Remote sensing of diffuse attenuation coef ?cient of photosynthetically active radiation in Lake Taihu using MERIS data
Kun Shi a ,Yunlin Zhang a ,?,Xiaohan Liu a ,b ,Mingzhu Wang a ,b ,Boqiang Qin a
a Taihu Lake Laboratory Ecosystem Station,State Key Laboratory of Lake Science and Environment,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China b
University of Chinese Academy of Sciences,Beijing 100049,China
a b s t r a c t
a r t i c l e i n f o Article history:
Received 8July 2013
Received in revised form 10September 2013Accepted 10September 2013Available online xxxx
Keywords:
Diffuse attenuation coef ?cient Lake Taihu
Medium Resolution Imaging Spectrometer Photosynthetically available radiation
Especially for a shallow and extremely turbid lake,light availability in the water column can determine the euphotic zone,and the horizontal and vertical distribution of algae species.Light attenuation is traditionally quanti ?ed as the diffuse attenuation coef ?cient of the photosynthetically available radiation (K d (PAR)).Global coverage of K d (PAR)at high spatial and temporal resolution can be provided by satellite measurements,and these data can be used to improve our understanding of the physical,chemical and biological processes in Lake Taihu,a large shallow lake in China,of considerable importance as a source of drinking water for several large cities.
The primary dominated contributor to K d (PAR)was determined ?rstly using linear regression.There was a sig-ni ?cant positive correlation between K d (PAR)and concentrations of total suspended matter (TSM).The determi-nation coef ?cient between K d (PAR)and TSM (R 2=0.91)was signi ?cantly higher than that between K d (PAR)and chlorophyll-a concentrations (Chl-a )(R 2=0.11),and between K d (PAR)and absorption of chromophoric dissolved organic matter (CDOM)(R 2=0.01).Our results therefore could demonstrate that TSM usually plays a dominant role in the attenuation of light in Lake Taihu.
A retrieval model of K d (PAR)values for Lake Taihu was subsequently developed using top-of-atmosphere (TOA)radiance of Medium Resolution Imaging Spectrometer (MERIS)image data at band 10,which was strongly cor-related with in situ K d (PAR)(R 2=0.74,p b 0.005,N =48).In contrast,the atmospheric corrected re ?ectance of MERIS image data was not strongly correlated with in situ K d (PAR).Thus,atmospheric correction was not a pre-requisite for estimations of K d (PAR)in this highly turbid and hyper-eutrophic lake,and a simple empirical model of the K d (PAR)estimation for Lake Taihu was effective.
With the simple model,the seasonal and spatial distributions of K d (PAR)in Lake Taihu were studied using the MERIS measurements from 2003to 2010.Our results show distinct seasonal,spatial,and wind driven,K d (PAR)values in Lake Taihu.The highest and the lowest K d (PAR)values were found in summer and in winter,respectively.The increase of K d (PAR)in summer can be attributed to the phytoplankton blooms,and wind-driven sediment resuspension.Spatially,the K d (PAR)values were high in the southern part and the lake center,and low K d (PAR)in East Lake Taihu.Based on the MERIS derived K d (PAR)values,a signi ?cant correlation was found between K d (PAR)and wind speeds,suggesting a critical role of wind speeds in the K d (PAR)variations in Lake Taihu.
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1.Introduction
The light available in the water column at wavelengths in the visible part of the spectrum,(400–700nm),is the photosynthetically active ra-diation (PAR)(Kirk,2011),used by phytoplankton and hydrophytes.The availability of PAR therefore constrains the type and distribution of algae species and hydrophytes,which contribute greatly to total pri-mary production in waters (Saulquin et al.,2013).
The light penetration and availability in aquatic systems can be expressed by the diffuse attenuation coef ?cient of PAR (K d (PAR)),which is de ?ned in terms of the exponential decrease of the ambient
irradiance with depth (Wang,Son,&Harding,2009;Zhang,Liu,Yin,Wang,&Qin,2012).Generally,K d (PAR)is determined by pure water and other three optically active components:(i)phytoplankton pigments (expressed here as the concentration of Chl-a ),(ii)absorption of CDOM,and (iii)the concentration of TSM.
Understanding K d (PAR)plays a critical role in limnology,and both PAR and K d (PAR)have received attention from biologists (Saulquin et al.,2013).Accurate estimation of K d (PAR)in the water column is crit-ical for understanding physical processes such as sediment resuspension and the heat transfer in the upper layer of a lake,and biological process-es such as phytoplankton photosynthesis in the euphotic zone (Wang et al.,2009;Wu,Tang,Sathyendranath,&Platt,2007;Zhang et al.,2012).For examples,K d (PAR)allows prediction of the light availability to aquatic organisms,including phytoplankton and hydrophytes,at
Remote Sensing of Environment 140(2014)365–377
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Zhang).0034-4257/$–see front matter ?2013Elsevier Inc.All rights reserved.
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j o u r n a l h om e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /r s e
various depths(Zhao et al.,2013).Additionally,knowledge of the spatial and temporal distribution of K d(PAR)is key for estimating primary pro-ductivity of a lake(Zhang,Qin,&Liu,2007),and in aiding restoration of an underwater light climate that meets the optical conditions for sub-merged aquatic vegetation(SAV)(Zhang,Zhang,Ma,Feng,&Le,2007).
The in situ spectral K d(PAR)was traditionally measured by the ocean-color scienti?c community at490nm and the following primary study was investigated in1970s(Jerlov,1976).Since the launch of the Coastal Zone Color Scanner(CZCS)in1973,satellite remote sensing image data has been used by the ocean color community to map the distribution of the diffuse attenuation coef?cient at large spatial scales,with better spatial and temporal resolution than was obtained from in situ data. From water color satellite sensors,such as the Sea-viewing Wide Field-of-view Sensor(SeaWiFS)(McClain,Feldman,&Hooker,2004; Mueller,2000),the Moderate Resolution Imaging Spectroradiometer (MODIS)(Shi&Wang,2010;Wang et al.,2009;Zhao et al.,2013), and the Medium Resolution Imaging Spectrometer(MERIS)(Kratzer, Brockmann,&Moore,2008;Saulquin et al.,2013),three main types of models are used to derive the diffuse attenuation coef?cient maps: (i)empirical relationships between the diffuse attenuation coef?cient and the normalized water-leaving radiance(or remote sensing re?ec-tance)of a single wavelength or different band ratios(such as the blue-green and blue-red)(Doron,Babin,Mangin,&Hembise,2007; Kratzer et al.,2008;Mueller,2000;Wang et al.,2009;Zhang&Fell, 2007;Zhang et al.,2012),(ii)empirical relationships between diffuse attenuation coef?cient and Chl-a,through regression analyses(Morel et al.,2007)and(iii)semi-empirical approaches based on radiative transfer models(Lee,Du,&Arnone,2005;Lee,Darecki,et al.,2005; Wang et al.,2009).
