Estimating Chlorophyll-a Concentration from Remotely Sensed Data in East Coast of Peninsular Malaysia


Results
Different algorithms were applied to the images. The in-situ data and the measured chlorophyll-a was highly correlated using empirical algorithm using level 1 data with r2 = 0.9472. This algorithm is however could be applied to that particular site. Table 1 shows the Correlation between In-situ chlorophyll-a and measured chlorophyll-a extracted from models. SeaBAM algorithm (NASA, 1997) had been modified to suit with the local condition and produced good correlation with in-situ data r2 = 0.924. Morel model (Morel, 1996) produced r2 = 0.8589 between In-situ chlorophyll-a and measured chlorophyll-a extracted from models.

Table 1: Correlation between In-situ chlorophyll-a and measured
chlorophyll-a extracted from models.
Model  Empirical
(Level 1 Data)		  Empirical
(Level 2 Data)		  Morel
(Level 1 Data)		 Seabam
(Level 1 Data)
R2  0.9472  0.798  0.8589   0.924


Discussion
The results show that the empirical model has significantly highest correlation to the in-situ data. SeaWiFS level 1 data gives correlation of and level 2 data gives correlation of . The ratio between channel 2, channel 3 and channel 5 is a good combination to extract chlorophyll-a from SeaWiFS data. For SeaWiFS data, ratio derived using blue channel (443nm), blue-green channel (490nm) and green channel (555nm) was used to extract the chlorophyll-a concentration from SeaWiFS data.

(a)

(b)

 (C)

(d)
Figure 1(a) (b): Chlorophyll-a concentration map measured using Empirical model level 1 data on 24th August 2000 and 29thAugust 2000. Figure 2(c) (d): Chlorophyll-a concentration map measured using SeaBAM model level 1 data on 24th August 2000 and 29thAugust 2000.


The ratio of ((443 - 555)/490) was used for implementing the empirical algorithm (linear regression) and Morel algorithm. For SeaBAM algorithm, the ratio of log10(443/555) was applied. The Morel and SeaBAM algorithms were modified to suit with the Tropical area. Details discussion can be found in Arnis (2001). Figure 1 (a) (b) and figure 2 (c) (d) show the chlorophyll-a concentration map.

From the map, the highest chlorophyll-a concentrations are found in the coastal waters of Terengganu and decreased to offshore. It can be concluded that a remote sensing technique with suitable extracting chlorophyll-a algorithm offers a useful technique for estimating of chlorophyll-a concentration.

Conclusion
The study proved that the remote sensing technique is a very useful tool for studying the distribution of chlorophyll-a concentration in a large water body area such as the Exclusive Economic Zone. In this work, channel 2, channel 3 and channel 5 of SeaWiFS data have been found to be the most suitable channel to extract the chlorophyll-a concentration from SeaWiFS data. Correlation analysis between remotely sensed data and chlorophyll-a in-situ data has indicated the possibility of mapping chlorophyll-a concentration with some degrees of success. The strong correlation of radiance ratio corresponding to above channel with in-situ data provided the basis for the development of equation and constant for the estimated chlorophyll-a concentration in South China Sea. However, the use of satellite remote sensing for mapping chlorophyll-a concentration in South China Sea is limited by the presence of cloud cover. Despite these advantages, satellite data are preferable to filed measurements if one aim is to follow the temporal of phytoplankton over large area.

References
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  • Mansor, S.B, Tan Chu Knee., and M.I.H Ibrahim. Proceeding of 22nd Asian Conference on Remote Sensing. Satellite fish forecasting in South China Sea (2001).
  • Mayo, M., Gitelson, A., and Yacobi. Y.Z., Ben Avraham.. Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data. International Journal of Remote Sensing. (16) 1: 175-182 (1995).
  • Morel, A. Optical properties of oceanic case 1 water (revisited). Ocean Optic XIII. SPIE. 2963:108- 114 (1996).
  • NASA. Bio-Optical Algorithm Mini-Workshop (1997).
    http://seabass.gsfc.nasa.gov/seabam/bioopt_workshop.html
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