Estimates of water quality in coastal waters using multi-date Landsat Thematic Mapper data

Pattiaratchi, Charitha; Lavery, Paul S.; Wyllie, A.; Hick, P.

doi:10.1080/01431169408954192

Cited by 69 publications

(44 citation statements)

References 16 publications

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“…Since Chla concentration is much lower than suspended particles, the latter should primarily determine the water apparent reflectance signature in the red and near-infrared regions (see Table 1). Previous studies have shown high sensitivity of Landsat-TM bands 3(630-690 nm) and 4 (760-900 nm) to suspended matter (Pattiaratchi et al 1994;Giardino et al 2001;Xu et al 2009b). IRS-P6 Fig.…”

Section: Models With Imagery Datamentioning

confidence: 97%

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Song

Wang

et al. 2011

Environ Monit Assess

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The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R (2) = 0.94, 0.91) than their performance on Chl-a concentration (R (2) = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R (2) = 0.91, 0.82) and TSM (R (2) = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R (2) = 0.75 and 0.90 for Chl-a; R (2) = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.

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Section: Models With Imagery Datamentioning

confidence: 97%

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Song

Wang

et al. 2011

Environ Monit Assess

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“…For example, remote sensing methods have been developed for operational large-scale monitoring of chlorophyll concentration and suspended materials (Kloiber et al 2000, Harma et al 2001, Wiangwang et al 2006. Several studies have shown that imagery from Landsat sensors can be used in the estimation of Secchi depth (SD), chlorophyll-a (Chl-a) concentration, turbidity and total suspended solids (Tassan 1987, Harrington et al 1992, Dekker and Peters 1993, Pattiaratchi et al 1994, Keiner and Yan 1998, Nellis et al 1998, Allee and Johnson 1999, Roelfsema et al 2001. These remote sensing methods were shown to be effective in reducing the experimental cost and labour (Khorram andCheshire 1985, Lathrop 1992).…”

Section: Introductionmentioning

confidence: 99%

Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data

et al. 2010

International Journal of Remote Sensing

114

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“…Chl-a determination through satellite sensors offers several advantages over traditional monitoring techniques. Because of the high altitude of the satellite large area can be monitored including the areas where accessibility is difficult or impossible (Pattiaratchi et al, 1994). Satellite imageries are useful also in studying past trends even when in-situ data are not available (Dahdouh-Guebas, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Most Chl-a retrieval algorithms developed using remote sensing data are for waters where color is mainly due to Chl-a with little amount of suspended particles and dissolved color substances. However, in most coastal and inland waters suspended particles and dissolved substances also contribute to the color and interfere with the spectral signal of Chl-a (Pattiaratchi et al 1994, Dall'Olmo et al2005, Tzortziou et al 2007). …”

Section: Introductionmentioning

confidence: 99%

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Dahanayaka¹,

Perera²,

Wijeyaratne³

et al. 2016

Turk. J. Fish. Aquat. Sci.

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This study was aimed to determine the present level and past trends of eutrophication of the Bolgoda North lake, Sri Lanka using in situ Chlorophyll-a (Chl-a) measurements and remote sensing data obtained from Advanced Space-borne Thermal Emission and Reflectance Radiometer (ASTER) satellite data. From March to October 2013, Chl-a, nitrate and phosphate contents of the lake were measured once a month on the days of ASTER overpass and using standard laboratory methods. Cloud-free ASTER images of the lake for the 2000-2013 period were atmospherically corrected using Fast Line-ofsight Atmospheric Analysis of Hypercubes (FLAASH) and in-situ Chl-a data were regressed with atmospherically corrected three ASTER Visible and Near Infrared band ratios of the same date. The green/red band ratio, which had the highest coefficient of determination, was used to develop algorithm for generation of 15-m resolution Chl-a distribution maps. Results indicated that eutrophication of this lake has increased from 2008 to 2011. Heavy eutrophic conditions were noted in several regions of the lake in 2013, especially in water stagnant areas and adjacent to freshwater inlets. Unplanned urbanization and inadequate facilities for waste management have resulted in heavy eutrophication of the water body. If the present trends of waste disposal and unplanned urbanization continue, enormous environmental problems would be resulted in future.

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Estimates of water quality in coastal waters using multi-date Landsat Thematic Mapper data

Cited by 69 publications

References 16 publications

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data

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