These three main types of models are generally applicable to clear, open ocean waters or slightly turbid coastal waters.The standard algo-rithm(model type i)for deriving the diffuse attenuation coef?cient from SeaWiFS data is not suitable for optically complex inland waters (Son,Campbell,Dowell,Yoo,&Noh,2005).Model type ii model is routinely used for the clear open ocean waters where the diffuse atten-uation coef?cient is dominated by phytoplankton.However,this ap-proach cannot be applicable to coastal or inland waters,because determination of the diffuse attenuation coef?cient is complicated by increased light attenuation of CDOM and TSM,Although the limitations of the empirical algorithms of model types i and ii have been addressed by the semi-empirical approach(model type iii),the uncertainty of em-pirical algorithms persists in deriving the diffuse attenuation coef?cient for turbid waters,especially for extremely turbid lake waters with abun-dant TSM and CDOM(Wang et al.,2009).Several studies have reported that the in situ measured diffuse attenuation coef?cients in extremely turbid,shallow lakes are approximately1–2orders of magnitude higher than those in clear,open sea waters or in slightly turbid coastal waters (Lee,Darecki,et al.,2005;Wang et al.,2009;Zhang,Zhang,et al., 2007;Zhang et al.,2012).The large difference in the diffuse attenuation coef?cients between the different types of waters strongly indicates that we should develop a new algorithm to estimate the diffuse attenu-ation coef?cient for extremely turbid and shallow waters.
Lake Taihu is a typical large shallow lake,with a maximal depth of less than3m and an average depth of only1.9m(Zhang,Qin,et al., 2007).Furthermore,it is located in the subtropical monsoon zone and affected by the prevailing typhoons(Qin,Xu,Wu,Luo,&Zhang,2007). The waters of this lake are consistently extremely turbid,with the exception of East Taihu Bay and some of the East Lake,where the lake bottom can be covered by hydrophytes(Ma,Duan,Gu,&Zhang, 2008).Additionally,Lake Taihu has frequent algae blooms in spring and summer,severely polluting the lake water(Hu et al.,2010).The blooms can become so severe,that in2007the normal life of the several million nearby residents was interrupted and adversely affected by the algae-polluted waters in this lake(Wang,Shi,&Tang,2011).
We have pursued the estimation of K d(PAR)from MERIS data,and focused on Lake Taihu.We were motivated by two factors:needing further information about this particular ecosystem and lack of high spatial resolution K d(PAR)data from the lake.The K d(PAR)is an ecolog-ically important index that allows estimation of the availability of light to underwater communities and thus can provide critical information for understanding the optical,biological,and ecological processes and phenomena in Lake Taihu ecosystem.There is a lack of K d(PAR)data with high spatial resolution for Lake Taihu;the studies on the character-istics of K d(PAR)based on in situ measurements lack spatial coverage (Zhang,Zhang,et al.,2007;Zhang et al.,2012).
Therefore,we sought to address the needs of both the research com-munity and environmental groups in understanding the light climate environment in Lake Taihu and the lack of knowledge of the validating satellite-derived K d(PAR)products.The aims of the present study are to:(1)collect high quality K d(PAR)data for Lake Taihu from repeated cruise surveys and determine the main contributor to K d(PAR),(2)de-velop a simple model to estimate the diffuse attenuation coef?cient for a shallow and extremely turbid inland lake based on MERIS image data,and(3)generate maps of K d(PAR)data with high spatial resolu-tion,and characterize the spatial and temporal variations of K d(PAR) in Lake Taihu by means of the derived K d(PAR)from MERIS images from2003to2010.
2.Materials and methods
2.1.In situ data
Samples were collected in Lake Taihu from November2005to January 2010to support algorithm development and validation.To obtain high quality in situ K d(PAR)data,measurements were taken under clear skies,with no wind or very low winds,between the hours of8:30and 16:30.
Data were collected during nine?eld investigation cruises in Lake Taihu(Fig.1),?ve of which were in Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center,and four of which were in the entire lake.Sampling dates,number of samples,and regions of the lake sampled are shown in Table1.From the nine cruises,a total of190samples were collected cov-ering different seasons.This allowed us to sample of a wide range of water conditions,and importantly,the samples gathered during these cruises covered a wide range of biogeochemical and optical variability in this inland water,from water masses in?uenced by strong terrestrial input to those in?uenced by strong phytoplankton blooms.
Surface,middle and bottom water samples were collected,and then mixed together in2-L acid-washed bottles,and held on ice at4°C while in the?eld.All samples were transported,during the day of collection, to a laboratory at the Taihu Lake Laboratory Ecosystem Research (TLLER)of the Chinese Academy of Sciences,on the shores of Meiliang Bay.
2.2.Water quality measurement
We recorded four water quality parameters:Chl-a,TSM,concen-tration of inorganic suspended matter(ISM),and the concentration of organic suspended matter(OSM).The water samples were?ltered with Whatman GF/F?berglass?lters with an average pore size of 0.7μm.The pigments were extracted using90%ethanol at80°C, and spectrophotometrically analyzed to obtain the absorption coef?-cients at750nm and665nm,and further calculated to get Chl-a con-centration(Jespersen&Christoffersen,1987).The0.7-μm GF/F?lters were pre-combusted at550°C for4h to remove organic traces for de-termining the TSM.After cooling,the?lters were pre-weighed and stored in numbered Petri dishes.After?ltration and rinsing,we stored the?lters at4°C.The?lters were dried at105°C for4h,and reweighed.The TSM weight was obtained by subtracting the second weight measurement from the?rst measurement.The TSM concentra-tion was obtained by dividing the weight by the volume?ltered.The?l-ters were then re-combusted at550°C for4h to remove the organic
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fraction and weighed again to obtain the ISM.The OSM was the differ-ence between the TSM and the ISM.2.3.CDOM absorption measurement
To measure the CDOM absorption αCDOM ,the particulate matter in the water samples was removed by ?ltration through a 47mm diame-ter Whatman GF/F ?berglass ?lter with 0.7μm pores,and then re-?ltered through a 25mm diameter Millipore ?lter with 0.22μm pores.The absorption of the ?ltrate was measured between 240and 800nm,at 1nm intervals,with a Shimadzu UV –V spectrophotometer with a 4cm quartz https://www.doczj.com/doc/5f12478822.html,li-Q water was used as a reference.The absorption coef ?cients were obtained using the method described by Bricaud,Morel,and Prieur (1981)
2.4.K d (PAR)and Secchi disk depth (SDD)measurements
The downwelling PAR measurements were taken on the sunny side of the boat,just below the water surface (0m)and at six depths (0.2,0.5,0.75,1.0,1.5,and 2.0m)using a Li-Cor 192SA underwater quantum
sensor connected to a Li-Cor 1400datalogger (https://www.doczj.com/doc/5f12478822.html, ),without water surface PAR intensity correction.The Li-Cor 192SA was installed in a 2009S lowering frame which provided stability for proper orientation of the sensor,minimized shading effects,and featured a lower mounting ring for stabilizing weight attachment if necessary.
