Remotely sensed assessment of water quality levels in the Pearl River Estuary, China.

In this paper, a method of assessing water quality from satellite data is introduced. The composite pollution index (CPI) was calculated from measured chemical oxygen demand (COD) and nutrient concentration. The relationships between CPI and 240 band combinations of SeaWiFS water-leaving radiance were analyzed and the optimal band combination for estimating CPI was chosen from the 240 band combinations. An algorithm for retrieval of CPI was developed using the optimal band combination, (L(443)xL(510))/(L(412)+L(490)). The CPI was estimated from atmospherically corrected SeaWiFS data by employing the algorithm. Furthermore, the CPI value range for each water quality level was determined based on data obtained from 850 samples taken in the Pearl River Estuary. The remotely sensed CPIs were then transferred to water quality levels and appropriate maps were derived. The remotely sensed water quality level maps displayed a similar distribution of levels based on in situ investigation issued by the State Ocean Administration, China. This study demonstrates that remote sensing can play an important role in water quality assessment.

[1]  Anatoly A. Gitelson,et al.  Real-time quality monitoring by remote sensing of contaminated water-bodies: Waste stabilization pond effluent , 1996 .

[2]  C. Giardino,et al.  Validation of satellite data for quality assurance in lake monitoring applications. , 2001, The Science of the total environment.

[3]  William M. Lewis,et al.  Determination of chlorophyll and dissolved organic carbon from reflectance data for Colorado reservoirs , 1996 .

[4]  K. Yin.  Influence of monsoons and oceanographic processes on red tides in Hong Kong waters , 2003 .

[5]  S. Tassan.  Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. , 1994, Applied optics.

[6]  Roland Doerffer,et al.  Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods , 1994 .

[7]  Yunpeng Wang,et al.  Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data. , 2004, The Science of the total environment.

[8]  C. Giardino,et al.  Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery. , 2001, The Science of the total environment.

[9]  P. Lavery,et al.  WATER QUALITY MONITORING IN ESTUARINE WATERS USING THE LANDSAT THEMATIC MAPPER , 1993 .

[10]  Ping Shi,et al.  Atmospheric correction of SeaWiFS imagery for turbid waters in Southern China coastal areas , 2003, SPIE Asia-Pacific Remote Sensing.

[11]  D. Pierson,et al.  Mapping of the water quality of Lake Erken, Sweden, from imaging spectrometry and Landsat Thematic Mapper. , 2001, The Science of the total environment.

[12]  Feizhou Lai,et al.  Time Series Analysis of Water Quality Data in Pearl River, China , 1989 .

[13]  Chuqun Chen,et al.  A local algorithm for estimation of yellow substance (gelbstoff) in coastal waters from SeaWiFS data: Pearl River estuary, China , 2003 .

[14]  J. Brock,et al.  Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL , 2004 .

[15]  J. Johnson,et al.  Use of satellite imagery to estimate surface chlorophyll a and Secchi disc depth of Bull Shoals Reservoir, Arkansas, USA , 1999 .

[16]  P. Lavery,et al.  Estimates of water quality in coastal waters using multi-date Landsat Thematic Mapper data , 1994 .