25] R. Ma, G. Jiang, H. Duan, L. Bracchini, and S. A. Loiselle, “Effective536 upwelling irradiance depths in turbid waters: A spectral analysis of origins537 and fate,” Opt. Express, vol. 19, no. 8, pp. 7127–7138, 2011.538 [26] F. X. Kong and G. Gao, “Hypothesis on cyanobacteria bloom-forming539 mechanism in large shallow eutrophic lakes,” Acta Ecol. Sin., vol. 25, no. 3,540 pp. 589–595, 2005.541 [27] J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring542 vegetation systems in the Great Plains with ERTS,” in Proc. 3rd ERTS543 Symp. NASA SP-3511, 1973, pp. 309–317.544 [28] A. R. Huete and C. Justice,MODIS Vegetation Index (MOD13) Algorithm545 Theoretical Basis Document Version 3, 1999.

546Yuchao Zhang received the Ph.D. degree in environ-547mental science from Nanjing University, Nanjing,548China, in 2008.549She has been a Researcher in Nanjing University550for 12 years. Since 2012, she has been an Associate551Professor with Nanjing Institute of Geography and552Limnology (NIGLAS), ChineseAcademy of Sciences553(CAS), Nanjing, China. Her research interests include554remotely sensedmonitoring of algal blooms andwater555color remote sensing in inland lakes.

556Ronghua Ma received the M.S. degree in surveying557and mapping from Xi’an University of Science and558Technology, Xi’an, China, in 1999, and the Ph.D.559degree in cartography and GIS from Nanjing Univer-560sity, Nanjing, China, in 2002.561He has been with Nanjing Institute of Geography562and Limnology (NIGLAS), Chinese Academy of563Sciences (CAS) since 2002, where he is currently a564Professor of Lake Remote Sensing. His research565interests include water environment remote sensing566of inland lakes and the application of remote sensing567and GIS technique for the dynamic monitoring of land use/land cover and its568effect on environment.

569Hongtao Duan received the Ph.D. degree in optical570remote sensing in inland waters from the Graduate571School of Chinese Academy of Sciences, Beijing,572China, in 2007.573He is currently an Associate Professor with the574State Key Laboratory of Lake Science and Envi-575ronments, Nanjing Institute of Geography and576Limnology, Chinese Academy of Sciences, Nanjing,577China. His research interest includes water color578remote sensing in inland lakes.

579Steven A. Loiselle received the degree in engineering580from Rensselaer Polytechnic Institute, NY, USA, and581the Ph.D. degree from the University of Siena, Siena,582Italy, in 1986 and 2000, respectively.583Since2002, hehasbeen aResearchProfessor at the584University of Siena. He is a Visiting Scholar at the585Nanjing Institute of Geography and Limnologywhere586he has studied the shallow lakes of theYangtzeValley,587China. He has coordinated research in the African588Great Lakes, the large lakes of the Paraná delta589(Argentina, Paraguay), and the coastal lakes of the590Mediterranean basin (Italy, France, Morocco) and is leading an urban aquatic591ecosystems project involving citizen scientists. His research interests are in the592study of local, regional and global environmental drivers and their impact on593freshwater ecosystems,using remote sensingandecologicalmodelingapproaches.

594Jinduo Xu received the M.S. degree in geographic595information systems from Nanjing Normal Univer-596sity, Nanjing, China, in 2008.597She is currently engaged in the data sharing598applications and services. Her research interests599include the development of geographic information600system and geographic information database601technology.

602Mengxiao Ma received the B.Sc. degree in environ-603mental science from Shandong Normal University,604Jinan, China, and is currently pursuing the Master605degree in environmental science from Nanjing Uni-606versity, Nanjing, China.607Her research interest includes environmental608remote sensing for lake applications.

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1 ANovel Algorithm to Estimate Algal BloomCoverage2 to Subpixel Resolution in Lake Taihu3 Yuchao Zhang, Ronghua Ma, Hongtao Duan, Steven A. Loiselle, Jinduo Xu, and Mengxiao Ma

4 Abstract—Remote sensing has often been used to monitor the5 distribution and frequency of floating algae in inland aquatic6 environments. However, due to the spatial resolution of the most7 common satellite sensors, accurate determination of algae coverage8 remains a major technical challenge. Here, a novel algorithm to9 estimate floating algae area to subpixel scales, denominated the

10 algae pixel-growing algorithm (APA), is developed and evaluated11 for a series of image data from the Moderate Resolution Imaging12 Spectroradiometer (MODIS). The algorithm utilizes the Rayleigh-13 corrected reflectance (Rrc) and a floating algae index (FAI) derived14 from Rrc in three spectral bands. Comparison with concurrent15 Landsat TM=ETMþ data indicate that the APA provides more16 accurate estimates of both algal bloom area and algal bloom17 intensity (i.e., floating algae coverage) (RSE ¼ 15:2 km2 and18 RE ¼ 9:9%), compared to other commonly used methods such as19 the linear unmixing algorithm (LA). Furthermore, this study con-20 firms thatFAI is a better indexwith respect to normalized difference21 vegetation index (NDVI) and enhanced vegetation index (EVI) for22 the estimation of algae area coverage, especially when combined23 with the APA. Finally, the study provides a theoretical basis for the24 objective assessment of bloom severity in complex inland25 waterbodies.

26 Index Terms—Algae pixel-growing algorithm (APA), algal27 blooms, floating algae index (FAI), Landsat, linear unmixing28 decomposition, Moderate Resolution Imaging Spectroradiometer29 (MODIS).

