文献类型：期刊文献

英文题名：Remote sensing estimation of δ¹⁵N_PN in the Zhanjiang Bay using Sentinel-3 OLCI data based on machine learning algorithm

作者：Yu, Guo[1,2,3];Zhong, Yafeng[1];Fu, Dongyang[1];Chen, Fajin[3];Chen, Chunqing[2,3]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ, Coll Chem & Environm Sci, Zhanjiang, Peoples R China;[3]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Peoples R China

年份：2024

卷号：11

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001232780200001)、、WOS

基金：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Key Research and Development Program of China (No. 2022YFC3103101); Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (No. GML2021GD0809); National Natural Science Foundation of China (No. 42206187); Key projects of the Guangdong Education Department (No. 2023ZDZX4009).

语种：英文

外文关键词：particulate nitrogen; delta N-15(PN); remote sensing; machine learning algorithm; Sentinel-3; OLCI; Zhanjiang Bay

外文摘要：The particulate nitrogen (PN) isotopic composition (delta N-15(PN)) plays an important role in quantifying the contribution rate of particulate organic matter sources and indicating water environmental pollution. Estimation of delta N-15(PN) from satellite images can provide significant spatiotemporal continuous data for nitrogen cycling and ecological environment governance. Here, in order to fully understand spatiotemporal dynamic of delta N-15(PN), we have developed a machine learning algorithm for retrieving delta N-15(PN). This is a successful case of combining nitrogen isotopes and remote sensing technology. Based on the field observation data of Zhanjiang Bay in May and September 2016, three machine learning retrieval models (Back Propagation Neural Network, Random Forest and Multiple Linear Regression) were constructed using optical indicators composed of in situ remote sensing reflectance as input variable and delta N-15(PN) as output variable. Through comparative analysis, it was found that the Back Propagation Neural Network (BPNN) model had the better retrieval performance. The BPNN model was applied to the quasi-synchronous Ocean and Land Color Imager (OLCI) data onboard Sentinel-3. The determination coefficient (R-2), root mean square error (RMSE) and mean absolute percentage error (MAPE) of satellite-ground matching point data based on the BPNN model were 0.63, 1.63 parts per thousand, and 20.10%, respectively. From the satellite retrieval results, it can be inferred that the retrieval value of delta N-15(PN) had good consistency with the measured value of delta N-15(PN). In addition, independent datasets were used to validate the BPNN model, which showed good accuracy in delta N-15(PN) retrieval, indicating that an effective model for retrieving delta N-15(PN) has been built based on machine learning algorithm. However, to enhance machine learning algorithm performance, we need to strengthen the information collection covering diverse coastal water bodies and optimize the input variables of optical indicators. This study provides important technical support for large-scale and long-term understanding of the biogeochemical processes of particulate organic matter, as well as a new management strategy for water quality and environmental monitoring.