Nitrogen content is an essential element in the whole life cycle of vegetation. The estimation of mangrove Canopy Nitrogen Content (CNC) by remote sensing is greatly important for mangrove health monitoring. At present, studies that use satellite hyperspectral data to retrieve CNC of forest at regional scales, especially for mangroves, are few. In addition, the low spatial resolution of most satellite hyperspectral images and the difficulty of measuring the average leaf nitrogen content of a single image pixel in real time limit the inversion accuracy. In this study, the super-resolution reconstruction of Sentinel-2 image and in-site measurement data was used for retrieving mangrove CNC to explore the application potential of enhanced Sentinel-2 image in mangrove monitoring.Taking Zhanjiang Gaoqiao Mangrove National Nature Reserve, China as the study area, the red edge bands, near-infrared, and short wave bands of Sentinel-2 were reconstructed from 20 m to 10 m by resampling, Sen2Res, and SupReMe algorithms, respectively. The reconstructed images are used to build 40 vegetation indices and analyze their correlation with CNC. Then, the SVM-RFE iterative feature deletion method was used to determine the optimal variable combination of mangrove CNC estimation, and the Kernel Ridge Regression (KRR) model was used to construct the prediction model of mangrove CNC. Finally, the optimal model was used to map CNC spatial distribution of mangrove forests.Significant differences in canopy nitrogen content and leaf nitrogen content were found among different mangrove species, and the variation of intraspecific CNC was abundant. The reconstructed images based on Sen2Res and supreme super resolution algorithm not only had high spectral consistency (the R² values of all bands are above 0.96) with the resampled image, but also significantly improved the clarity and spatial detail of the image compared with the 20 m resolution image. The bands sensitive to mangrove CNC are mainly concentrated in the red band (B4), red-edge band (B5), near-infrared band (B8a), and short-wave infrared band (B11 and B12). Vegetation indices related to red-edge band (RSSI and TCARIre1/OSAVI) are also effective variables to predict mangrove CNC. The inversion accuracy (R²val>0.579) of the reconstructed 10 m image based on the three methods is better than that of the original 20 m image (R²val=0.504). The fitting accuracy of the inversion model based on the reconstructed Sen2Res image (R²val=0.630, RMSE_val=5.133, RE_val=0.179) is almost the same as the resampled (R²val=0.640, RMSE_val=5.064, RE_val=0.179), and its model validation accuracy (R²cv=0.497, RMSE_cv=5.985, RE_cv=0.214) is higher. In addition, the variable number of Sen2Res is the most reasonable.Based on the spectral details and model accuracy of reconstructed images, Sentinel-2 images constructed by Sen2Res algorithm have good application potential in mangrove canopy nitrogen content estimation and can provide effective method reference and data support for fine monitoring of mangrove canopy health status at regional scale. Compared with vegetation, such as crops and grasslands, the factors influencing CNC inversion of mangroves are more complex. Although the influence of the main canopy structure factor (LAI) was considered in this study, other factors, such as species, community structure, leaf inclination, and synergistic changes, in other biochemical components should be further investigated.

remote sensing;Mangrove forests;Canopy nitrogen content;Sentinel-2;image reconstruction;SVM-RFE;KRR