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

Abstract

The particulate nitrogen (PN) isotopic composition (δ15NPN) plays an important role in quantifying the contribution rate of particulate organic matter sources and indicating water environmental pollution. Estimation of δ15NPN 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 δ15NPN, we have developed a machine learning algorithm for retrieving δ15NPN. 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 δ15NPN 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 (R2), 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 δ15NPN had good consistency with the measured value of δ15NPN. In addition, independent datasets were used to validate the BPNN model, which showed good accuracy in δ15NPN retrieval, indicating that an effective model for retrieving δ15NPN 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.