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Retrieval of CDOM and DOC Using In Situ Hyperspectral Data: A Case Study for Potable Waters in Northeast China

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Abstract

Chromophoric dissolved organic matter (CDOM), the light absorbing fraction of dissolved organic carbon (DOC), together with phytoplankton and total suspended matter are the main optically active components could be retrieved by remote sensing data. Generally, different composition of DOC and CDOM corresponds to different water surface reflectance. Absorption properties of CDOM and retrieval models for CDOM and DOC were investigated with data from potable reservoirs located in the central of Jilin Province. Water sampling field surveys were conducted on 15, 16 and 19 of September 2012 across the Shitoukoumen, Erlonghu and Xilicheng reservoirs, respectively. Both empirical regression (single band model and band ratio model) and partial least squares coupled with back-propagation artificial neural models (PLSBPNN) were established to estimate CDOM absorption coefficient at 355 nm [aCDOM(355)] and DOC concentration with in situ measured remote sensing reflectance. It was found that the band ratio models and PLSBPNN model performed well for estimating DOC concentration while the band ratio models yielded the best result in retrieval CDOM. Moreover, all the three models performed better on the DOC concentration estimation than the performance on aCDOM(355). Band ratio models outperformed (R 2 = 0.55) other models for estimating CDOM absorption coefficient, while PLSBPNN model outperformed other models with respect to DOC estimation (R 2 = 0.93). High spectral slope values indicated that CDOM in the potable waters primarily comprised low molecular weight organic substances; while sources of DOC were mainly coming from exogenous input, which was the main reason lead to the difference of model performances on DOC and aCDOM(355) estimation. The algorithms developed in this study is needed to be tested and refined with more in situ spectral data, also future work is still needed to be undertaken for characterizing the dynamic of the potable water quality with remotely sensed imagery.

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Acknowledgments

This research was jointly supported by the National Natural Science Foundation of China (No. 41471290) and the ‘One Hundred Talents Project’ of Chinese Academy of Sciences, China (granted to Dr. Kaishan Song). We would like to thank S. Lv, Y. Guan, and L. Liu for their capable assistance in the field and laboratory. We are grateful for the constructive comments from anonymous reviewers that have strengthened the manuscript.

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Authors and Affiliations

  1. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

    Tiantian Shao, Kaishan Song, Jia Du, Ying Zhao & Bai Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Tiantian Shao

  3. School of Geographical Sciences, Northeast Normal University, Changchun, China

    Zhiming Liu

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  1. Tiantian Shao
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Correspondence to Kaishan Song.

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Shao, T., Song, K., Du, J. et al. Retrieval of CDOM and DOC Using In Situ Hyperspectral Data: A Case Study for Potable Waters in Northeast China. J Indian Soc Remote Sens 44, 77–89 (2016). https://doi.org/10.1007/s12524-015-0464-2

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