| 第一作者： | Xu, Shiqi |
|---|---|
| 英文第一作者： | Xu, Shiqi |
| 联系作者： | Li, Sijia |
| 英文联系作者： | Li, Sijia |
| 发表年度： | 2022 |
| 卷： | 14 |
| 摘要： | Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 mu g/L, MAE = 0.57 mu g/L, slope = 0.94, and R-2 = 0.98; Validation: N = 40, RMSE = 2.12 mu g/L, MAE = 1.58 mu g/L, slope = 0.91, R-2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion. |
| 刊物名称： | Remote Sensing |
| 参与作者： | Xu, S. Q. Li, S. J. Tao, Z. Song, K. S. Wen, Z. D. Li, Y. Chen, F. F. |
删除或更新信息，请邮件至freekaoyan#163.com(#换成@)
Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images.
本站小编 Free考研考试/2024-01-13
Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images.
相关话题/
领限时大额优惠券,享本站正版考研考试资料!
优惠券领取后72小时内有效，10万种最新考研考试考证类电子打印资料任你选。涵盖全国500余所院校考研专业课、200多种职业资格考试、1100多种经典教材，产品类型包含电子书、题库、全套资料以及视频，无论您是考研复习、考证刷题，还是考前冲刺等，不同类型的产品可满足您学习上的不同需求。 ...考试优惠券 本站小编 Free壹佰分学习网 2022-09-19
