Tracking historical chlorophyll-a change in the guanting reservoir, Northern China, based on landsat series inter-sensor normalization

ABSTRACT

The Guanting Reservoir supplied drinking water to Beijing until 1997, following which the water quality of the reservoir deteriorated. The chlorophyll-a concentration (C_chl-a) of water is an important indicator of eutrophication. Therefore, changes in the C_chl-a of the Guanting Reservoir should be monitored and analysed. For more than 30 years, the monitoring of C_chl-a in inland waterbodies has only been possible using the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI). However, there are data consistency problems in monitoring C_chl-a using these sensors. To address this issue, this study utilized inter-sensor normalization models of the different sensors and a unified C_chl-a estimation model for Landsat series data with a long time span. After inter-sensor normalization, the mean relative error (MRE) of remote sensing reflectance (R_rs) between OLI and TM/ETM+ was corrected to 1–5%. The unified model of C_chl-a estimation employed the normalized ratio index of blue R_B and near-infrared R_NIR remote sensing reflectance: (R_NIR – R_B): (R_NIR + R_B). The MRE for estimating C_chl-a was 25.7% and the root-mean-square error (RMSE) was 5.65 mg m^–3. The C_chl-a of the Guanting Reservoir was then estimated for years between 1985 and 2019. During this time, C_chl-a had a distinct seasonal distribution trend; the annual changes go through four stages, and each period shows different change characteristics. This study is the first to systematically understand the history of chlorophyll-a changes in the Guanting Reservoir over the past 30 years and provides references data for its eutrophication process and management.

Acknowledgements

We would like to thank the National Aeronautics and Space Administration (NASA) for providing Landsat TM/ETM+/OLI data, and the Big Earth Data Platform for Three Poles for providing China meteorological forcing dataset.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the Strategic Priority Research Programme of the Chinese Academy of Sciences (grant number XDA19080304), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (grant number KFJ-STS-ZDTP-077), the Major Projects of High-resolution Earth Observation System (grant number 04-Y30B01-9001-18/20), the Special Project for Scientific Research and Development of Henan Academy of Sciences (grant number 200201019), and the National Natural Science Foundation of China (grant numbers 41701402, 41971318, and 41671203).