当前位置: X-MOL 学术Int. J. Appl. Earth Obs. Geoinf. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Trophic state assessment of optically diverse lakes using Sentinel-3-derived trophic level index
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-30 , DOI: 10.1016/j.jag.2022.103026
Hui Liu, Baoyin He, Yadong Zhou, Tiit Kutser, Kaire Toming, Qi Feng, Xiaoqin Yang, Congju Fu, Fan Yang, Wen Li, Feng Peng

An accurate estimation of trophic state of lakes with satellite remote sensing is a challenge due to the optical complexity and variability associated with inland waters. Match-up data from 393 sampling stations that has concurrent Sentinel-3 OLCI images were acquired across Wuhan lakes. Trophic Level Index (TLI) algorithms were developed within a global Optical Water Type (OWT) classification system. The performance of algorithms with limited training data gathered by using spectral similarity of highest Sowt was not improved compared with that on basis of no classification. In contrast, using spectral similarity of Sowt > 0.9 rather than the highest Sowt to group more training data with similar traits for each OWT can help build more robust algorithms, which performance is better than that on basis of no classification. Algorithm performance statistics of the test dataset for the stepwise multiple linear regression (SMLR) method were the following: Mean Absolute Error (MAE) = 5.56; Mean Absolute Percentage Error (MAPE) = 11.02 %; Root Mean Square Error (RMSE) = 7.24 and for the back propagation neural network on the basis of the Levenberg-Marquardt-Bayesian regularization algorithm (LMBR-BPNN) method MAE = 4.56; MAPE = 8.33 %; RMSE = 5.98. We detected 8 different OWTs (2,3,4,5,9,10,11,12) in Wuhan lakes and clear spatio-temporal patterns of the trophic state between 2018 and 2020.Our results revealed that the trophic state of Wuhan lakes did not decrease as expected during the COVID-19 lockdown period.



中文翻译:

使用 Sentinel-3 衍生的营养水平指数对光学多样化湖泊的营养状态进行评估

由于与内陆水域相关的光学复杂性和可变性,利用卫星遥感准确估计湖泊的营养状态是一项挑战。在武汉的湖泊中获取了来自 393 个采样站的匹配数据,这些采样站具有并发的 Sentinel-3 OLCI 图像。营养水平指数 (TLI) 算法是在全球光学水类型 (OWT) 分类系统中开发的。通过使用最高光谱相似性收集的有限训练数据的算法的性能小号奥特与没有分类的基础上相比没有改善。相反,使用光谱相似性小号奥特 > 0.9 而不是最高小号奥特为每个OWT分组更多具有相似特征的训练数据可以帮助构建更稳健的算法,其性能优于没有分类的算法。逐步多元线性回归 (SMLR) 方法的测试数据集的算法性能统计如下:平均绝对误差 (MAE) = 5.56;平均绝对百分比误差 (MAPE) = 11.02 %;均方根误差 (RMSE) = 7.24,对于基于 Levenberg-Marquardt-Bayesian 正则化算法 (LMBR-BPNN) 方法的反向传播神经网络 MAE = 4.56;MAPE = 8.33 %;RMSE = 5.98。我们在武汉湖泊中检测到 8 种不同的 OWT(2、3、4、5、9、10、11、12),并在 2018 年至 2020 年间明确了营养状态的时空格局。

更新日期:2022-09-30
down
wechat
bug