30 I. INTRODUCTION

31 I N RECENT decades, many major lakes have experienced32 an increased occurrence of algal blooms, including Lake33 Victoria in Africa, Lake Okeechobee in the United States, and34 Lake Taihu in China [1]–[3]. Compared to traditional in situ

35sampling and laboratory analysis, satellite-derived optical and36thermal data provide synoptic and high frequency information,37useful for early warning, real-time monitoring, and postbloom38evaluations of bloom events [1]. Over the last three decades,39there have been many important developments in the use of40remotely sensed data to estimate phytoplankton biomass con-41centrations and the associated bio-optical properties [4], [5].42This has been particularly important where eutrophication and43floating algal mats have compromised lake functioning.44Information regarding the coverage and thickness of algal mats45provides fundamental information necessary for biomass esti-46mates, which are more appropriate than traditional chlorophyll-a47concentration measurements for decision-making. Choosing an48appropriate algal bloom algorithm is the first step to estimate49bloom coverage.50Single-bands, band ratios, and band differences have been51used in a similar manner to land surface vegetation detection52algorithms [6]–[8]. Indices such as the normalized difference53vegetation index (NDVI), the enhanced vegetation index (EVI),54the normalized difference algae index (NDAI), maximum chlo-55rophyll index (MCI), red tide index (RI), the floating algae index56(FAI), and the ocean surface algal blooms index (OSABI) have57been utilized to identify floating algal blooms [9]–[15]. But in58practical application, NDVI, EVI, and FAI are much more59popular than the other indices. However, it has been found that60NDVI and EVI are particularly sensitive to aerosol characteristics61(type and thickness), sun glint, and solar viewing geometry with62respect to FAI, which has been found to be relative stable under63different environmental and observing conditions [13]. Mini-64mizing the atmospheric effects is vital to time-series study and65characterization on algal blooms and water optical properties in66turbid inland lakes [16], [17]. Thus, FAI appears to be a67reasonable choice to detect and quantify bloom coverage.68However, algalmats can be smaller than the satellite pixel size,69leading to uncertainties in the total bloom coverage estimates.70Indeed, algal blooms may present complex temporal and spatial71characteristics, which may limit the use of satellite data. Most72algorithms use data from the Landsat TM=ETMþ, Moderate73Resolution Imaging Spectroradiometer (MODIS), and Medium74Resolution Imaging Spectrometer (MERIS), with spatial resolu-75tions of 30 m, 250=500=1000 m, and 300=1200 m with tempo-76ral resolutions of 16 days, 1 day, and 3 days, respectively. Among77these, MODIS has the best temporal resolution, but its best78spatial resolution is 250m,whichmay not detect small patches of79floating algae and may lead to uncertainties in area coverage80estimates. The ability to detect and quantify surface features at81subpixel scales would improve the use of satellite systems for82bloom monitoring and management.

1939-1404 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Manuscript received September 02, 2013; revised January 22, 2014; acceptedMay 12, 2014. This work was supported in part by the National High TechnologyResearch and Development Program of China (2014AA06A509), in part by theNational Natural Science Foundation of China under Grant 41101316, Grant41171271, and Grant 41171273, in part by Scientific Data Sharing Platform forLake and Watershed, in part by Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences (NIGLAS), in part by the “135” Program ofNIGLAS (NIGLAS2012135010 and NIGLAS2012135014), and in part by theDragon 3 program (10561). MODIS data were provided by the U.S. NASA, andthe Landsat TM=ETMþ data were provided by the U.S. Geological Survey.(Corresponding author: Ronghua Ma.)

Y.Zhang,R.Ma,H.Duan, and J.Xu arewith the StateKeyLaboratory of LakeScience and Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing 210008, China (e-mail: rhma@niglas.ac.cn).

S. A. Loiselle is with the CSGI, University of Siena, Siena 253100, Italy(e-mail: loiselle@unisi.it).

M. Ma is with the State Key Laboratory of Pollution Control & ResourcesReuse, School of the Environment, Nanjing University, Nanjing 210023, China(e-mail: mmxlb369@163.com).

Color versions of one ormore of the figures in this paper are available online athttp://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2327076

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f83 Bloom detection and quantification at subpixel scales depend84 on the feature size (the relative percentage of the algae bloom85 within a pixel) and the sensitivity of the satellite sensor (signal to86 noise ratio). The accuracy of algae coverage estimates is strongly87 influenced by mixed pixels containing both algae and algae free88 (water) surfaces. When the total lake area coverage is small,89 mixed pixels may represent a large portion of the total algae90 pixels. Hu et al. [18] have used a linear unmixing algorithm (LA)91 to estimate the total algae area coverage in Lake Taihu, an92 eutrophic lake in East China. Shanmugam et al. [15] have also93 applied the similar algorithm to detect ocean surface algal94 blooms in subpixel scales. However, this algorithm needs to be95 further improved to allow for the determination of algae coverage96 at subpixel scales and more accurate estimation of threshold97 values for algae and nonalgae areas of optically complex eutro-98 phic lakes.99 In China, satellite-based estimations of algal blooms (cover-

100 age, distribution, and duration) are used by environmental101 management agencies at both state and local levels for a variety102 of purposes, from policy implementation and water management103 (including the physical removal of the algae mats). Coverage104 estimates are generated by different agencies using a variety of105 methods, most of which rely on human interpretation of the106 manually adjusted satellite images (to account for variable107 atmospheric and observing conditions as well as algorithm108 artifacts). The large degree of subjectivity has resulted in incom-109 patible estimates and inconsistent recommendations among110 agencies. There is a clear need for an objective method, which111 would be valid for variable conditions, including the partial algae112 coverage at subpixel scales. An objective approach would allow