Using the instantaneous mode of the Li-1400datalogger (con ?gured to display 15-second running averages of 1-second measurements),for each depth three values of PAR were recorded at 1-minute intervals and their average value was considered as the PAR intensity for that depth.The K d (PAR)could be determined from nonlinear regression of the un-derwater irradiance pro ?le (Zhang et al.,2012).Only K d (PAR)from re-gressions with R 2≥0.99were accepted.The number of depths used in the regressions was 4–7,determined by the penetration depth.A standard Secchi disk with 30-cm diameter was used to measure Secchi disk depth (SDD).2.5.MERIS images
MERIS is a 68.5°?eld-of-view,push-broom,imaging spectrome-ter that measures the solar radiation re ?ected by the Earth,at a ground spatial resolution of 300m,in 15spectral bands (B1:407.5–417.5nm;B2:437.5–447.5nm;B3:485–495nm;B4:505–515nm;B5:555–565nm;B6:615–625nm;B7:660–670nm;B8:677.5–685nm;B9:703.75–713.75nm;B10:750–757.5nm;B11:758.75–762.5nm;B12:771.25–786.25nm;B13:845–885nm;B14:875–895nm and B15:890–910nm).
A total of 39MERIS images of Lake Taihu,acquired from 2003to 2010,were downloaded from the European Space Agency (ESA)Earthnet Online (http://earth.esa.int/).The images are 1P processed,meaning that they have undergone calibration of top-of-atmosphere ra-diance (TOA).The images were processed using BEAM 4.8software for geometric calibration and smile correction.Of the 39MERIS images,we discarded two because they were poor quality,de ?ned by the quality control ?ags in the data products (i.e.,problems due to clouds,stray light,high TOA,low water leaving radiance,large solar/viewing angles,and navigation failure).Thus we had a total of 37high quality MERIS im-ages (260m ?290m),taken during 2003–2010,which were available for our further
study.
Fig 1.Location of Lake Taihu,and the sampling areas within the lake.
Table 1
Sampling date,number of samples,and distribution of sampling sites for nine cruises in Lake Taihu during 2005–2009.Sampling date Number of samples Sampling sites
17–19November 200532Entire lake
17December 200514Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center 16March 200614Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center 17April 2006
14Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center 1–2January 2007
25Entire lake 20–22November 200732Entire lake 13–15May 200831Entire lake
13January 200914Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center 14January 2009
14
Meiliang Bay,Zhushan Bay,Gonghu Bay and lake center
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Three plug-in algorithms(Case-2Regional Processer,Boreal Lakes Processor,and Eutrophic Lakes Processor)were developed for MERIS 1P data,which are the basic Beam4.8toolbox for MERIS and can be freely downloaded from the Beam Project home page http://www. Brockmanncosult.de/beam/.These processers are based on trained arti-?cial neural network(ANN)that can retrieve the remote sensing re?ec-tance at the bottom-of-atmosphere from MERIS1P TOA radiances and then generate the inherent optical parameters and subsequently the concentrations of water constituents.Therefore,the core of these pro-cessors includes two ANNs:one network for performing atmospheric correction and other for deriving inherent optical parameters and con-centrations of water constituents(Doerffer&Schiller,2007).The train-ing datasets of ANN for Boreal Lakes Processor and Eutrophic Lakes Processor are based on the measurements and optical modeling from several Finland and Spanglish lakes(Doerffer&Schiller,2007,2008). Regional Processer was designed not for being used in lake waters (Doerffer&Schiller,2007,2008).Most of boreal lakes are not euphotic and therefore Boreal Lakes Processor cannot suitable for Lake Taihu waters.Therefore,atmospheric correction was carried out using ANN provided by Eutrophic Lakes Processor of Beam4.8.
https://www.doczj.com/doc/5f12478822.html,parison of satellite and in situ data
The satellite data and in situ ground data should be concurrent with-in a period determined by the natural variation of the process being measured.We set the criterion for matching satellite and in situ obser-vations to≤2days,to maximize the number of possible matching pairs between satellite and in situ observations.Among the190samples, our criterion produced96“matches”of data,i.e.,data pairs of MERIS im-ages and in situ data collocated in space(same pixel).Subsequently,we evenly divided the“matches”data into two parts:one part comprising 48samples was used to construct the model for estimating K d(PAR) from MERIS image data,and the other part comprising48samples was used for validation of the model.
2.7.Wind speed data acquisition
To determine the relationship between wind speeds and the K d(PAR) values,the long-term wind speed data for Lake Taihu from2003to 2010,that could be matched to MERIS-derived K d(PAR)data,were downloaded from the China Meteorological Data Sharing Service Sys-tem(http://cdc.c,https://www.doczj.com/doc/5f12478822.html,/home.do).
The wind data were collected from the site(31°4′N,120°26′E)of Dongshan meteorological station(Fig.1).The site was located at the Peak of Dongshan Mountain,with an evaluation of175m and being surrounded by Lake Taihu in the three directions(Fig.1).Therefore, the data of this site could re?ect the real characteristics of wind proper-ties in Lake Taihu.The wind data include the day-averaged wind speeds, season-averaged wind speeds,year-averaged wind speed,and wind directions.The wind speed data were reordered per5min and all avail-able5minute-observation wind speed data of a day was used to calcu-late the day-averaged wind speeds;the seasonal and year-averaged wind speeds were thereby calculated from the day-averaged wind speeds.The wind speed data have an accuracy of0.1m s?1.
2.8.Statistical analysis and accuracy assessment
Statistical analyses including calculations of the average,maximum, and minimum value,and linear and non-linear regressions,were performed using SPSS17.0software(Statistical Program for Social Sciences).Correlation analysis was used to investigate the relationships between variables using SPSS software.Signi?cance levels are reported to be signi?cant(p b0.05)or not signi?cant(p N0.05).
The accuracy of algorithms was assessed by calculating the relative error(RE),mean absolute percent error(MAPE),and root-mean-square error(RMSE)between measured and predicted values,using the follow-ing equations.
RE?
K d PAR
eT
me asured
?K d PAR
eT
estimated
K d PAR
eT
measured
e1TMAPE?
