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Remote Estimation of the Chlorophyll-a Concentration in Lake Dianshan, China Using High-Spatial-Resolution Satellite Imagery

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Abstract

The high spatial resolution of satellite data and the capability of physics-based approaches are considered highly suitable for testing the integration of remote sensing technologies into the water quality monitoring of small and medium-sized inland lakes. This research thus aimed to investigate an operational algorithm for chlorophyll-a (Chla) estimation based on China’s recently launched high-spatial-resolution GF-1 satellite data for Lake Dianshan, a eutrophic lake in Shanghai city, eastern China. For the calibration of the empirical model, an enhanced three-band model and an improved four-band model (IFB) developed by model derivation and statistical analysis based on in situ water sampling and satellite reflectance data were proposed. The IFB model could account for more than 90% of the Chla variation in the GF-1 satellite data. For the calibration of the semi-empirical model, the performance of ΔΦ and an improved NCI model (NCI’) were analyzed and validated with field spectral measurements and GF-1 satellite data. The corresponding GF-1 satellite ΔΦ model and NCI’ model reached high estimation accuracies of R2 = 0.80 and 0.76, respectively. The good estimated results indicate that the established GF-1 satellite models are promising and applicable to estimating Chla in small and medium-sized eutrophic inland lakes.

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Acknowledgements

This work was jointly funded by Natural Science Foundation of Shanghai (Grant No. 15ZR1404000), Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR (KFKT-2022-06), and National Natural Science Foundation of China (No. 41001234). We thank Huai Hongyan from the Shanghai environmental monitoring center for providing in situ data.

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Authors and Affiliations

  1. Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China

    Xiaoman Lu, Chunyan Situ, Jiajia Wang, Liguo Zhou & Weichun Ma

  2. Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai, 200063, China

    Xiaoman Lu & Liguo Zhou

  3. Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai, 200062, China

    Liguo Zhou

  4. Department of Geography, California State University, Long Beach, CA, 90840, USA

    Linna Li

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  1. Xiaoman Lu
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Correspondence to Liguo Zhou.

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Lu, X., Situ, C., Wang, J. et al. Remote Estimation of the Chlorophyll-a Concentration in Lake Dianshan, China Using High-Spatial-Resolution Satellite Imagery. J Indian Soc Remote Sens 50, 2465–2477 (2022). https://doi.org/10.1007/s12524-022-01614-8

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