113different agencies and groups to generate consistent estimates114based on the same data.115In this study, we developed a new algorithm [algae pixel-116growing algorithm (APA)] to estimate algae coverage at subpixel117scales usingMODIS data.We evaluated the accuracy of theAPA118using concurrent higher resolution (30 m) Landsat TM=ETMþ119data by comparing their spatial distributions and coverage120statistics. In addition, several practical issues in using MODIS121for bloom monitoring were examined, including an automatic122and objective determination of the pure algae threshold as well as123a method to adjust pixels contaminated by striping noise. The124ultimate goal is to provide an objective method to determine125bloom coverage and size to establish a long-term baseline record126and real-time assessment of bloom severity to support manage-127ment decisions.

128II. STUDY AREA AND DATA SOURCES

129A. Study Area

130With an extension of 36800 km2 and encompassing major131population centers such as Shanghai, Suzhou, Wuxi, and132Changzhou, the Lake Taihu basin is one of the most highly133developed areas of China (Fig. 1). At the center of the Basin is134Lake Taihu, China’s third-largest freshwater lake (2400 km2).135The lake provides drinking water for more than 2 million people,136and sustains important fisheries including crabs, carp, and eels137[2]. With the rapid economic development in the last decades,138Lake Taihu has become highly eutrophic with frequent cyano-139bacteria blooms as a consequence of increased nutrient loads

F1:1 Fig. 1. Study site was Lake Taihu, China lying in the Yangtze River Delta. The lake is often divided into several lake segments, which include Zhushan Bay (ZB),F1:2 Meiliang Bay (MB), Gong Bay (GB), West Lake (WL), South Lake (SL), Central Lake (CL), East Lake, and East Taihu. The brown areas represent major cities.

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140 [19]–[21]. In 2007, a massive algal bloom led to a drinking water141 shortage for more than 1 million people [2], [22]. Although the142 Chinese government recognized the importance of managing143 nutrient loads and monitoring lake conditions, the long-term144 management of the Lake Taihu Basin is still in its infancy.145 Since 2007, remote sensing has been used to monitor the146 occurrence of algal blooms as part of an early warning system147 with daily communications to national and local decision148 makers.

149 B. Satellite Data

150 TerraMODIS data at 250 and 500m resolutions and Landsat151 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic152 Mapper Plus (ETMþ) data at 30 m resolution were used in this153 study. Landsat TM=ETMþ Level-1 data were obtained from154 the Earth Resources Observation and Science Center (http://155 glovis.usgs.gov).MODIS data were obtained from the LAADS156 website of the U.S. NASA Goddard Space Flight Center.157 Twenty-four pairs of concurrent (i.e., same day within a hour)158 MODIS and TM=ETMþ data (Table I) with low cloud cover-159 age ( <10%) were georeferenced (UTM projection) with an160 error of <0:5 pixel. The 500-m resolution MODIS data at161 1240 nm and 1640 nm were resampled to 250 m resolution (to162 match the 645-nm data). TM=ETMþ data were atmospheri-163 cally corrected using the radiative transfer calculations based164 on the second simulation of the satellite signal in the solar165 spectrum [23]. MODIS data were corrected by removing the166 molecular (Rayleigh) scattering effects, and then converted to167 Rayleigh-corrected reflectance (Rrc) following Hu et al. [24].168 The lake segment of East Lake Taihu was excluded from the169 bloom analysis as this area is characterized by aquatic macro-170 phytes growing from the bottom [25].

171 III. METHODOLOGY

172 A. Floating Algal Index Algorithm

173 The FAI algorithm for MODIS proposed by Hu [13] utilizes a174 baseline subtraction, defined as the difference between reflec-175 tance at 859 nm (vegetation “red edge”) and a linear baseline

176between the red band (645 nm) and short-wave infrared band177(1240 or 1640 nm)

FAIMODIS ¼ Rrcð859Þ � R0rcð859Þ (1)

R0rcð859Þ ¼ Rrcð645Þ þ Rrcð1240Þ � Rrcð645Þ½ �

� ð859� 645Þ=ð1240� 645Þ178where Rrc is defined as the difference between the calibrated179sensor radiance [after adjustment for ozone (Lt

�) and other180gaseous absorption] and Rayleigh reflectance (Rr) [24]

Rrc ¼ �Lt�=F0 cos �0 � Rr (2)

181whereF0 is the extraterrestrial solar irradiance at data acquisition182time and �0 is the solar zenith angle.183FAI has been used to map bloom size to derive long-term184statistics [18]. However, the estimation of bloom size is subject to185large errors if a single threshold is used to determine the bloom186presence or absence. Hu et al. [18] used an LA to estimate partial187coverage at subpixel scales (�pixel) through a statistical analysis188of the 9-year MODIS FAI dataset with FAI thresholds for189100% bloom coverage (FAIalgae) and 0% bloom coverage190(FAInonalgae). When FAInonalgae < FAIpixel � FAIalgae, LA191is expressed as

�pixel ¼ ðFAIpixel � FAInonalgaeÞ=ðFAIalgae � FAInonalgaeÞ:(3)

192B. Algae Pixel-Growing Algorithm (APA)

1931) Theoretical Considerations:Consideringn high-resolution194pixels (Rrc

Hi) that make up a low-resolution pixel (RrcLo), where

195RrcLo is the arithmetic mean of all Rrc

Hi

RrcLo ¼ Rrc

HiðiÞ: (4)

