Estimation of chlorophyll-a concentration in estuarine waters:

Transcription

1 Estimation of chlorophyll-a concentration in estuarine waters: case study of the Pearl River estuary Yuanzhi Zhang *, Chuqun Chen #, Hongsheng Zhang *, Xiaofei*, Chen Guiying Chen# *Institute of Space and Earth Information Science The Chinese University of Hong Kong #South China Sea Institute of Oceanology Chinese Academy of Sciences

2 Contents 1. Background 2. Methods 3. Results 4. Conclusions

3 Water quality model Water quality model development for coupled terrestrial and aquatic environments Assessment of the impacts of climate change and land management activities on contaminant fate and transport, biogeochemical cycling, and eutrophication Data modeling from ins-situ measurements and remotesensing observations

4 Inland and coastal waters Water quality polluted by: high sediment load due to soil erosion and run-off from agricultural high populated areas; high agrochemical residuals that increase nutrients, which have resulted in eutrophication; discharge of untreated or semi-treated industrial as well as municipal effluents.

5 Remote sensing of water Concentrations of chlorophyll a (Chl a), coloured dissolved organic matter (CDOM), and total suspended solid (TSS) in waters If any correlation between these quantities can be established, it can help determine the main causes of the decline of water quality This kind of information can result crucial in developing a management strategy of the polluted lakes or rivers or coastal waters

6 Remote sensing of water Coastal and inland waters are generally much more complex. Because of detrital material due to coast erosion or brought from the wind or from rivers, in fact, correlations between compounds fall down. To completely describe these water bodies, known as Case II waters, it is necessary to develop more complex algorithm. This complexity has been reached also through the definition of models, which link the spectral behaviour radiometric field to the optical properties of water compounds. This kind of algorithms is known as physically based algorithms, which often relate to the radiative transfer theory.

7 Water Surface, Subsurface Volumetric, and Bottom Radiance Total radiance, (L t ) recorded by a remote sensing system over water is a function of the electromagnetic energy received from: L p = atmospheric path radiance L s = free-surface layer reflectance L v = subsurface volumetric reflectance L b = bottom reflectance (Jensen, 2006)

8 Percent Reflectance Percent Reflectance a clear water algae-laden water Wavelength (nm) Percent reflectance of clear and algae-laden water based on in situ spectroradiometer measurement. Note the strong chlorophyll a absorption of blue light between 400 and 500 nm and strong chlorophyll a absorption of red light at approximately 675 nm Algae-Laden Water with Various Suspended Sediment Concentrations 500 mg/l Percent reflectance of algae-laden water with various concentrations of suspended sediment ranging from mg/l b mg/l Wavelength (nm) (Han, 1997; Jensen, 2007)

9 clay silt In situ Spectroradiometer Measurement of Clear Water with Various Levels of Clayey and Silty Soil Suspended Sediment Concentrations Reflectance peak shifts toward longer wavelengths as more suspended sediment is added (Lodhi et al., 1997; Jensen, 2007)

10 Remote sensing of chlorophyll-a Remote estimation of the concentrations of water constituents is based on 1) the relationship between the spectral reflectance, 2) the backscattering coefficient, and 3) the absorption coefficient Spectral reflectance Absorption coefficient Backscattering coefficient

11 Remote sensing of chlorophyll-a To retrieve the chlorophyll-a concentration 1) For Case 1 waters (open ocean water) Reflectance in the blue and green spectral regions used to be employed The maximum band ratio (MBR), calculated as the maximum of three-band reflectance ratios at wavelength 443, 490, 520, and 565 nm (ρ443/ρ565, ρ490/ρ565, ρ520/ρ565), was used.

12 Remote sensing of chlorophyll-a To retrieve the chlorophyll-a concentration 2) For Case 2 waters (turbid productive water) These spectral regions cannot be used to estimate chl-a because of i) the overlapping, ii) uncorrelated absorptions by coloured dissolved organic matter (CDOM) and nonalgal particles (NAP).

13 Remote sensing of chlorophyll-a To retrieve the chlorophyll-a concentration 2) For Case 2 waters (turbid productive water) The algorithms developed to estimate chl-a in turbid productive waters are based on reflectance in the red and near infrared (NIR) spectral region. Reflectance peak (Stumpf and Tyler, 1998)

14 Remote sensing of chlorophyll-a To retrieve the chlorophyll-a concentration 2) For Case 2 waters (turbid productive water) Two-band NIR-red model For chl-a concentration above 10 mg m -3 (Gitelson et al 1985, 1986, Gitelson and Kondrat ev 1991, Gitelson 1992, Dekker 1993)

15 Remote sensing of chlorophyll-a To retrieve the chlorophyll-a concentration 2) For Case 2 waters (turbid productive water) A three-band reflectance model was developed for the estimation of pigment contents in terrestrial vegetation (Gitelson et al 2003). The three-band model relates the pigment concentration to reflectance in three spectral bands.

16 Remote sensing of chlorophyll-a Three-band model Two-band model

17 Objective of this study The objective of this study is to estimate the chl-a concentration in the Pearl River estuary (PRE) in China, with focus on: (a) comparing the ability of the models used for Case 1 and Case 2 waters to estimate the chl-a concentration in the range 1 12 mg m -3 (b) assessing the potential of MODIS and MERIS to estimate the chl-a concentrations.

18 Data sets satellite data MODIS and MERIS data

19 Methods 1) The collected reflectance spectra were re-sampled to spectral bands of MODIS and MERIS using the spectral response functions of MODIS and MERIS bands. 2) The simulated reflectances in the red and NIR bands of MODIS and MERIS were used to estimate chl-a concentration by the two band model; 3) The red, red edge, and NIR reflectances were used to estimate chl-a by the two band and three band models; 4) The maximum band ratio( MBR) was calculated as the maximum of the three reflectance ratios in the spectral bands of MODIS.

20 In-situ data 15 water sampling stations in the PRE

21 Spectral features due to Chl-a Chl-a absorption Chl-a peak A) Reflectance of spectra B) Reciprocal of reflectance spectra

22 Shift of Chl-a peak position

23 Chl-a estimation with MODIS data MBR (O Reilly et al 1998) Two band model

24 Testing the reflectance at 675 nm The reciprocal reflectance at 675 nm was weakly related to chl-a (A) showing that, in addition to chl-a, it was strongly affected also by variation in the TSS concentration (B). Thus, an optimization of the band selection in the three band model is needed.

25 Optimization of λ 1 Chl-a [ ( )- (700)]* (730)

26 Optimization of λ Chl-a [ (665)- ( 2 )]* (730)

27 Optimization of λ Chl-a [ (665)- (700)]* ( 3 )

28 The PRE three-band model -1-1 Chl-a [R (665)-R (700)]*R(730) 2.8 (Zhang et al., 2011)

29 Chl-a estimation with three band model Chl-a -1-1 [R (665)-R (700)]*R(730)

30 Three band model -> Two band model Chl-a [ ( )- ( )]* ( ) nm Chl-a ( 3)/ ( 1)

31 Chl-a estimation with MERIS data Two band model Three band model

32 Water quality mapping from MERIS (Zhang et al., 2009)

33 Conclusions A. The MBR calculated in the spectral bands of MODIS proved to be a good proxy for chl-a concentration (R 2 > 0.93). B. NIR red two-band model explained more than 95% of the chl-a variation, and we were able to estimate chl-a concentrations with a root mean square error below 1 mg m -3. C. The two- and three-band NIR red models with MERIS spectral bands accounted for 93% of the chl-a variation. D. These findings imply that the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl-a in the Pearl River estuary.

34 Questions?