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

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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.

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Authors and Affiliations

  1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China, 130012

    Kaishan Song, Zongming Wang, Dianwei Liu, Bai Zhang, Jia Du & Yuandong Wang

  2. Department of Earth Sciences, Indiana University–Purdue University, Indianapolis, IN, 46246, USA

    Kaishan Song, Lin Li, Linhai Li & Shuai Li

  3. Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, P.O. Box 9718, China, 100101

    Jingping Xu

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  1. Kaishan Song
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Song, K., Li, L., Wang, Z. et al. Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources. Environ Monit Assess 184, 1449–1470 (2012). https://doi.org/10.1007/s10661-011-2053-3

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