196Similarly, there exists a relationship between the low-197resolution pixel and high-resolution pixels (or subpixels198within the low-resolution pixel) for FAI

FAIpixel ¼ FAIsubpixel: (5)

199If we assume that, in a 3� 3 pixel window, the central pixel is200a function themaximum andminimumFAI values present within201the window (Fig. 2), (5) becomes

FAIcenter ¼ � � FAImax þ 1� �ð ÞFAImin (6)

TABLE IT1:1 ACQUISITION DATE OF TERRA/MODIS AND LANDSAT TM (MARKEDWITH ASTERISK)/T1:2 ETMþ DATA USED IN THIS STUDY

F2:1Fig. 2. In the APA, a 3� 3windowwas chosen randomly from aMODIS image.F2:2Gray-scale shows the FAI value of each pixel. The central pixel was divided intoF2:3two kinds of subpixels which have the same maximum and minimum FAI valuesF2:4of the other pixels present in the 3� 3 window.

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f202 where � is the decomposition parameter of the 3� 3window that203 is determined based on the relationship between the known FAI204 values (center, max, and min).205 In a mixed pixel, the algae coverage is defined as the propor-206 tion (�) of the pixel covered by floating algae such that

FAI ¼ � � FAIthreshalgae þ ð1� �Þ � FAIthreshwater

¼ FAIthreshalgae � FAIthreshwater

� �� �

þ FAIthreshwater (7)

207 where FAIthreshalgae and FAIthreshwater are the FAI thresholds for pure208 algae (100%) and nonalgae water (0%) pixels, respectively.209 Assuming thatFAIthreshalgae andFAIthreshwater are constant in a 3� 3210 window, the FAI of central pixel could be expressed as follows:

FAIcenter ¼ FAIthreshalgae � FAIthreshwater

� �� �center

þ FAIIthreshwater : (8)

211 The FAI of max and min pixels in a 3� 3 window could also212 be similarly expressed in the same way. Based on (6)–(8), FAI213 has a linear relationship with the floating algae coverage � in the214 mixed pixel

�center ¼ � � �max þ 1� �ð Þ � �min (9)

215 where �max and �min are the algae coverage of pixels with216 maximum and minimum FAI values in a 3� 3 window,217 respectively.

2182) APA Implementation and Data Processing: There are three219iterative steps in applying the APA to MODIS Rrc data (Fig. 3).2201) The preprocessing step identifies the location of the maximum221and minimum FAI value in a roaming 3� 3 window, and then222calculates � for the central pixel using (6). 2) The “seed” pixels223are then identified in twoways: a) if local pixels are found to have224their FAI values > FAIthreshalgae , these pixels are taken as the

225seed pixels and their algae coverage are defined as 1.0 (100%);226b) if no local pixels have FAI values > FAIthreshalgae , then local

227center pixels with maximum FAI values in the 3� 3window are228taken as the seed pixels. The coverage is determined via (3) using229a pure-algae endmember threshold of FAIthreshalgae . The coverage

230of other pixels is assigned 0.0 in the initial estimation. 3) Thefinal231algae coverage at each pixel is determined iteratively using (9)232whenever the maximum coverage in each 3� 3 window is not233zero. The iteration is terminated when two consecutive calcu-234lations yield similar results. In practice, three iterations were235necessary before the termination (see below). Through iterations,236the algal bloom coverage expands from the initial pure algae

F3:1 Fig. 3. Flowchart of the APA process.

F4:1Fig. 4. Changes in the MODIS-derived algae coverage area with increasingF4:2iterations (steps) of the APA. The stars mark the true algal bloom area identifiedF4:3from the corresponding Landsat TM=ETMþ data.

F5:1Fig. 5. Estimation of floating algae coverage using the APA (the aquaticF5:2macrophytes area marked with gray color), from the initial estimation (step 1),F5:3the determination of the growing points (step 2), and the final estimation ofF5:4algal coverage after 3 iterations (step 3). A and B are the ends of a transect withinF5:5the lake.

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237 pixels or high-coverage pixels to low-coverage pixels, according238 to the relationship between adjacent pixels in a 3� 3 pixel239 window. For very thin algae layers, the measured reflectance240 may always be confused with surface waters no algal bloom241 but with high concentrations of chlorophyll-a. However, by242 including the constraints related to adjacent pixels with high-243 algae coverage, this error is reduced unless these conditions244 are present over a large geographic area. If such conditions are245 not present, the problematic pixel is assigned as nonalgae246 pixel. Total algae coverage is determined by the no zero algae247 coverage.248 3) FAI Threshold for Pure Algae Coverage: The APA results249 are sensitive to the choice of theFAIthreshalgae and to the termination

250 conditions of the iterative calculation. The FAIthreshalgae was

251 determined from concurrent higher resolution TM=ETMþ252 observations, but could also be estimated using in situ253 reflectance spectra of the pure algae. Due to the lack of in situ254 reflectance data covering the spectral range of > 1000 nm, the255 former method was used. With a higher spatial resolution,256 Landsat TM=ETMþ data provide a better resolution of the257 pure algae pixels. We assumed that there were only pure algae258 pixels and pure nonalgae pixels existing in LandsatTM=ETMþ259 images. A pixel’s gradient was defined as the FAI difference260 from the adjacent pixels in a 3� 3window. It was found that the261 pixels associated with the maximum gradient determined mode262 could be used to separate floating algae from other nonbloom263 waters very well [18]. Using the large gradient in FAI values264 across the algae and nonalgae boundary, the mean value of all265 pixels associated with the gradient was used to represent the266 TM=ETMþ threshold value for pure algae. After resizing from267 30 to 250 m (to match the MODIS resolution), the partial algae268 coverage in the new low-resolution pixel could be determined.269 If all TM=ETMþ pixels within the low-resolution pixel had270 100% algae coverage, the low-resolution pixel could be iden-271 tified as the pure algae pixel. From all these low-resolution pure272 algae pixels, the FAI value (�2� standard deviations) was273 used to represent the threshold value for pure (100%) algae274 coverage (FAIthreshalgae ).