1
N
X N
i?1
K d PAR
eT
measured;i
?K d PAR
eT
estimated;i
K d PAR
eT
measured;i
e2TRMSE?
??????????????????????????????????????????????????????????????????????????????????????????????
1X N
i?1
K d PAR
eT
measured;i
?K d PAR
eT
estimated;i
2
v u
u t
e3T
where N is the number of samples,K d(PAR)measured,is the measured K d(PAR),and K d(PAR)estimated is the estimated K d(PAR).
3.Results
3.1.Limnological conditions
There were large variations in each of the eight bio-optical parame-ters measured during the?eld study(Table2).The concentration of TSM ranged considerably,from7.2mg L?1to246.9mg L?1,with an average of76.0±55.1mg L?1;the highest values were recorded in the southern part of the lake,which was in?uenced by river discharge and sediment resuspension.The average value of76.0mg L?1TSM for Lake Taihu is higher when compared to other water bodies(Kirk, 2011;Saulquin et al.,2013),indicating that Lake Taihu could be consid-ered as an extremely turbid inland water.Overall,the concentrations of ISM are higher than that of OSM.The relatively high ISM/TSM ratios we observed(average0.8±0.2),demonstrated that TSM was dominated by ISM in Lake Taihu.
The Chl-a concentrations ranged from0.7to372.8mg m?3,with an average of26.3±46.7mg m?3The maximal Chl-a values corresponded to strong phytoplankton blooms,which typically occurred in summer; the lowest Chl-a value was observed in winter.The variation in the CDOM absorption coef?cient at440nm,encompasses most of the natural variability reported for highly contrasted coastal areas and complex in-land waters(Babin&Stramski,2002;Babin et al.,2003;Tilstone et al., 2012;Zhang,van Dijk,Liu,Zhu,&Qin,2009).The maximal CDOM absorp-tion was typically recorded in summer,and was generated an autochtho-nous production especially that related to the bacterial activity during the senescence of phytoplankton biomass(Vantrepotte,Loisel,Dessailly,& Mériaux,2012;Zhang et al.,2009,2011).
Both K d(PAR)and SDD showed large variation in Lake Taihu: K d(PAR)ranged from0.7m?1to15.4m?1with an average of5.5±3.2m?1,and SDD was varied from8.0cm to85cm with a average of 38.0±14.1cm.The high K d(PAR)and low SDD values were character-istic of a typical shallow and extremely turbid lake,where sediment sus-pension noticeably increased light attenuation and decreased water clarity.
Table2
Maximum,minimum,average and S.D.of eight bio-optical parameters in Lake Taihu: K d(PAR),TSM,ISM,OSM,TSM/ISM,Chl-a,a CDOM(440),SDD.
Parameters Maximum Minimum Average S.D.
K d(PAR)(m?1)15.40.7 5.5 3.2 TSM(mg L?1)246.97.27655.1 OSM(mg L?1)85.2 1.211.610.2 ISM(mg L?1)206.9 5.163.748.2 ISM/TSM0.90.10.80.2 Chl-a(mg m?3)372.80.726.346.7 a CDOM(440)(m?1) 2.40.30.80.3 SDD(cm)8583814.1
368K.Shi et al./Remote Sensing of Environment140(2014)365–377
3.2.Relationships between K d (PAR)and water constituent concentrations Attenuation of light in water depends on concentrations of particu-late matter and dissolved matter,and therefore can be expressed by the four indicators:TSM,OSM,Chl-a ,and CDOM (Fig.2).There was a signi ?cant positive correlation between K d (PAR)and TSM.The determi-nation coef ?cient between K d (PAR)and TSM (R 2=0.91)was signi ?-cantly higher than that between K d (PAR)and Chl-a (R 2=0.11),and between K d (PAR)and CDOM (R 2=0.01).Thus we demonstrate that TSM was the main contributor to the PAR attenuation of Lake Taihu.The sediments in this shallow lake are vulnerable to suspension,espe-cially during typhoon season,resulting in relatively high concentrations of TSM in the water.
3.3.Calibration and validation of K d (PAR)estimation model
The calibration dataset contained 48samples,with in situ K d (PAR)ranging from 1.2m ?1to 13.5m ?1,and an average of 5.9±3.1m ?1.To determine the best band by which to estimate K d (PAR)in the ex-tremely turbid Lake Taihu,correlation analysis was carried out between in situ K d (PAR)and two kinds of MERIS image data:uncorrected top-of-atmosphere radiance (TOA),and atmospheric re ?ectance corrected by the trained arti ?cial neural network (ANN)provided by Beam 4.8soft-ware (Fig.3).
There was a signi ?cant positive correlation between K d (PAR)and uncorrected MERIS TOA,and the uncorrected MERIS TOA at band 10(central wavelength:753nm)gave the best correlation with the in situ K d (PAR)measurements (R =0.896;Fig.3).The correlation coef-?cient between K d (PAR)and uncorrected MERIS TOA was signi ?cantly higher than that between K d (PAR)and MERIS re ?ectance corrected by ANN.
The noticeably high correlations between K d (PAR)and MERIS data at band 10,were similar to those between TSM and in situ remote sens-ing re ?ectance at the near-infrared wavelengths (Binding,Jerome,Bukata,&Booty,2010;Zhang,Li,Shen,&Chen,2008;Zhang et al.,2012).For the ANN-corrected MERIS data,band 10gave the best corre-lation coef ?cient with the in situ K d (PAR)measurements.The markedly higher correlation coef ?cient for the uncorrected MERIS TOA than for the ANN corrected data,suggests that there are problems with the at-mospheric correction.Among the four types of mathematical function we used to develop the relationships between K d (PAR)and uncorrected MERIS TOA dataset,(linear,logarithmic,exponential,and quadratic),
the logarithmic function gave the best precision,with the highest deter-mination coef ?cient (R 2=0.74)and the lowest MAPE (29.8%)and RMSE (1.6m ?1)(Fig.4).
To further understand the applicability of this simple model for esti-mating K d (PAR)from MERIS data,we assessed its performance using the independent validation dataset of 48samples.To validate the model,we used values for K d (PAR)from 1.3m ?1to 11.2m ?1with an average of 5.9±2.8m ?1,(which lay within the range of K d (PAR)used to calibrate the model).Without adjusting optimal locations of the band,and re-parameterization,the simple model performed well in estimation of K d (PAR)(Fig.4).
