干旱区生态环境知识资源中心(Arid): Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region

Arid

DOI	10.1364/OE.520667
	Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region
	Ren, Jianhua; Zhou, Haoyun; Tao, Zui; Ge, Liu; Song, Kaishan; Xu, Shiqi; Li, Yong; Zhang, Lele; Zhang, Xiyu; Li, Sijia
通讯作者	Li, SJ
来源期刊	OPTICS EXPRESS
ISSN	1094-4087
出版年	2024
卷号	32 期号:9 页码:16371-16397
英文摘要	Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional -scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K -Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi -Layer Perception Network (MLP), were developed using our in -situ measured Chl-a. A cross -validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 mu g/L). Adjustments were made to align the Chl-a spatial -temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001236762300007
WOS关键词	INHERENT OPTICAL-PROPERTIES ; SEMIANALYTICAL MODEL ; REMOTE ESTIMATION ; INLAND WATERS ; PHOSPHORUS ; QUALITY ; RED
WOS类目	Optics
WOS研究方向	Optics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405007
推荐引用方式 GB/T 7714	Ren, Jianhua,Zhou, Haoyun,Tao, Zui,et al. Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region[J],2024,32(9):16371-16397.
APA	Ren, Jianhua.,Zhou, Haoyun.,Tao, Zui.,Ge, Liu.,Song, Kaishan.,...&Li, Sijia.(2024).Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.OPTICS EXPRESS,32(9),16371-16397.
MLA	Ren, Jianhua,et al."Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region".OPTICS EXPRESS 32.9(2024):16371-16397.