275 4) The Condition to Terminate Iterations: For each MODIS276 pixel, the algae coverage is a function of the FAI maximum and277 minimum in each 3� 3 window. For each iteration, the278 estimated coverage increases until it reaches a stable value279 (Fig. 4). The termination condition allows the calculation to

280conclude when the absolute difference or relative difference of281consecutive steps reaches a minimum value. For variable bloom282areas (from several hundreds to >1000 km2) with millions of283algae pixels, the absolute difference or relative difference284inconsecutive steps can be significantly different between285images. Importantly, it was found that the smallest relative286difference between MODIS and TM=ETMþ data occurred287after three iterations in all 24 concurrent MODIS/TM image288pairs (Fig. 4). This is a natural consequence of the fact that mixed289pixels are usually found adjacent to pure-algae pixels or high290algae-coverage pixels. The APA approach builds algae coverage291from pure-algae pixels with respect to nonalgae pixels (or from292high-coverage pixels to low-coverage pixels).293An example of these calculation steps is presented for a lake294transect characterized by pure algae to nonalgae water in Lake295Taihu on September 24, 2011 (Fig. 5). The algae coverage of296some pixels varied at every iteration. In general, the algae297coverage of mixed pixels increased gradually, as the algae298coverage of the FAI maximum pixel or FAI minimum pixel299changed in each 3� 3 pixel. From the algae coverage distribu-300tion of each iteration, the algal bloom coverage expanded from301the pure algae pixels or high-coverage pixels to low-coverage302pixels, based on the condition of adjacent pixels in a 3� 3 pixel303window. In total, the algae coverage will be ignored if there is no304algal bloom existing in adjacent pixels.

F6:1 Fig. 6. Comparison of algal bloom coverage derived from several different methods ( coverage interval ¼ 0:005): the full line marks the true bloom coverage usingF6:2 30 m TM=ETMþ data resized to 250 m; the dashed lines show the estimate of bloom coverage from the concurrent MODIS image using the APA; the dotted linesF6:3 represent the estimate of bloom coverage from the same MODIS image using an LA.

F7:1Fig. 7. North south profile of estimated algae coverage (0%–100%) from resizedF7:2ETMþ pixels (all at a 250-m resolution) andMODIS using the APA and an LA,F7:3along a hypothetical transect fromMeiliang Bay to Tiaoxi River (white line in theF7:4inset image). Each point represents an arithmetic mean of 3� 3 pixels along theF7:5transect.

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f305 IV. RESULTS AND VALIDATION

306 A. Algal Bloom Coverage Estimations

307 Algal bloom coverage identified from the 24 TM=ETMþ308 images ranged from 14.8 to 505:7 km2. The relative standard309 error (RSE) between MODIS estimates and TM=ETMþ esti-310 mates was 15.2 and 24:8 km2, respectively, for the APA and LA311 methods, with their corresponding relative error (RE) of 9.9%312 and 17.3%. The results of APA and LA presented here should be313 interpreted as MODIS pixels with both 100% and partial algae314 bloom coverage.315 If we ignored the partial algae bloom coverage and took the316 mean FAI value of pixel with 10% algae coverage as the317 threshold of algae pixel and nonalgae pixel, algae coverage area318 fromMODIS images is increased by >40%, and its correspond-319 ing RE betweenMODIS estimates andTM=ETMþ estimates is320 more than 30%. Due to the partial coverage provided by APA321 and LA, their errors are much smaller (as gauged by the high-322 resolution TM=ETMþ data). APA provided more accurate esti-323 mates than the LA, with the already small errors nearly halved.324 A comparison of MODIS algal bloom coverage with paired325 TM=ETMþ coverage, resized from 30 to 250 m, gave similar326 results (Fig. 6). When the pure algae pixels of MODIS were set327 with resized TM=ETMþ, LA over-estimated the number of328 pixels with partial algae coverage. The over-estimation indicated329 that the nonalgae threshold was low. The APA determination

330provided a similar frequency distribution for MODIS data with331resized TM=ETMþ images.332In order to examine the algae coverage distributions derived333from different methods, an example of the MODIS results on334September 24, 2011 is shown in Fig. 7. An artificial transect from335Meiliang Bay (North Lake) to the mouth of Tiaoxi River (South336Lake) was chosen to show a range of algae coverage. In order to337minimize the impact of image noise and geometric correction338error, the average coverage of the 3� 3 window was used to339represent the central pixel along the transect. Both APA and LA340yielded reasonable results when compared to ETMþ. However,341bothAPA and LAunderestimated algae coveragewhen the algae342coverage was >40% in the mixed pixels, and overestimated343coverage when the percent coverage was very low. On average,344the difference betweenMODIS andETMþwas 13.7%when the345APA was used and 15.4% when the LA was used.