The RE of the simple model ranged from 0.004to 1.32,with MAPE of 0.294(RMSE =1.7m ?1)(Fig.5).The relative errors of 50%and 60%samples for the simple model are below 0.20and below 0.30,https://www.doczj.com/doc/5f12478822.html,parisons between the measured and predicted K d (PAR)using the simple model from MERIS TOA data,showed that these values were in good agreement,with a highly signi ?cant linear relationship (R 2=0.73)(Fig.4B).The measured and predicted K d (PAR)were even-ly distributed along the 1:1line (Fig.4B).These results indicate that the simple single band model,from uncorrected MERIS TOA data,could be used to estimate K d (PAR)for the validation dataset in this extremely turbid water with satisfactory performance.
The relationships between RE and TSM,Chl-a ,and TSM/Chl-a were examined to demonstrate the ability of the simple model to estimate K d (PAR)(Fig.6).Although the RE decreases with increasing TSM/Chl-a
,
Fig.2.Relationships between K d (PAR)and four bio-optical parameters of the water:TSM (A),OSM (B),Chl-a (C),and a CDOM (440)
(D).
Fig.3.Correlation coef ?cients between in situ K d (PAR)and (i)MERIS uncorrected top-of-atmosphere radiance (TOA),and (ii)MERIS atmospheric re ?ectance corrected by the trained arti ?cial neural network (ANN)provided by Eutrophic Lakes Processors of Beam 4.8software.
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there is no statistically signi ?cant relationship between RE and TSM,or between RE and Chl-a .Thus,the simple model may not be applicable to waters with a low ratio of TSM/Chl-a ,especially to waters where phyto-plankton is the main contributor of K d (PAR).However,the simple model could have satisfactory performance in extremely turbid waters,such as Lake Tahu and other large shallow lakes (Arst,Noges,Noges,&Paavel,2008;V-Balogh,Nemeth,&Voros,2009).Thus,our simple model could be used to derive a long-term K d (PAR)for Lake Taihu from all available MERIS image data collected between 2003and 2010,to facilitate study of the spatial and temporal variability of K d (PAR)in Lake Taihu.
3.4.Temporal characterization of K d (PAR)in Lake Taihu
Maps of MERIS derived K d (PAR)for four seasons in Lake Taihu,using the simple single band model,are shown in There was strong seasonal variability in K d (PAR)over the entire lake,and the K d (PAR)values shows the typical seasonal characteristics (Fig.7).The values for K d (PAR)were highest in summer (June –August),moderate in spring and autumn (March –May and September –November),and lowest in
winter (December –February).The K d (PAR)values were markedly in-creased in spring compared to autumn (T -test,p b 0.05).
The distribution frequency of K d (PAR)in each season was calculated from the estimated,K d (PAR)data over the periods of 2003to 2010(Fig.8).In winter,K d (PAR)ranged from 0.049to 11.68m ?1,with an average of 5.9±3.4m ?1,and the most frequent value was 8.6m ?1.In spring,K d (PAR)ranged from 0.39to 20m ?1,with an average of 10.0±5.7m ?1;the most frequent value was 14.0m ?1.In summer,K d (PAR)ranged from 1.4to 23.0m ?1,with an average of 12.4±6.7m ?1;the most frequent value was 15.0m ?1.In autumn,K d (PAR)ranged from 0.2to 15.1m ?1,with an average of 7.6±6.7m ?1;and the most frequent value was 8.0m ?1.
We investigated the intra-seasonal variability of K d (PAR)for Lake Taihu by calculating the intra-seasonal standard deviation (S.D.)based on the derived K d (PAR)from the simple band model (Fig.9).The largest and smallest S.D.of K d (PAR)were found in summer and winter,respec-tively,and the S.D.in autumn was greater than that in spring.The day-to-day variability of K d (PAR)in summer and autumn was relatively large,whereas in spring and winter it was relatively small.
Temporal changes from 2003to 2010in average MERIS derived K d (PAR)values were determined for the six regions of Lake Taihu (Meiliang Bay,Gonghu Bay,Zhushan Bay,East Lake Taihu,the lake center,and the southern part),by averaging (spatially and temporally)all valid pixels over water for each region (Fig.10).The K d (PAR)values
were
Fig.4.Calibration (A)and validation (B)of simple single band model to estimate K d (PAR)in turbid Lake Taihu,using uncorrected top-of-atmosphere radiance (TOA)data from MERIS band
10.
Fig.5.Frequency distribution and cumulative percentage of the relative errors for the sim-ple single band model to estimate K d
(PAR).
Fig.6.Relationship between relative error and TSM concentration (A),Chl-a concentration (B),and TSM/Chl-a (C)for the simple single band model to estimate K d (PAR).
370K.Shi et al./Remote Sensing of Environment 140(2014)365–377
generally higher in the southern part of the lake than in the east.Peak summer K d(PAR)for the southern part of Lake Taihu ranged from 16.7m?1to23.8m?1,whereas those in peak East Lake Taihu did not exceed16.8m?1.Minimum K d(PAR)values in the southern part of Lake Taihu were more than6.1m?1and minimum values for the other parts of the lake were below8.7m?1
.
Fig.7.Maps of MERIS derived K d(PAR)for four seasons in Lake Taihu using the simple single band model:(A)spring,(B)summer,(C)autumn;and(D)
winter.
Fig.8.Frequency distribution of K d(PAR)in four seasons from2003to2010in Lake Taihu estimated from MERIS images with the simple model.371
K.Shi et al./Remote Sensing of Environment140(2014)365–377
3.5.Spatial characterization of K d (PAR)in Lake Taihu
The MERIS derived K d (PAR)data from 2003to 2010were averaged to calculate the regional K d (PAR)distribution for Lake Taihu (Fig.11A).The K d (PAR)values of the entire lake were varied from 0.1to 15.5m ?1,with an average of 7.8±4.3m ?1,and a mode of 13.3m ?1.The K d (PAR)was highest in the southern part of Lake Taihu,followed by the lake center and Meiliang Bay in the north.In Meiliang Bay,K d (PAR)increased from inner to outer regions.The K d (PAR)was lowest in Gonghu Bay,Zhushan Bay and East Lake Taihu.The results con ?rm
the conclusion that Lake Taihu is generally highly turbid,and particularly so in the south and the central parts of this lake.The results are consis-tent with several previous studies (Wang et al.,2011;Zhang et al.,2008,2012).
The spatial distribution of S.D.values were calculated from the MERIS derived K d (PAR)(Fig.11B).The largest K d (PAR)S.D.values were in Zhushan Bay and Meiliang Bay,and the smallest K d (PAR)S.D.values were in the southern part and the lake center.Overall,the distri-bution of K d (PAR)S.D was opposite to that of K d (PAR)itself,that is the region with high K d (PAR)values generally had low S.D.values.
