气候变化领域集成服务门户(CCPortal): Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

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DOI	10.3389/fenvs.2023.1335725
	Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin
	Shi, Kaifang; Han, Jing-Cheng; Wang, Peng
发表日期	2024
EISSN	2296-665X
起始页码	11
卷号	11
英文摘要	Lake Surface Water Temperature (LSWT) is essential for understanding and regulating various processes in lake ecosystems. Remote sensing for large-scale aquatic monitoring offers valuable insights, but its limitations call for a dynamic LSWT monitoring model. This study developed multiple machine learning models for LSWT retrieval of four representative freshwater lakes in the Yangtze River Basin using Himawari-8 (H8) remote sensing imagery and in-situ data. Based on the in situ monitoring dataset in Lake Chaohu, the dynamic LSWT retrieval models were effectively configured and validated to perform H8-based remote sensing inversion. The test results showed that six models provided satisfactory LSWT retrievals, with the Back Propagation (BP) neural network model achieving the highest accuracy with an R-squared (R2) value of 0.907, a Root Mean Square Error (RMSE) of 2.52 degrees C, and a Mean Absolute Error (MAE) of 1.68 degrees C. Furthermore, this model exhibited universality, performing well in other lakes within the Yangtze River Basin, including Taihu, Datonghu and Dongtinghu. The ability to derive robust LSWT estimates confirms the feasibility of real-time LSWT retrieval using synchronous satellites, offering a more efficient and accurate approach for LSWT monitoring in the Yangtze River Basin. Thus, this proposed model would serve as a valuable tool to support the implementation of more informed policies for aquatic environmental conservation and sustainable water resource management, addressing challenges such as climate change, water pollution, and ecosystem restoration.
英文关键词	water quality; remote sensing retrieval; synchronous Satellite; LSWT; machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001144542900001
来源期刊	FRONTIERS IN ENVIRONMENTAL SCIENCE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298297
作者单位	Qinghai University; Shenzhen University; Shandong Agricultural University
推荐引用方式 GB/T 7714	Shi, Kaifang,Han, Jing-Cheng,Wang, Peng. Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin[J],2024,11.
APA	Shi, Kaifang,Han, Jing-Cheng,&Wang, Peng.(2024).Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin.FRONTIERS IN ENVIRONMENTAL SCIENCE,11.
MLA	Shi, Kaifang,et al."Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin".FRONTIERS IN ENVIRONMENTAL SCIENCE 11(2024).