AUTHOR=Yu Guo , Zhong Yafeng , Fu Dongyang , Chen Fajin , Chen Chunqing 

TITLE=Remote sensing estimation of δ15NPN in the Zhanjiang Bay using Sentinel-3 OLCI data based on machine learning algorithm

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1366987

DOI=10.3389/fmars.2024.1366987

ISSN=2296-7745

ABSTRACT=The particulate nitrogen (PN) isotopic composition (δ 15 NPN) plays an important role in quantifying the contribution rate of particulate organic matter sources and indicating water environmental pollution. Estimation of δ 15 NPN 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 δ 15 NPN, we have developed a machine learning algorithm for retrieving δ 15 NPN. 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, we compared the retrieval results of Back Propagation Neural Network (BPNN), Random Forest (RF) and Multiple Linear Regression (MLR) using optical indicators composed of in situ remote sensing reflectance as input variables, and found that the 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‰, and 20.10%, respectively. From the satellite retrieval results, it can be inferred that the retrieval value of δ 15 NPN had good consistency with the measured value of δ 15 NPN, indicating that an effective model for retrieving δ 15 NPN 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.