The variations of K d (PAR)in the southern part and the lake center were smaller than in other regions of Lake Taihu,and the K d (PAR)in the southern part and the lake center were relatively high.4.Discussion
4.1.Characteristics of K d (PAR)in Lake Taihu from in situ data
In the present study,the in situ K d (PAR)values were high,ranging from 0.7m ?1to 15.4m ?1,with an average of 5.5m ?1,re ?ecting that Lake Taihu is an extremely turbid water body.These results were similar to those for other shallow,turbid,inland waters (Arst et al.,2008;V-Balogh et al.,2009),and previous studies in Lake Taihu (Zhang et al.,2006,2012).However,the K d (PAR)values in Lake Taihu are higher than those for open sea waters (Morel et al.,2007;Zhao et al.,2013),and slightly turbid estuaries (Saulquin et al.,2013;Wang et al.,2009).
For
Fig.9.Maps of the intra-seasonal standard deviation (S.D.)of K d (PAR)in Lake Taihu derived using the simple single band model:(A)spring,(B)summer,(C)autumn;and (D)
winter.
Fig.10.Temporal variations of MERIS K d (PAR)in six regions of Lake Taihu between 2003and 2010.
372K.Shi et al./Remote Sensing of Environment 140(2014)365–377
example,the average value of the in situ K d (PAR)measurements for Lake Taihu was larger than that three times as that for clear and slightly turbid ocean waters.
K d (PAR)is controlled by four substances,namely CDOM,TSM,phy-toplankton and pure water.(Kirk,2011;Zhang,Zhang,et al.,2007).Among these,the concentrations of CDOM,TSM,and Chl-a ,and their relative contributions to K d (PAR),vary between for different waters,and within the same water body in different seasons (Christian &Sheng,2003;Lund-Hansen,2004;Phlips,Aldridge,Schelske,&Crisman,1995;Zhang,Zhang,et al.,2007;Zhang et al.,2006).For example,in open sea waters where the optical properties are simply controlled by phytoplankton,K d (PAR)is also dominated by phytoplankton and its as-sociated degradations.In clear open sea,Chl-a explained more than 90%of the variation in K d (490)(diffuse attenuation coef ?cient at 490nm,as a surrogate for K d (PAR)).In contrast,in extremely turbid,shallow lake waters,TSM (dominated by non-phytoplankton)controlled the absorp-tion,scattering,and PAR (Arst et al.,2008;Zhang,Zhang,et al.,2007);in Lake Taihu,TSM could explain 97.5%variation of K d (PAR)(Zhang,Zhang,et al.,2007).
The relationships we derived from the ?eld measurements between K d (PAR)and the three water constituents con ?rmed that high TSM con-centrations dominate the K d (PAR)properties in Lake Taihu.The strong effect of TSM on K d (PAR)in Lake Taihu is mainly caused by strong sed-iment re-suspension,which is particularly important in such shallow
polymictic aquatic lake ecosystems (James,Best,&Barko,2004;Zhang,Zhang,et al.,2007).Understanding the individual contributions of CDOM,Chl-a ,and TSM to K d (PAR)can facilitate the development of algorithms for estimating K d (PAR)from remote sensing data.After de-termination of the main contributor to K d (PAR),the corresponding ap-propriate algorithm can be developed.
Previous studies on shallow lakes and estuaries have demonstrated that TSM has noticeable impacts on water transparency,diffuse attenu-ation coef ?cients,euphotic depth and primary productivity (James,Barko,&Butler,2004;James,Best,et al.,2004;James,Martin,Wool,&Wang,1997;Phlips,Lynch,&Badylak,1995;Pierson,Markensten,&Strombeck,2003;Van Duin et al.,2001;Zhang et al.,2012).For example,Phlips,Aldridge,et al.(1995)reported that diffuse attenuation coef ?-cients were more closely related to ISM than to Chl-a in a shallow inner shelf lagoon,Florida Bay,USA.James,Best,et al.(2004)demon-strated that the PAR attenuation coef ?cients exceeded 10m ?1under slight wind wave conditions,and could exceed 25m ?1under strong wind wave conditions,and that PAR was related to TSM and transparency in a large shallow lake,Peoria Lake.
In our study,we measured the underwater PAR at only 4–7depths,because the lake was very shallow,and there were frequent waves and wind in Lake Taihu.The limited number of the depths at which we took measurements is markedly smaller than that typically used in deep lakes,estuaries,and open sea waters (Lund-Hansen,Andersen,Nielsen,&Pejrup,2010).In addition,the PAR intensity was less than the detection limitation at depths b 1.5or 2.0m,due to the average shallow depth and extreme turbidity in Lake Taihu.However,the ?tting error of K d (PAR)was relatively small,considering the high determina-tion coef ?cients (R 2N 0.99).
4.2.Implications from the simple model
The re ?ectance that is suitable for estimating TSM in extremely tur-bid waters,can also be used to estimate K d (PAR)because of a signi ?cant correlation between TSM and K d (PAR).Several previous studies sug-gested that in very turbid inland waters,TSM could be accurately de-rived from the remote sensing re ?ectance at near red or near infrared bands (Nechad,Ruddick,&Park,2010;Odermatt,Gitelson,Brando,&Schaepman,2012;Ondrusek et al.,2012;Song et al.,2012;Zhang et al.,2008).Furthermore,the relationships between the re ?ectance at red or near infrared bands and TSM are what would be expected theo-retically (Nechad et al.,2010).Thus,we expected that our model based on the single,near infrared band of MERIS image data (band 10),would be most successful in predicting the K d (PAR)values in Lake Taihu.Hence,we recommended the band 10of MERIS data for re-trieving the K d (PAR)values in Lake Taihu.
The simple model proposed in this study could distinguish between high TSM concentration and low Chl-a ,however,we do not have enough samples to tell whether the model can accurately estimate K d (PAR)in the waters with low TSM and high Chl-a .It must be empha-sized that the simple model may be only suitable for the regional waters with K d (PAR)N 1.16m ?1and TSM/Chl-a N 0.587(*103).In addition,in other regional waters with different sediment types and bio-optical properties,the coef ?cients of the simple model should be calibrated according to the water bio-optical properties.
The performance of both empirical (Morel et al.,2007)and semi-analytical models (Lee,Du,et al.,2005)developed for clear waters was assessed for Lake Taihu by Zhang et al.(2012).The models were de-termined as non-applicable to Lake Taihu due to the large differences in bio-optical properties between the waters;the empirical and semi-analytical models were successfully developed for clear or slightly tur-bid ocean waters,using the band ratios of blue-green (490/555)or blue-red (490/665,490/709,488/667,488/645)(Doron et al.,2007;Morel et al.,2007;Wang et al.,2009).Zhang et al.(2012)con ?rmed that remote sensing re ?ectance at the wavelength b 700nm could not be used to estimate K d (PAR)in an extremely turbid water,and
the
Fig.11.Map of K d (PAR)(A)and the corresponding SD (B)in Lake Taihu averaged from all K d (PAR)estimates from the MERIS images from 2003to 2010.
