Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes

Abstract

The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (Rrs) was established to develop a retrieval algorithm for POC sources with the following four bands: (Rrs(443)/Rrs(492)) × (Rrs(704)/Rrs(665)). The results showed that the four-band algorithm performed well with R2, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes.

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

“…where l is the wavelength, L u (l) is the upwelling spectral radiance, L s (l) is the incident spectral sky radiance and r is the proportionality coefficient, with a value of 0.025 (Yu et al, 2023). L p (l) is the radiance from gray reference panel, r p (l) is the known reflectance of the gray panel (Yu et al, 2023).…”

Spatial Patterns of Turbidity in Cartagena Bay, Colombia, Using Sentinel-2 Imagery

“…However, due to the significant spatial variability, mainly in coastal waters, higher spatial resolution remote sensing sensors such as the Landsat Family, i.e., Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), and particularly the Operational Land Image (OLI) temporal series have been recently used to track water quality changes [21][22][23][24][25][26]. Since 2015, multispectral instruments (MSIs) on board the Sentinel-2 satellites have significantly improved spatial and temporal capabilities to study the behavior of biogeochemical parameters in several bays, including San Francisco Bay [27], Kastela Bay [28], the Bengal Bay [29], and Zhanjiang Bay [30]. The high spatial resolution of Sentinel-2 data makes them a valuable complement to traditional sampling strategies.…”