氮素是植被整个生命周期的必要元素，红树林冠层氮素含量（CNC）遥感估算对红树林健康监测具有重要意义。以广东湛江高桥红树林保护区为研究区，本文旨在基于Sentinel-2影像超分辨率重建技术进行红树林CNC估算和空间制图。研究首先基于三次卷积重采样、Sen2Res和SupReMe算法实现Sentinel-2影像从20 m分辨率到10 m的重建；然后以重建后的影像和原始20 m影像为数据源构建40个相关植被指数，采用递归特征消除法（SVM-RFE）确定CNC估算的最优变量组合，进而构建CNC反演的核岭回归（KRR）模型；最后选取最优模型实现CNC制图。研究结果表明：基于Sen2Res和SupReMe超分辨率算法的重建影像不仅与原始影像具有很高的光谱一致性，且明显提高了影像的清晰度和空间细节。红树林CNC反演波段主要集中在红（B4）、红边（B5）、近红外波段（B8a）以及短波红外波段（B11和B12），与“红边波段”相关的植被指数（RSSI和TCARIre1/OSAVI）也是红树林CNC反演的有效变量。基于3种方法重建后10 m的影像构建的模型反演精度（R²val>0.579）均优于原始20 m的影像（R²val=0.504）；基于Sen2Res算法重建影像构建的反演模型拟合精度（R²val=0.630，RMSE_val=5.133，RE_val=0.179）与基于三次卷积重采样重建影像的模型拟合精度（R²val=0.640，RMSE_val=5.064，RE_val=0.179）基本相当，前者模型验证精度（R²cv=0.497，RMSE_cv=5.985，RE_cv=0.214）较高且模型变量选择数量最为合理。综合重建影像光谱细节及模型精度，基于Sen2Res算法重建的Sentinel-2影像在红树林CNC估算中具有良好的应用潜力，能为区域尺度红树林冠层健康状况的精细监测提供有效的方法借鉴和数据支撑。

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.