Estimating chlorophyll-A concentration in the Caspian Sea from MODIS images using artificial neural networks |
Boudaghpour, Siamak
(Civil Engineering Department, K.N.Toosi Technical University)
Moghadam, Hajar Sadat Alizadeh (Civil Engineering Department, K.N.Toosi Technical University) Hajbabaie, Mohammadreza (Environmental Engineering Department, K.N.Toosi Technical University) Toliati, Seyed Hamidreza (Chemical Engineering Department, University of Tehran) |
1 | Hu C, Chen Z, Clayton T, Swarzenski P, Brock J, Muller-Karager F. Assessment of estuarine water-quality indicators using MODIS medium Resolution bands: Initial results from Tampa Bay. Remote Sens. Environ. 2004;93:423-441. DOI |
2 | Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2:359-366. DOI |
3 | Parlos AG, Chong KT, Atiya AF. Application of the recurrent multilayer perceptron in modeling complex process dynamics. Neural Netw. 1994;5:255-266. DOI |
4 | Lippmann RP. An introduction to computing with neural networks. IEEE ASSP Magazine 1987;4-22. |
5 | Looney CG. Pattern recognition using neural networks: Theory and algorithms for engineers and scientists. Oxford: Oxford Univ. Press.; 1997. |
6 | Holyer R, Sandidge J. Coastal bathymetry from hyperspectral observation of water radiance. Appl. Optics 1998;65:341-345. |
7 | Lee Z, Zhang M, Carder K, Hall L. A neural network approach to deriving optical properties and depths of shallow waters. In Proceedings, Ocean Optics XIV, SG Ackleson, J. Campbell, Eds. Washington D.C.: Office of Naval Research; 1998. |
8 | Kishino M, Tanaka A, Ishizaka J. Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data. Remote Sens. Environ. 2005;99:66-74. DOI |
9 | Doerffer R, Schiller H. The MERIS Case 2 water algorithm. Int. J. Remote Sens. 2007;28:517-535. DOI |
10 | Gholamalifard M. Satellite monitoring of optically active components of Caspian Sea by inverse modeling of radiative transfer equation [dissertation]. Tehran: Tarbiat Modares Univ.; 2013. |
11 | Martin S. An introduction to ocean remote sensing. Cambridge:Cambridge Univ. Press; 2004. p.426. |
12 | Hagan MT, Menhaj MB. Training feedforward networks with the marquardt algorithm. IEEE Trans. Neural Netw. 1994;5:989-993. DOI |
13 | Allan MG, Hamilton DP, Hicks BJ, Brabyn L. Landsat remote sensing of chlorophyll a concentration in central North Island lakes of New Zealand. Int. J. Remote Sens. 2011;32:2037-2055. DOI |
14 | O'Reilly JE. Ocean Color Chlorophyll Algorithms for SeaWiFS, OC2, and OC4: Version 4. In: Hooker SB, Firestone ER Eds. Sea WiFS Postlaunch Calibration and Validation Analyses, Part 3. Washington D.C.: NASA Technical Memorandum; 2000. p. 9-11 |
15 | Carder K, Chen F, Lee Z, Hawes S, Cannizzaro J. Modis ocean science team algorithm theoretical basis document, Case 2 Chlorophyll a [Internet]. Available From: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod19.pdf. |
16 | Jensen JR. Remote sensing of the environment: An earth resource perspective. 2nd ed. Univ. of South Carolina: Pearson Prentice Hall; 2007. p. 409-440. |
17 | Tang D, Kawamura H, Lee M, Dien TV. Seasonal and Spatial Distribution of Chlorophyll a Concentrations and Water Conditions in the Gulf of Tonkin, South China Sea. Remote Sens. Environ. 2003;85:475-483. DOI |
18 | Miles TN, He R. Temporal and spatial variability of Chl-a and SST on the South Atlantic Bight: Revisiting with cloud-free reconstructions of MODIS satellite imagery. Cont. Shelf Res. 2010;30:1951-1962. DOI |
19 | Gregg WW, Casey NW. Global and regional evaluation of the seaWiFS Chlorophyll data set. Remote Sens. Environ. 2004;93:463-479 DOI |
20 | Pinkerton MH, Richardson KM, Boyd PW, et al. Intercomparison of ocean colour band-ratio algorithms for Chlorophyll concentration in the subtropical front East of New Zealand. Remote Sens. Environ. 2005;97:382-402. DOI |
21 | Kavak MT, Karadogan S. The relationship between sea surface temperature and chlorophyll concentration of phytoplankton in the Black Sea using remote sensing techniques. J. Environ. Biol. 2012;32:493-498. |