346B. Sensitivity Analysis

347In this study, the MODIS threshold of pure algae was deter-348mined from concurrent TM=ETMþ observations. To examine349how sensitive the MODIS results were to the threshold estima-350tion, three MODIS images in different years (April 6, 2007;351October 20, 2009; May 5, 2012) with different bloom coverage352were compared with TM=ETMþ (Fig. 8). The bloom area was353found to increase sharply when the threshold for pure algae354coverage was lower than the mean minus the standard deviation.355The results confirm that both the APA and LA significantly356overestimated the algae coverage if the pure algae threshold was357low. A higher threshold (mean plus standard deviation) resulted358in a slight under estimation, which was less sensitive to further359changes of the threshold.360An artificial sensitivity test was also conducted by varying the361pure algae threshold by �10% to þ10%. The total algae area362from the APA estimates changed þ15:1% and �13:5% with363respect to TM=ETMþ values, whereas the area from the LA364estimates changedþ9:7% and�8:3%. In general, the change in365the total algae area estimates was proportional to changes in the366threshold values.

367V. DISCUSSIONS

368A. Lake Specific FAI Thresholds

369There are two options to determine FAI thresholds for long-370term applications: 1) threshold based on multi-image statistics

F8:1 Fig. 8. Comparison of the floating algae area estimated fromTM=ETMþ andMODIS using the two algorithms [the APA and an (LA)] when different FAI thresholdF8:2 values are used to represent pure (100%) algae coverage. u and δ represent the mean and standard deviation of the threshold values.

F9:1 Fig. 9. Linear relationship between MODIS Rrcð1240Þ and Rrcð1650Þ fromF9:2 several MODIS images.

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f371 [18]; and 2) threshold based on the individual images. Because372 environmental factors such as air temperature, wind speed and373 direction, and hydrological conditions all influence algal aggre-374 gation characteristics (i.e., algal bloom thickness) [26], the pure375 algae threshold (as well as the pure water threshold) may vary376 between images. Therefore, an image-specific threshold is more377 accurate. In this study, this was achieved by comparing concur-378 rentMODIS andTM=ETMþ image pairs. However, for routine379 monitoring, TM=ETMþ data are not available at the MODIS380 temporal frequency and the pure algae threshold needs to be381 determined in alternative ways. Because the red-edge effect382 would lead to elevated reflectance in the near-IR and shortwave383 IR, the pure algae pixel was determined when Rrc (1240)384 was greater than Rrc (645) and FAI was great than 0. Com-385 parison with the thresholds determined from the MODIS and386 TM=ETMþ pairs showed consistency, allowing for operational387 use of APA with MODIS data in absence of TM=ETMþ.

388 B. MODIS Striping Noise

389 One of theMODIS bands used to derive the FAI, the 1240-nm390 band on MODIS Terra, suffers from significant striping noise.391 This would seriously affect the FAI value andAPA performance.392 In this study, the noise-contaminated data were replaced with393 coincident the 1640-nm data, using the linear relationship be-394 tween the two bands derived from the high-quality (undisturbed)395 pixels of the same image. A significant linear relationship396 between Rrcð1240Þ and Rrcð1640Þ, especially in algae-covered

397areas was evident for all the images examined (Fig. 9). Although398the relationship varies among images, an image-specific rela-399tionship to derive Rrcð1240Þ for noise-contaminated pixels can400be used in FAI calculations. The results in indicate that such a401correction yields different statistics in algae area coverage402(Fig. 10).

403C. Comparison of FAI, NDVI, and EVI

404FAI has been used to calculate algae coverage because of its405reduced sensitivity to variable environmental and observational406conditions [18]. However, other indexes have also been widely407utilized. Among these are NDVI [27] and EVI [28], defined as

NDV I ¼ RNIR � RREDð Þ= RNIR þRREDð Þ (10)

EV I ¼ G � RNIR � RREDð Þ=RNIR þ C1 � RRED � C2 � RBLUE þ C3ð Þ (11)

408where RNIR, RRED, and RBLUE are the reflectance in the near-409infrared (NIR), red and blue bands; G is the gain factor, and C1,410C2, and C3 are the pixel-independent coefficients to compensate411for aerosol effects and vegetation background. For MODIS data,412G ¼ 2:5,C1 ¼ 6,C2 ¼ 7:5, andC3 ¼ 1. Given these alternative413choices on the vegetation indexes, we examined if FAI is the best414choice to derive the algae coverage with the APA.415A MODIS Terra image for August 10, 2013 was used to416evaluate algae coverage using FAI, NDVI, and EVI, where sun417glint influences part of the image (Fig. 11). The FAI image

F10:1 Fig. 10. Histogram distributions of Rrcð1240Þ before and after removal of the Rrcð1240Þ striping noise.

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f418 displayed a near homogeneous water background regardless of419 the sun glint. In contrast, the NDVI image revealed higher420 variability in the water background in areas where sun glint was421 significant. Sun glint led to a significant increase in the NDVI422 values, making it more difficult to differentiate algae pixels from423 the water background. The performance of EVI was much better424 than NDVI, but still showed a higher sensitivity to sun glint425 contamination with respect to FAI. The algae areas determined426 from the three images, all using the APA, were 178.04, 353.68,427 and 236:26 km2, respectively. Thus, when sun glint was signifi-428 cant, bothNDVI and EVI overestimated algae coverage. Because429 of the low-latitude (�31N) location of Lake Taihu, most images430 during the summer months contain sun glint. Thus, FAI is a431 preferred index for its tolerance to this as well as to the interfer-432 ence of thick aerosols (not shown here) for long-term, routine433 monitoring of the bloom coverage.

