Water property monitoring and assessment for China s inland Lake Taihu from

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1 Water property monitoring and assessment for China s inland Lake Taihu from MODIS-Aqua measurements By Wang M, W Shi and J Tan, Remote Sensing of Environment 115 (2011) Wei Wang 9 December 2011

2 Outline Background Methods Results Discussion

3 1. Background Fig. 1 Geo-location of Lake Taihu Wang, 2011

4 1.1 Why do we focus on water property in Taihu? Taihu locates in one of the world s most heavily populated regions with the rapidest economic growth. It helps irrigate millions of ha. of farmlands, also provides drinking water for more than 2 million people, and sustains one of China s most important fisheries (Guo, 2007). Since 1980s, Taihu has became more and more eutrophic. In early June 2007, massive blue-green algae bloomed over Taihu, contaminating tap water for millions of Wuxi residents (Ding, et al., 2007; Hu et al., 2010).

6 Table 1 Comparison between Remote Sensing g( (RS) and In situ measurement Remote Sensing In situ measurement Temporal coverage Spatial coverage Characteristics High rate, long-term Broad field of view Rapid, synoptic, repeated, stable Spectral Resolution Spatial Resolution Temporal Resolution Ma et al., 2009, 2010 Low rate, short-term Fixed locations Labor-intensive, confined to short period, restricted by external conditions MODIS measurements

7 1.2 Basic knowledge for Lake Color RS L sw r L sky L w L g L f Fig. 1 Above water signal composition (Kirk, 2011)

8 L r L L L r L L L sw w sky f g Radiance received by sensors sw F nlw ( ) L Ed (0 ) 0 Lw Ma et al., 2010 Reflectivity of water-atmosphere interface L w Water-leaving radiance L sky Radiance contributions of white cap Sunglint specular f g nl Normalized water-leaving radiance w E d (0 ) Incident radiance above surface F Top of atmosphere radiance at mean Earth-Sun distance 0 L Skylight radiance

9 Sun n Plane of observation Sensor v v Plane of solar incidence Fig. 2 Viewing geometry of above-water measurement (Tang et al., 2004)

10 Table 2 Optical properties es of Lake water (Case ⅡWaters) e s)( (Ma et al., 2010) Water leaving radiance Apparent optical properties Reflectance (AOPs) g Inherent optical properties (IOPs) Normalized water-leaving radiance Remote sensing reflectance Diffuse attenuation coefficient of water Diffuse attenuation coefficient of irradiance Absorption coefficient Scattering coefficient Scattering phase function Volume scattering coefficient Beam attenuation coefficient

11 Absorption spectra of Chlorophyll Chemical of the week: Chlorophyll Suspended matter reflectance spectra (NASA) Absorption spectra of CDOM (Zhang, 2006) Reflectance spectra over Taihu (Duan, 2010) Fig.3 Typical spectral

12 Table 3 Atmospheric correction algorithm suitable for Water Color RS (Ma et al., 2010) Ocean Color RS Characteristic, inf 0 Algorithm Lake Color RS L L, inf 0 w, near rared wnear, Gordon algorithm for CZCS (Gordon, 1993) Gordon algorithm for SeaWiFs (Gordon and Wang, 1994) Gordon algorithm for MODIS (Gordon and Voss, 1999) rared Principal component analysis (Neumann,1995) Arnone iteration (Arone, 1998) Artificial neural network method (Doerffer and Schiller, 1998) Absorption coefficient iteration in near infrared (Aiken and Moore, 2000) Hu algorithm (Hu, 2000) Ruddick algorithm (Ruddick, 2000, 2001) Fixed remote sensing reflectance algorithm (He and Pan, 2003) Shortwave infrared (SWIR) algorithm Shortwave infrared (SWIR) algorithm (Wang and Shi, 2005)

13 2. Methods Table 4 Data set In situ data Water-leaving radiance spectral (10-18 June 2007) a (31.43 N, E), b (31.27 N, E) c (31.95 N, E), d (31.06 N, E) MODIS-Aqua images 29 March, 19 April, 7 May, 14 May, 8 June 2007 Validation data (From CERN) In situ Chl-a concentration 17 March (13), 15 April (13), May (31), 15 June (13) 2007

14 Fig. 4 Flow chart of SWIR-based atmospheric correction algorithm MODIS L1B data, geo-location data and ancillary data Aerosol scattering of 1240nm and 2130nm bands Negligible g water-leaving radiance for SWIR bands Appropriate aerosol Ångström exponent values Selecting clean pixels Aerosol scattering Rayleigh L f L of other bands Scattering g L w nl w

