Shuo Wang, Shijun Wu, Canjun Yang. The pressure compensation technology of deep-sea sampling based on the real gas state equation[J]. Acta Oceanologica Sinica, 2020, 39(8): 88-95. doi: 10.1007/s13131-020-1637-6
Citation: MAO Zhihua, ZHU Qiankun, GONG Fang, PAN Delu. The algorithms of chlorophyll-a concentration for CMODIS[J]. Acta Oceanologica Sinica, 2007, (5): 25-33.

The algorithms of chlorophyll-a concentration for CMODIS

  • Received Date: 2005-04-15
  • Rev Recd Date: 2007-02-08
  • The algorithms of extracting chlorophyll-a (Chl-a) concentration have been established for Chinese moderate resolution imaging spectrometer (CMODIS) mounted on Shenzhou-3 spaceship launched on 25 March 2002.The CMODIS is an ocean color sensor with 30 visible channels and 4 infrared channels, much different from other ocean color satellites and needs new algorithms to process data.Three models of Chl-a concentration were established based on Chl-a data retrieved from sea-viewing wide field-of-view sensor (SeaWiFS), with the average relative errors of 26.6%, 24%.0% and 33.5%, respectively.This practical and economic approach can be used for developing the algorithms of Chinese ocean color and temperature sensor (COCTS) on the satellite Haiyang-1 to derive the Chl-a concentration concentration distribution.The applicability of the algorithms was analyzed using some in situ measurements.Suspended sediment is the main factor influencing the accuracy of the spectral ratio algorithms of Chl-a concentration.The algorithms are suitable to using in the regions where suspended sediment concentrations (SSC) are less than 5 g/m3 under the condition of relative error of Chl-a concentration retrieval within 35%.High concentration of suspended sediment leads to the overestimate remote sensing retrieval of concentration of Chl-a, while low-middle SSCs lead to the low Chl-a concentration values using the spectral ratio algorithms.Since the accuracy of Chl-a concentration by the spectral ratio algorithms is limited to waters of Case 2, it is necessary to develop semi-analytical models to improve the performance of satellite ocean color remote sensing in turbid coastal waters.
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