434 D. MODIS-Based Data Record and Event-Driven Response

435 The APA approach, together with the statistically determined436 FAI thresholds, provides an objective method to estimate the437 bloom severity. This can be used to estimate bloom spatial extent438 and effective algae coverage as well as to compare current bloom439 conditions against historical baselines determined using the same440 objective method. This will lead to more accurate estimates of441 the bloom severity in Lake Taihu in near real-time aswell as pave442 the way to obtain consistent answers from various research443 groups and management agencies.

444VI. CONCLUSION

445Remote sensing has beenwidely used to assess algal blooms in446Lake Taihu, but the methods and parameterization varied sub-447stantially between different users, making it difficult to compare448results and agree on a common action. The present approach to449develop and validate a more objective method to characterize450the bloom severity provides a novel to meet this challenge. The451results, based on the MODIS FAI data, showed improved452performance over other methods or indices. The APA approach453serves as an objective and more accurate method to determine454bloom severity in both near real-time monitoring and historical455analysis, thereby improving the capacity of decision makers to456manage Lake Taihu and its basin.

457ACKNOWLEDGMENT

458The constructive comments from two anonymous reviewers459are greatly appreciated.

460REFERENCES

461[1] A. Reinart and T. Kutser, “Comparison of different satellite sensors in462detecting cyanobacterial bloom events in the Baltic Sea,” Remote Sens.463Environ., vol. 102, no. 1–2, pp. 74–85, 2006.464[2] L. Guo, “Ecology-doing battle with the green monster of Taihu Lake,”465Science, vol. 317, no. 5842, p. 1166, 2007.466[3] S. Q. Zhao et al., “The 7-decade degradation of a large freshwater lake in467central Yangtze River, China,” Environ. Sci. Technol., vol. 39, no. 2,468pp. 431–436, 2005.

F11:1 Fig. 11. Comparison among MODIS FAI, NDVI, and EVI values for MODIS data obtained on August 10, 2013 and corresponding algae coverage distributionsF11:2 (derived from the APA). Areas with aquatic macrophytes are marked in gray.

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f

469 [4] S. Sathyendranath, “Remote sensing of ocean colour in coastal, and other470 optically-complex waters,” International Ocean-Colour Coordinating471 Group, pp. 47–76, 2000.472 [5] Z. P. Lee, “Remote sensing of inherent optical properties: Fundamentals,473 tests of algorithms, and applications,” International Ocean-Colour474 Coordinating Group, pp. 43–93, 2006.475 [6] P. S. Richard and C. T. Michelle, “Remote sensing of harmful algal476 blooms,” Remote Sens. Coastal Aquat. Environ., vol. 7, pp. 277–296, 2005.477 [7] H. T.Duan, S.X.Mang, andY. Z.Mang, “Cyanobacteria bloommonitoring478 with remote sensing in Lake Taihu (in Chinese with English abstract),”479 J. Lake Sci., vol. 20, no. 2, pp. 145–152, 2008.480 [8] R. H. Ma et al., “Spatio-temporal distribution of cyanobacterial blooms481 based on satellite imageries in Lake Taihu, China (in Chinese with English482 abstract),” J. Lake Sci., vol. 20, no. 6, pp. 687–694, 2008.483 [9] J. F. R. Gower, “Red tide monitoring using AVHRR HRPT imagery484 from a local receiver,” Remote Sens. Environ., vol. 48, no. 3,485 pp. 309–318, 1994.486 [10] J.Gower, S.King,G.Borstad, andL.Brown, “Detection of intense plankton487 blooms using the 709 nm band of theMERIS imaging spectrometer,” Int. J.488 Remote Sens., vol. 26, no. 9, pp. 2005–2012, 2005.489 [11] Y. H. Ahn and P. Shanmugam, “Detecting the red tide algal blooms from490 satellite ocean color observations in optically complex Northeast-Asia491 Coastal waters,” Remote Sens. Environ., vol. 103, no. 4, pp. 419–437, 2006.492 [12] C. M. Hu and M. X. He, “Origin and offshore extent of floating algae in493 olympic sailing area,” EOS, Trans. Amer. Geophys. Union, vol. 89, no. 33,494 pp. 302–303, 2008.495 [13] C. M. Hu, “A novel ocean color index to detect floating algae in the global496 oceans,” Remote Sens. Environ., vol. 113, no. 10, pp. 2118–2129, 2009.497 [14] W. Shi andM.Wang, “Green macroalgae blooms in the Yellow Sea during498 the spring and summer of 2008,” J. Geophys. Res., vol. 114, no. C12,499 pp. 1–10, 2009.500 [15] P. Shanmugam, M. Suresh, and B. Sundarabalan, “OSABT: An innovative501 algorithm to detect and characterize ocean surface algal blooms,” IEEE J.502 Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 4, pp. 1879–1892,503 Aug. 2013.504 [16] J. Chen, W. T. Quan, M. W. Zhang, and T. W. Cui, “A simple atmospheric505 correction algorithm for MODIS in shallow turbid waters: A case study in506 Taihu Lake,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6,507 no. 4, pp. 1825–1833, Aug. 2013.508 [17] M. Wang, S. Son, Y. Zhang, and W. Shi, “Remote sensing of water optical509 property for China’s inland Lake Taihu using the SWIR atmospheric510 correction with 1640 and 2130 nm bands,” IEEE J. Sel. Topics Appl. Earth511 Observ. Remote Sens., vol. 6, no. 6, pp. 2505–2516, Dec. 2013.512 [18] C. M. Hu et al., “Moderate Resolution Imaging Spectroradiometer513 (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,”514 J. Geophys. Res.-Oceans, vol. 115, C04002, 2010, doi: 10.1029/515 2009JC005511.516 [19] Q. M. Cai, X. Y. Gao, Y. W. Chen, S. W. Ma, and M. Dokulil, “Dynamic517 variations of water quality in Lake Taihu and multivariate analysis of its518 influential factors,” Chin. Geogr. Sci., vol. 6, no. 4, pp. 364–374, 1996.519 [20] Y. W. Chen, B. Q. Qin, K. Teubner, and M. T. Dokulil, “Long-term520 dynamics of phytoplankton assemblages: Microcystis-domination in Lake521 Taihu, a large shallow lake in China,” J. Plankton Res., vol. 25, no. 4,522 pp. 445–453, 2003.523 [21] J. Chen and W. T. Quan, “Using Landsat/TM imagery to estimate nitrogen524 and phosphorus concentration in Taihu Lake, China,” IEEE J. Sel. Topics525 Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 273–280, Feb. 2012.526 [22] H. T. Duan et al., “Two-decade reconstruction of algal blooms in China’s527 Lake Taihu,” Environ. Sci. Technol., vol. 43, no. 10, pp. 3522–3528, 2009.528 [23] E. F. Vermote et al., “Atmospheric correction of visible to middle-infrared529 EOS-MODIS data over land surfaces: Background, operational algorithm530 andvalidation,” J.Geophys. Res.-Atmos., vol. 102, no.D14, pp. 17131–17141,531 1997.532 [24] C. Hu et al., “Assessment of estuarine water-quality indicators using533 MODIS medium-resolution bands: Initial results from Tampa Bay,534 Florida,” Remote Sens. Environ., vol. 93, no. 3, pp. 423–441, 2004.535 [25] R. Ma, G. Jiang, H. Duan, L. Bracchini, and S. A. Loiselle, “Effective536 upwelling irradiance depths in turbid waters: A spectral analysis of origins537 and fate,” Opt. Express, vol. 19, no. 8, pp. 7127–7138, 2011.538 [26] F. X. Kong and G. Gao, “Hypothesis on cyanobacteria bloom-forming539 mechanism in large shallow eutrophic lakes,” Acta Ecol. Sin., vol. 25, no. 3,540 pp. 589–595, 2005.541 [27] J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring542 vegetation systems in the Great Plains with ERTS,” in Proc. 3rd ERTS543 Symp. NASA SP-3511, 1973, pp. 309–317.544 [28] A. R. Huete and C. Justice,MODIS Vegetation Index (MOD13) Algorithm545 Theoretical Basis Document Version 3, 1999.