15 Deriving Chl-a concentration and diffuse attenuation coefficient K d (490) from nl w spectra O Reilly et al., 2000 Wang et al., 2009

16 3. Results 10 1 Lake Taihu in China, June 10, 2007 (a) 10 1 Lake Taihu in China, June 12, 2007 (b) nlw( ) (mw cm -2 m -1 sr -1 ) 10 0 Location: ( o E, o N) In Situ Data MODIS-Aqua nlw( ) (mw cm -2 m -1 sr -1 ) 10 0 Location: ( o E, o N) In Situ Data MODIS-Aqua Wavelength (nm) Wavelength (nm) 10 1 Lake Taihu in China, June 17, 2007 (c) 10 1 Lake Taihu in China, June 18, 2007 (d) r -1 ) ( ) (mw cm -2 m -1 sr nlw( 10 0 Location: ( o E, o N) In Situ Data MODIS-Aqua r -1 ) ( ) (mw cm -2 m -1 sr nlw( 10 0 Location: ( o E, o N) In Situ Data MODIS-Aqua Wavelength (nm) Wavelength (nm) Fig.5 Comparison of normalized water-leaving radiance spectra

17 Importance of nl w (λ) for typical bands nl w (443): representing characteristic of algae absorption, low value corresponds to high algae concentration; nl w (555), nl w (645), nl w (859): representing variation of total suspended sediment (TTS), high value corresponds to high TTS concentration ; K d (490): water diffuse attenuation coefficient at 490nm, low value corresponds to high-clarity waters.

19 Fig.6 MODIS-Aqua monitoring for 2007 algae bloom in Taihu

20 Fig.7 Sampling locations distribution in Taihu

21 Fig.8 Chl-a concentration comparison between MODSI retrieval and in situ observation (monthly)

23 Fig.9 Chl-a concentration comparison between MODSI retrieval and in situ observation (every location)

27 Fig. 10 (a) MODIS derived time series (29 March to 8 June 2007) for Chl-a and nl w (443), (b) nl w (λ) spectral on 7 May 2007 for algae contaminated and non- contaminated waters.

28 Fig. 11 MODSI-derived ( ) monthly climatology nl w (443)

29 Fig. 12 MODSI-derived ( ) monthly climatology nl w (555)

30 Fig. 13 MODSI-derived ( ) monthly climatology nl w (645)

31 Fig. 14 MODSI-derived ( ) monthly climatology nl w (859)

32 Fig. 15 MODSI-derived ( ) monthly climatology K d (490)

33 Fig. 16 MODSI-derived K d (490) for central lake, Gonghu Bay and Meiliangwan Bay

34 Fig 17 Seasonal variability of MODSI derived K (490) for the central lake Fig. 17 Seasonal variability of MODSI-derived K d (490) for the central lake, Gonghu Bay and Meiliangwan Bay

35 Fig. 18 MODSI-derived d climatology l water property

36 Main conclusions Modified SWIR-based atmospheric correction algorithm is suitable for highly turbid inland Lake Taihu; Algae usually appears in the bay regions (Meiliangwan Bay, Gonghu Bay and Zhushan Bay), and high Chl-a concentration were often observed during late spring to early summer; Waters in Taihu are consistently highly turbid all year around, TTS concentration, driven by wind is high in winter-spring seasons and low in summer-fall seasons; Inland freshwater optical and biological properties, as well as water quality can be monitored and evaluated quantitatively by RS measurements (MODIS).

Reflected short-wave irradiance (MLW) Similar il incident id tirradiance Albedo (DPK)> Albedo (MLW)

37 4. Discussion Hypothesis for daily mean water surface albedo analysis WS DPK >WS MLW Difference Similarity Similar atmospheric conditions TTS DPK >TTS MLW Reflected short-wave irradiance (DPK) > Reflected short-wave irradiance (MLW) Similar il incident id tirradiance Albedo (DPK)> Albedo (MLW) The spatial pattern of daily mean albedo may be similar to TTS distribution pattern p p y y p on local scale.

38 Difficulties for validation of lake color RS Reflection of bottom sediment in optical shallow regions; Asynchronism between RS measurement and in situ observation; Aerosol changing and fast variation of water color driven by wind; Spatial non-uniformity of water property in Lake Taihu.

39 Wang, 2011

40 Look forward to your suggestions. Thank you!

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University Ocean Color: Spectral Visible Radiometry Color of the ocean contains latent information on the water qualitycdom,