References
- Jensen JR. Remote sensing of the environment: An earth resource perspective. 2nd ed. Univ. of South Carolina: Pearson Prentice Hall; 2007. p. 409-440.
- 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. https://doi.org/10.1016/S0034-4257(03)00049-X
- Gregg WW, Casey NW. Global and regional evaluation of the seaWiFS Chlorophyll data set. Remote Sens. Environ. 2004;93:463-479 https://doi.org/10.1016/j.rse.2003.12.012
- 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. https://doi.org/10.1016/j.rse.2005.05.004
- 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. https://doi.org/10.1080/01431161003645840
- 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
- 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.
- 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. https://doi.org/10.1016/j.csr.2010.08.016
- 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.
- 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. https://doi.org/10.1016/j.rse.2004.08.007
- Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2:359-366. https://doi.org/10.1016/0893-6080(89)90020-8
- Parlos AG, Chong KT, Atiya AF. Application of the recurrent multilayer perceptron in modeling complex process dynamics. Neural Netw. 1994;5:255-266. https://doi.org/10.1109/72.279189
- Lippmann RP. An introduction to computing with neural networks. IEEE ASSP Magazine 1987;4-22.
- Looney CG. Pattern recognition using neural networks: Theory and algorithms for engineers and scientists. Oxford: Oxford Univ. Press.; 1997.
- Holyer R, Sandidge J. Coastal bathymetry from hyperspectral observation of water radiance. Appl. Optics 1998;65:341-345.
- 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.
- 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. https://doi.org/10.1016/j.rse.2005.05.016
- Doerffer R, Schiller H. The MERIS Case 2 water algorithm. Int. J. Remote Sens. 2007;28:517-535. https://doi.org/10.1080/01431160600821127
- Gholamalifard M. Satellite monitoring of optically active components of Caspian Sea by inverse modeling of radiative transfer equation [dissertation]. Tehran: Tarbiat Modares Univ.; 2013.
- Martin S. An introduction to ocean remote sensing. Cambridge:Cambridge Univ. Press; 2004. p.426.
- Hagan MT, Menhaj MB. Training feedforward networks with the marquardt algorithm. IEEE Trans. Neural Netw. 1994;5:989-993. https://doi.org/10.1109/72.329697
Cited by
- A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective vol.13, pp.21, 2021, https://doi.org/10.3390/rs13214347