35 years of Landsat and Sentinel-2-derived water color and clarity in Baiyangdian Lake: Spatiotemporal variations and driving factors
7 affiliations
- 1Center for Geo-Spatial Information, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- 2The Ninth Geological Brigade of Hebei Geological and Mineral Exploration and Development Bureau, Xingtai, 054000, China
- 3International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
- 4State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; Jilin Provincial Key Laboratory of Remote Sensing Big Data for Ecology and Environment Research, Changchun, 130102, China
- 5College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
- 6Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094, China; Key Laboratory of Satellite Remote Sensing, Ministry of Ecology and Environment, Beijing, 100094, China
- 7Liaoning No. 4 Geological Brigade CO., Ltd, Fuxin, 123000, China
Abstract
Baiyangdian Lake (BYD) is a critical ecological component of the Xiong'an New Area, maintaining regional ecosystem stability and biodiversity in the Beijing-Tianjin-Hebei metropolitan region. Prolonged intensive human activities have reduced lake inflows and caused severe water pollution, biodiversity loss, and degradation of ecosystem functions. Landsat and Sentinel-2 data from 1988 to 2023 were employed to track water clarity (Secchi disk depth, Zsd) and water color (Forel-Ule Index, FUI), enabling multi-dimensional analysis of spatiotemporal variations and associated driving factors. The results reveal spatial heterogeneity, with higher turbidity in peripheral zones possibly attributed to sediment and nutrient inputs from inflowing rivers. Seasonally, BYD tended to exhibit clearer in summer and autumn, while higher turbidity occurred in spring, likely driven by ecological water replenishment and precipitation. At the interannual scale, the water quality fluctuated but tended to improve particularly after 2014, which may be attributed to the impact of governance and restoration measures. And water quality may occur alongside climate, land use, landscape and water replenishment. Furthermore, land use and landscape patterns surrounding BYD are identified as significant driving factors of water quality variations. By integrating long-term remote sensing observations, this study quantitatively elucidates the spatiotemporal variations of water quality and its response to these driving factors. Furthermore, the lagged effect of water quality to post-2017 was assessed. The findings provide scientific support for optimizing field monitoring strategies, sustained water quality monitoring and ecological restoration in BYD.
Related
- From Physiological Variations to Institutional Barriers: A Qualitative Inquiry into the Academic and Professional Life Cycles of Color-Vision Deficient Chemistry Students
- SMART SENTINEL: Nondestructive Point-of-Need CRISPR-Cas12a Lateral Flow Platform for Rapid New World Screwworm Field Triage
- Development of a Deep Reinforcement Learning Approach for Transition State Search in Molecular Systems: A Transferable Model Across Structural and Dimensional Variations
- Space‐Based Observation of Decadal Variations of Ammonium Sulfate Aerosols Over Northern China
- Ankle arthrodesis and total ankle replacement – Variations in surgical practice across England using Hospital Episode Statistics database
BibTeX
@article{zhao2026years,
title = {{35 years of Landsat and Sentinel-2-derived water color and clarity in Baiyangdian Lake: Spatiotemporal variations and driving factors}},
author = {Yelong Zhao and Hongzhong Li and Chunying Ren and Xiaoda Xing and Deepakrishna Somasundaram and Zongming Wang and Mingming Jia and Linan Guo and Wenjie Yin and Longlong Zhao and Xiaoli Li and Hongkun Zhu},
journal = {Environmental and Sustainability Indicators},
year = {2026},
month = nov,
doi = {10.1016/j.indic.2026.101590},
url = {https://doi.org/10.1016/j.indic.2026.101590},
note = {CC BY},
}
RIS
TY - JOUR
TI - 35 years of Landsat and Sentinel-2-derived water color and clarity in Baiyangdian Lake: Spatiotemporal variations and driving factors
AU - Yelong Zhao
AU - Hongzhong Li
AU - Chunying Ren
AU - Xiaoda Xing
AU - Deepakrishna Somasundaram
AU - Zongming Wang
AU - Mingming Jia
AU - Linan Guo
AU - Wenjie Yin
AU - Longlong Zhao
AU - Xiaoli Li
AU - Hongkun Zhu
PY - 2026
DA - 2026/11/04
JO - Environmental and Sustainability Indicators
DO - 10.1016/j.indic.2026.101590
UR - https://doi.org/10.1016/j.indic.2026.101590
AB - Baiyangdian Lake (BYD) is a critical ecological component of the Xiong'an New Area, maintaining regional ecosystem stability and biodiversity in the Beijing-Tianjin-Hebei metropolitan region. Prolonged intensive human activities have reduced lake inflows and caused severe water pollution, biodiversity loss, and degradation of ecosystem functions. Landsat and Sentinel-2 data from 1988 to 2023 were employed to track water clarity (Secchi disk depth, Zsd) and water color (Forel-Ule Index, FUI), enabling multi-dimensional analysis of spatiotemporal variations and associated driving factors. The results reveal spatial heterogeneity, with higher turbidity in peripheral zones possibly attributed to sediment and nutrient inputs from inflowing rivers. Seasonally, BYD tended to exhibit clearer in summer and autumn, while higher turbidity occurred in spring, likely driven by ecological water replenishment and precipitation. At the interannual scale, the water quality fluctuated but tended to improve particularly after 2014, which may be attributed to the impact of governance and restoration measures. And water quality may occur alongside climate, land use, landscape and water replenishment. Furthermore, land use and landscape patterns surrounding BYD are identified as significant driving factors of water quality variations. By integrating long-term remote sensing observations, this study quantitatively elucidates the spatiotemporal variations of water quality and its response to these driving factors. Furthermore, the lagged effect of water quality to post-2017 was assessed. The findings provide scientific support for optimizing field monitoring strategies, sustained water quality monitoring and ecological restoration in BYD.
SN - 2665-9727
ER -