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algorithms based on wavelength combinations developed for the clear or the slightly turbid ocean waters were not applicable to Lake Taihu.
The CDOM absorption coef?cient at short wavelengths in Lake Taihu was noticeably larger than that in clear ocean waters.The strong CDOM absorption in Lake Taihu will remove the signal of K d(PAR)at short wavelengths,that is to say,the re?ectance at wavelength N700nm may be used to estimate K d(PAR)in the extremely turbid waters. Based on the MODIS-Aqua data,Wang et al.(2011)derived K d(490) (b4.0m?1,corresponding to K d(PAR)b3.0m?1)of Lake Taihu,using the semi-analytical model developed for the slightly turbid Chesapeake Bay waters(Wang et al.,2009).However,no in situ measurement data of Lake Taihu were presented by Wang et al.(2011)to validate the re-sults,and the K d(PAR)values derived by Wang et al.(2011)markedly underestimated the in situ measurement of K d(PAR)in Lake Taihu.
One challenge for the reliable retrieval of optical parameters over inland waters is the accurate atmospheric correction(Li et al.,2012).At-mospheric correction is an unsolved problem for inland waters,espe-cially for extremely turbid inland waters(Tebbs,Remedios,&Harper, 2013);standard methods for the open clear ocean fail because backscat-tering at the near infrared wavelengths overcomes the absorption by water molecules,invalidating the assumption of negligible water leav-ing radiance.Our simple algorithm,derived from the single band TOA of MERIS image data,gave a better correlation than did the ANN method of corrected re?ectance,suggesting problems with the atmospheric cor-rection.The ANN correction method was derived using the training dataset collected from European coastal waters or inland lakes,which may not produce reliable results for contrasting waters,such as the highly turbid and eutrophic Lake Taihu.
Despite the effects of atmosphere,TOA at the near infrared bands still can re?ect the K d(PAR)information due to the strong particulate back-scattering in an extremely turbid water.However,using a correction method with inappropriate parameters(as discussed in the preceding paragraph),may destroy the intrinsic relationship between the satellite signals and the optical parameters.Our algorithm provides built in cor-rection for atmospheric variability,and this was not improved upon by adopting the ANN correction.Our results are therefore in agreement with the previous studies demonstrating that atmospheric correction is not a prerequisite for the estimations of the optical parameters of water in highly turbid and hyper-eutrophic lakes(Matthews,Bernard, &Robertson,2012;Matthews,Bernard,&Winter,2010;Reinart& Kutser,2006;Tebbs et al.,2013).
The errors of the models can be attributed to the time difference be-tween the in situ data and the images.As mentioned in the Section2.6, the time interval for matching satellite and in situ observation were set to≤2days;this crude value was selected in order to maximize the number of possible matching pairs between in situ and satellite observa-tions.However,especially in Lake Taihu where TSM was easily resus-pended by wind,the time interval would mean that there is a certain difference in wind speed at the moments of the in situ and satellite ob-servations;this indicates that K d(PAR)at the time of the in situ and sat-ellite observations was different,which will lead to errors for the model proposed in this study.Taking into consideration the time difference be-tween the in situ data and the images,and the spatial resolution,the ac-curacy is acceptable for estimating K d(PAR)in inland eutrophic waters. Our results could meet the NASA criterion for35%relative accuracy.
Based on the relationship between K d(PAR)and euphotic depth,the model for deriving euphotic depth from MERIS images was developed as following:
D eu?4:605
e4T
where,D eu is euphotic depth,and TOA(B10)is the top-of-atmosphere radiance of MERIS images at band10.Therefore,the MERIS euphotic depth products could be generated using Eq.(4).Euphotic depth is not only a quality index of an lake ecosystem(Bergamino et al.,2010),but also an important input parameter for estimating primary produc-tion(Zhang,Zhang,et al.,2007;Zhang et al.,2012)and heat transfer in the upper water column(Vincent,Mueller,&Vincent,2008).The MERIS products of euphotic depth and Chl-a(Shi et al.,2013)thus allow us to estimate lake primary production from satellite data,pre-senting new insights for the shallow and turbid lake carbon cycle and climate change.
4.3.Mechanisms of the spatial–temporal variations in K d(PAR)distribution
We have shown that the K d(PAR)values in Lake Taihu are markedly different in different regions of the lake,and that these spatial variations can be attributed to the changes in TSM concentrations.As a shallow lake,TSM concentration is greatly affected by wave driven sediment re-suspension(Zhang et al.,2006).By using a dynamic ratio index(the square root of the surface area divided by the average depth),a lake's susceptibility to sediment re-suspension induced by wind-driven waves can be determined(Wang,Nim,Son,&Shi,2012;Zhang,Zhang, et al.,2007).Bachmann,Hoyer,and Can?eld(2000)suggest that resuspension would be driven by wave disturbance when the dynamic ratio exceeded0.8km m?1.The dynamic ratio for Lake Taihu calculated by Zhang,Zhang,et al.(2007)was25.6km m?1,which considerably exceeds the critical value of0.8km m?1,meaning that resuspension in Lake Taihu is largely driven by winds,which could be supported by the signi?cant correlation between K d(PAR)values and wind speeds (Fig.12).
The unconsolidated sediments released by extensive wind wave mixing result in signi?cantly high TSM levels,and therefore in?uence the water turbidity characteristics in lakes,especially those that are shallow(Havens et al.,2011;James et al.,2008).Numerous studies have shown that the wind speeds are the dominant contributors to the spatial–temporal variations of K d(PAR)in turbid lakes and estuaries (Le,Hu,English,Cannizzaro,&Kovach,2013;Paavel,Arst,&Reinart, 2008;Wang et al.,2009,2012;Zhang et al.,2006).
We now further investigate the correlation between K d(PAR)and wind speeds in Lake Taihu.All valid pixels over the entire lake water were spatially averaged to generate the corresponding time average value for Lake Taihu(Fig.12).The change of average K d(PAR)values for the entire lake during2003–2010,completely coincided with that of the wind speeds,with K d(PAR)values increasing with wind speed in-creasing.The correlation between the average K d(PAR)values and the wind speeds was statistically signi?cant(r=0.890,p b0.005),sug-gesting that wind-induced sediment resuspension is the parameter that most directly increases K d(PAR)values,thereby reducing the water transparency and euphotic depth,and determining the seasonal and spatial distribution of K d(PAR)in Lake Taihu.A simple liner function with signi?cantly high determination(R2=0.80,p b0.005)depicts the relationship between K d(PAR)values and the wind speeds in Lake Taihu(Fig.12
).
