title: Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources.
abstract: 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.
authors.affiliation: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China 130012. kaissong@iupui.edu
authors.forename: Kaishan
authors.initials: K
authors.lastname: Song
authors.forename: Lin
authors.initials: L
authors.lastname: Li
authors.forename: Zongming
authors.initials: Z
authors.lastname: Wang
authors.forename: Dianwei
authors.initials: D
authors.lastname: Liu
authors.forename: Bai
authors.initials: B
authors.lastname: Zhang
authors.forename: Jingping
authors.initials: J
authors.lastname: Xu
authors.forename: Jia
authors.initials: J
authors.lastname: Du
authors.forename: Linhai
authors.initials: L
authors.lastname: Li
authors.forename: Shuai
authors.initials: S
authors.lastname: Li
authors.forename: Yuandong
authors.initials: Y
authors.lastname: Wang
mesh_terms: D002734:Chlorophyll / Q000032:analysis*; D000077194:Chlorophyll A; D060766:Drinking Water / Q000737:chemistry*; D004784:Environmental Monitoring / Q000379:methods*; D016571:Neural Networks, Computer; D058998:Remote Sensing Technology; D018531:Spacecraft; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data; D014881:Water Supply / Q000032:analysis / Q000706:statistics & numerical data
chemical_list: D060766:Drinking Water; D014874:Water Pollutants, Chemical; D002734:Chlorophyll; D000077194:Chlorophyll A