海河水利Issue(2)：17-21,5.DOI:10.3969/j.issn.1004-7328.2024.02.005
基于集成学习的内陆水体叶绿素a浓度反演
Chlorophyll-a Concentration Retrieval in Inland Water Based on Ensemble Learning
孟黎 1孟静2
作者信息
- 1. 山东城市建设职业学院,山东 济南 250103
- 2. 山东省国土测绘院,山东 济南 250102
- 折叠
摘要
Abstract
Using satellite data to monitor inland or water quality status is of great significance for ecological decision-making.The concentration of Chlorophyll-a(Chla)in Nansi lake,Shandong Province is retrieved by combining two ensemble learning algorithms,based on Sentinel-2 satellite data with high spatiotemporal resolution.The results show that Sentinel-2 data corrected for remote sensing reflectance are more suitable for water quality inversion.The XGBoost model performs optimally on the 5-fold cross-validation inversion results(R2=0.732 5,RMSE=9.168 1 μg/L),making the inversion results more realistic.Therefore,using this model to invert the Chla concentration in the Nansi lake can provide a better understanding of its spatiotemporal variability,and the conclusions of this paper can provide some reference for similar studies in other regions.关键词
哨兵二号数据/南四湖/叶绿素a/集成学习Key words
sentinel-2 data/Nansi Lake/Chlorophyll-a/ensemble learning分类
资源环境引用本文复制引用
孟黎,孟静..基于集成学习的内陆水体叶绿素a浓度反演[J].海河水利,2024,(2):17-21,5.