Fig.12.Relationship between average K d(PAR)of all the lake,derived from MERIS data, and the daily average wind speed.
374K.Shi et al./Remote Sensing of Environment140(2014)365–377
The high K d (PAR)values in the lake center were primarily associated with the high TSM concentrations.In the lake center,with its wide and open water surface area,the winds speeds are higher than they are near the lake shore.With the higher wind speeds,more lake sediments will be resuspended in the lake center than near the shore,resulting in in-creased TSM concentrations and K d (PAR)values in the lake center.Based on in situ measurements from 1993to 2003,Zhang et al.(2006)reported TSM concentrations in the lake center were signi ?cantly higher than at other stations.
When considering the situation in other areas of the lake,in Meiliang Bay,from inner to outer regions,there was an increase in wind waves,which would cause the increases of TSM concentration and K d (PAR)values.The distribution of K d (PAR)values in Meiliang Bay could result from the decrease of euphotic depth from inner to outer regions (Zhang et al.,2006).In the southern part of Lake Taihu,the highest K d (PAR)values can be attributed to TSM carried by incom-ing surface runoff from rivers.In East Lake Taihu,the K d (PAR)values were lowest because:(i)the impacts of wind waves on the water of East Lake Taihu were negligible because the region lies in a narrow and long bay in the south-eastern part of Lake Taihu (Zhang,Zhang,et al.,2007);(ii)the submerged aquatic vegetation has functions of water ?ltration,water puri ?cation,wave abatement,restraining sedi-ment re-suspension and phytoplankton growth;East Lake Taihu is a typical macrophyte-dominated water region,characterized by abun-dant submerged aquatic vegetation communities;and (iii)the concen-trations of water constituents which could contribute to K d (PAR)values were lower in East Lake Taihu than in other regions of the lake.
There were strong seasonal variations in K d (PAR)in Lake Taihu,at-tributable to the seasonal variability in wind speed,the summer algal blooms and high CDOM absorption.The maximum wind speeds were in spring and the minimum in winter (Fig.13).The wind speeds can ex-plain the lowest K d (PAR)values observed in autumn,but not,the highest K d (PAR)in summer,especially in Meiliang Bay,Zhushan Bay,and western part of Lake Taihu.
In the past 15years,algal blooms have frequently occurred in Meiliang Bay,Zhushan Bay,and western part of Lake Taihu in summer (Qin et al.,2007).According to the wind data collected over the periods of 2003–2011,approximate 51%wind directions are southeast in sum-mer,whereas approximate 61%wind directions are northwest in win-ter,meaning that the prevailing wind directions in the two seasons are southeast and northwest,respectively.In winter,the algal blooms occurred not usually,and therefore,the impacts of wind directions on algae accumulation are not obvious;however,in summer,Meiliang Bay,Zhushan Bay and western part lake shore of Lake Taihu are like the bowls that gather most of the algae growing in summer due to the prevailing wind direction.Therefore,these three regions are the place where algal blooms show most frequently,which results in the relative-ly higher K d (PAR)in these regions in summer.
The correlation coef ?cients between the concentration of the six water constituents and the K d (PAR)values in Lake Taihu are presented for each of the four seasons (Fig.14).TSM and ISM concentrations were closely correlated with K d (PAR)in all four seasons.However,for Chl-a
concentrations and CDOM absorption at 440nm there were only high correlation coef ?cient with K d (PAR)in summer,suggesting that K d (PAR)values in summer were co-dominated by TSM,Chl-a and CDOM.Our in situ measurements showed that Chl-a concentrations and CDOM absorption coef ?cients were signi ?cantly higher in summer than in the other three seasons.Therefore,the relatively high K d (PAR)values in summer may be caused by the higher Chl-a concentrations and CDOM absorption coef ?cients (Fig.14).Thus,wind properties and biological activities co-determined the K d (PAR)values in summer in Lake Taihu.5.Conclusion
There was a signi ?cant positive correlation between K d (PAR)and TSM.The determination coef ?cient between K d (PAR)and TSM was sig-ni ?cantly higher than that between K d (PAR)and concentrations of other water constituents.Our results indicated that TSM usually plays a dominant role in the attenuation of light in Lake Taihu.
Analysis of satellite images from MERIS spanning the years 2003to 2010,showed that the top-of-atmosphere (TOA)radiance at band 10gave the best correlation with the in situ K d (PAR)measurements (R 2=0.74;MAPE =29.8%;RMSE =1.6m ?1).These MERIS data was then used to develop a simple model for estimating K d (PAR)in Lake Taihu.
We have validated this simple model,and demonstrated its utility in characterization of the spatial –temporal variations of K d (PAR)distribu-tions in Lake Taihu.Waters in Lake Taihu are consistently highly turbid year-round,and the highest and lowest K d (PAR)were usually found in summer and winter,respectively.Spatially,the high K d (PAR)values were observed in the southern part of the lake and in the lake center,whereas the low K d (PAR)values were recorded in East Lake Taihu.The results of this study could be related to other environmental vari-ables,such as wind speeds,to determine the possible causes of K d (PAR)variations.Based on the MERIS derived K d (PAR)values,there was signi ?cant correlation between K d (PAR)and wind speeds,suggest-ing that wind speeds play a critical role in determining variations in K d (PAR)in Lake Taihu.The simple model we have proposed will have to be calibrated and validated in other turbid inland waters before ap-plying it to other localities.
The present simple model will contribute to our goal of calculating the primary production of Lake Taihu.In our future work,the MERIS products of euphotic depth (for which a long term depth series can be derived from the simple model we have presented),and Chl-a (as demonstrated by Li et al.(2012),and Shi et al.(2013),could be combined with the MODIS lake surface temperature data (which can be downloaded from National Aeronautics and Space Administration (NASA)website (https://www.doczj.com/doc/5f12478822.html,/),to calculate the primary production of Lake Taihu.Acknowledgments
This study was jointly funded by the National Natural Science Foun-dation of China (grants 41271355,41230744),the Key Program
of
Fig.13.Variations in average seasonal wind speed in Lake Taihu from 2003to
2010.
Fig.14.Correlation coef ?cients between six water quality parameters and the K d (PAR)values in Lake Taihu in four seasons.
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K.Shi et al./Remote Sensing of Environment 140(2014)365–377
Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences(NIGLAS2012135003),and the Provincial Natural Science Foundation of Jiangsu of China(BK2012050).The authors would like to thank Guangwei Zhu,Mingliang Liu,Yan Yin,Longqing Feng,and Zhiqiang Shi for their participation in the?eld samples collection and experimental analysis.The authors also would like to thank the three anonymous reviewers for their useful comments and constructive suggestions.
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