Comparison of Chlorophyll Algorithms in the Bohai Sea of China

다국어 초록 (Multilingual Abstract)

Empirical band-ratio algorithms and artificial neural network techniques to retrieve sea surface chlorophyll concentrations were evaluated in the Bohai Sea of China by using an extensive field observation data set. Bohai Sea represents an example of optically complex case II waters with high concentrations of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities and chlorophyll a concentration (Chl), which were taken on 8 cruises between 2003 and 2005. The data covers a range of variability in Chl in surface waters from 0.3 to 6.5 mg m-3. The comparison results showed that these empirical algorithms developed for case I and case II waters can not be applied directly to the Bohai Sea of China, because of significant biases. For example, the mean normalized bias (MNB) for OC4V4 product was 1.85 and the root mean square (RMS) error is 2.26.

참고문헌 (Reference)

1 "The statistic inversion algorithms of water constituents for the Huanghai Sea and the East China Sea" 23 (23): 617-626, 2004

2 "The heritage of SeaWiFS A retrospective on the CZCS NIMBUS Experiment Team" 1994

3 "The calibration and validation of SeaWiFS data" 45 : 427-465, 2000

4 "Study on the correlation between chlorophyll maximum and remote sensing data" 5 (5): 213-218, 2006

5 "Remote-sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements" 2963 : 160-166, 1996

6 "Remote sensing of ocean colour in coastal, and other optically-complex, waters" Dartmouth, Nova Scotia, IOCCG Project Office 3 : 140-, 2000

7 "Preliminary study on distribution of deep chlorophyll maximum and remote sensing model in the Bohai Sea of China" 28 (28): 2599-2612, 2007

8 "Practical Neural Network Recipes in C ++" 1993

9 "Optical properties and remote sensing of inland and coastal waters" CRC Press, Boca Raton 1995

10 "Ocean color chlorophyll a algorithms for SeaWIFS" 103 (103): 24937-24953, 1998

11 "Ocean Color Chlorophyll a Algorithms for SeaWiFS, OC2 and OC4: Version 4" NASA Goddard Space Flight Centre, Greenbelt, Maryland 11 : 2000

12 "Neural network for emulation of an inverse model operational derivation of Case II water properties from MERIS data" 30 : 1735-1746, 1999

13 "Minimum requirements for an operational ocean-colour sensor for the open ocean" Dartmouth, Nova Scotia, IOCCG Project Office 1 : 46-, 1998

14 "MODIS Algorithm Theoretical Basis Document, Bio- Optical lgorithmsCase 1 Waters, version 1.2" 1997

15 "Fundamentals of neural networks" Prentice Hall 1994

16 "Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters" 108 : 3286-3298, 2003

17 "Estimating oceanic chlorophyll concentrations with neural networks" 20 : 189-194, 1999

18 "Artificial neural networks for modeling the transfer function between reflectance and phytoplankton pigment concentration" 106 : 3483-3495, 2000

19 "An overview of MODIS capabilities for ocean science observations" 36 : 1250-1265, 1998

20 "An evaluation of MODIS and SeaWIFS bio-optical algorithms in the Baltic Sea" 89 : 326-350, 2004

21 "Advanced Algorithms for Neural networks: A C++ Sourcebook" Wiley, NewYork 1995

22 "Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance" 89 : 361-368, 2004

23 "A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery" 66 (66): 153-165, 1998

24 "A neural network model for estimating Surface Chlorophyll and Sediment Content at the Lake Kasumi Gaura of Japan" 2000

25 "A neural network as a nonlinear transfer model for retrieving surface wind speeds from the special sensor microwave imager" 100 : 11033-11045, 1995