546Yuchao Zhang received the Ph.D. degree in environ-547mental science from Nanjing University, Nanjing,548China, in 2008.549She has been a Researcher in Nanjing University550for 12 years. Since 2012, she has been an Associate551Professor with Nanjing Institute of Geography and552Limnology (NIGLAS), ChineseAcademy of Sciences553(CAS), Nanjing, China. Her research interests include554remotely sensedmonitoring of algal blooms andwater555color remote sensing in inland lakes.

556Ronghua Ma received the M.S. degree in surveying557and mapping from Xi’an University of Science and558Technology, Xi’an, China, in 1999, and the Ph.D.559degree in cartography and GIS from Nanjing Univer-560sity, Nanjing, China, in 2002.561He has been with Nanjing Institute of Geography562and Limnology (NIGLAS), Chinese Academy of563Sciences (CAS) since 2002, where he is currently a564Professor of Lake Remote Sensing. His research565interests include water environment remote sensing566of inland lakes and the application of remote sensing567and GIS technique for the dynamic monitoring of land use/land cover and its568effect on environment.

569Hongtao Duan received the Ph.D. degree in optical570remote sensing in inland waters from the Graduate571School of Chinese Academy of Sciences, Beijing,572China, in 2007.573He is currently an Associate Professor with the574State Key Laboratory of Lake Science and Envi-575ronments, Nanjing Institute of Geography and576Limnology, Chinese Academy of Sciences, Nanjing,577China. His research interest includes water color578remote sensing in inland lakes.

579Steven A. Loiselle received the degree in engineering580from Rensselaer Polytechnic Institute, NY, USA, and581the Ph.D. degree from the University of Siena, Siena,582Italy, in 1986 and 2000, respectively.583Since2002, hehasbeen aResearchProfessor at the584University of Siena. He is a Visiting Scholar at the585Nanjing Institute of Geography and Limnologywhere586he has studied the shallow lakes of theYangtzeValley,587China. He has coordinated research in the African588Great Lakes, the large lakes of the Paraná delta589(Argentina, Paraguay), and the coastal lakes of the590Mediterranean basin (Italy, France, Morocco) and is leading an urban aquatic591ecosystems project involving citizen scientists. His research interests are in the592study of local, regional and global environmental drivers and their impact on593freshwater ecosystems,using remote sensingandecologicalmodelingapproaches.

594Jinduo Xu received the M.S. degree in geographic595information systems from Nanjing Normal Univer-596sity, Nanjing, China, in 2008.597She is currently engaged in the data sharing598applications and services. Her research interests599include the development of geographic information600system and geographic information database601technology.

602Mengxiao Ma received the B.Sc. degree in environ-603mental science from Shandong Normal University,604Jinan, China, and is currently pursuing the Master605degree in environmental science from Nanjing Uni-606versity, Nanjing, China.607Her research interest includes environmental608remote sensing for lake applications.

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