Retrieving canopy nitrogen content of mangrove forests from Sentinel-2 super-resolution reconstruction data

Jianing ZHEN; Xiapeng JIANG; Demei ZHAO; Junjie WANG; Jing MIAO; Guofeng WU

doi:10.11834/jrs.20221461

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Retrieving canopy nitrogen content of mangrove forests from Sentinel-2 super-resolution reconstruction data
Vol. 26, Issue 6, Pages: 1206-1219(2022)
Received：01 July 2021，

Published：07 June 2022
DOI： 10.11834/jrs.20221461
稿件说明：

移动端阅览

甄佳宁，蒋侠朋，赵德梅，王俊杰，苗菁，邬国锋.2022.利用Sentinel-2影像超分辨率重建的红树林冠层氮含量反演.遥感学报，26（6）： 1206-1219 DOI： 10.11834/jrs.20221461.

Zhen J N，Jiang X P，Zhao D M，Wang J J，Miao J and Wu G F. 2022. Retrieving canopy nitrogen content of mangrove forests from Sentinel-2 super-resolution reconstruction data. National Remote Sensing Bulletin， 26（6）：1206-1219 DOI： 10.11834/jrs.20221461.

摘要

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（

val

0.579）均优于原始20 m的影像（

val=0.504）；基于Sen2Res算法重建影像构建的反演模型拟合精度（

val=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（

val=0.640，RMSE_val=5.064，RE_val=0.179）基本相当，前者模型验证精度（

cv=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。

Abstract

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

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 (

val

0.579) of the reconstructed 10 m image based on the three methods is better than that of the original 20 m image (

val=0.504). The fitting accuracy of the inversion model based on the reconstructed Sen2Res image (

val=0.630

RMSE_val=5.133

RE_val=0.179) is almost the same as the resampled (

val=0.640

RMSE_val

5.064

RE_val

0.179)

and its model validation accuracy (

cv=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.

关键词

Keywords

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