School of Earth and Space Sciences, Peking University, Beijing, 100871, China; Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the PRC, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China.
College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China; Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing, 100048, China.
Environ Res. 2023 May 15;225:115509. doi: 10.1016/j.envres.2023.115509. Epub 2023 Feb 15.
Eutrophication is one of the major threats to the inland water ecosystem. Satellite remote sensing provides a promising way to monitor trophic state at large spatial scale in an efficient manner. Currently, most satellite-based trophic state evaluation approaches have focused on water quality parameters retrieval (e.g., transparency, chlorophyll-a), based on which trophic state was evaluated. However, the retrieval accuracies of individual parameter do not meet the demand for accurate trophic state evaluation, especially for the turbid inland waters. In this study, we proposed a novel hybrid model to estimate trophic state index (TSI) by integrating multiple spectral indices associated with different eutrophication level based on Sentinel-2 imagery. The TSI estimated by the proposed method agreed well with the in-situ TSI observations, with root mean square error (RMSE) of 6.93 and mean absolute percentage error (MAPE) of 13.77%. Compared with the independent observations from Ministry of Ecology and Environment, the estimated monthly TSI also showed good consistency (RMSE=5.91,MAPE=10.66%). Furthermore, the congruent performance of the proposed method in the 11 sample lakes (RMSE=5.91,MAPE=10.66%) and the 51 ungauged lakes (RMSE=7.16,MAPE=11.56%) indicated the favorable model generalization. The proposed method was then applied to assess the trophic state of 352 permanent lakes and reservoirs across China during the summers of 2016-2021. It showed that 10%, 60%, 28%, and 2% of the lakes/reservoirs are in oligotrophic, mesotrophic, light eutrophic, and middle eutrophic states respectively. Eutrophic waters are concentrated in the Middle-and-Lower Yangtze Plain, the Northeast Plain, and the Yunnan-Guizhou Plateau. Overall, this study improved the trophic state representativeness and revealed trophic state spatial distribution of Chinese inland waters, which has the significant meanings for aquatic environment protection and water resource management.
富营养化是内陆水生态系统的主要威胁之一。卫星遥感提供了一种很有前途的方法，可以有效地在大空间尺度上监测营养状态。目前，大多数基于卫星的营养状态评估方法都集中在水质参数的反演（例如透明度、叶绿素-a）上，根据这些参数来评估营养状态。然而，单个参数的反演精度不能满足准确评估营养状态的要求，特别是对于浑浊的内陆水域。在本研究中，我们提出了一种新的混合模型，通过结合基于 Sentinel-2 图像的与不同富营养化水平相关的多个光谱指数来估计营养状态指数（TSI）。该方法估计的 TSI 与现场 TSI 观测值吻合较好，均方根误差（RMSE）为 6.93，平均绝对百分比误差（MAPE）为 13.77%。与生态环境部的独立观测值相比，估计的月度 TSI 也表现出良好的一致性（RMSE=5.91，MAPE=10.66%）。此外，该方法在 11 个样本湖泊（RMSE=5.91，MAPE=10.66%）和 51 个未测湖泊（RMSE=7.16，MAPE=11.56%）上的一致表现表明了模型具有良好的泛化能力。然后，该方法被应用于评估 2016-2021 年夏季中国 352 个永久性湖泊和水库的营养状态。结果表明，分别有 10%、60%、28%和 2%的湖泊/水库处于贫营养、中营养、轻富营养和中富营养状态。富营养化水体主要集中在长江中下游平原、东北平原和云贵高原。总体而言，本研究提高了中国内陆水域营养状态的代表性，揭示了营养状态的空间分布，这对水环境保护和水资源管理具有重要意义。
