Research progress and prospects of hyperspectral remote sensing for global wetland from 2010 to 2022

SUN Weiwei; LIU Weiwei; WANG Yumiao; ZHAO Rui; HUANG Mingzhu; WANG Yao; YANG Gang; MENG Xiangchao

doi:10.11834/jrs.20232620

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Research progress and prospects of hyperspectral remote sensing for global wetland from 2010 to 2022
Vol. 27, Issue 6, Pages: 1281-1299(2023)
Received：15 November 2022，

Published：07 June 2023
DOI： 10.11834/jrs.20232620
稿件说明：

移动端阅览

孙伟伟，刘围围，王煜淼，赵锐，黄明珠，王耀，杨刚，孟祥超.2023.2010年—2022年全球湿地高光谱遥感研究进展与展望.遥感学报，27（6）： 1281-1299 DOI： 10.11834/jrs.20232620.

Sun W W， Liu W W， Wang Y M， Zhao R， Huang M Z， Wang Y， Yang G and Meng X C. 2023. Research progress and prospects of hyperspectral remote sensing for global wetland from 2010 to 2022. National Remote Sensing Bulletin， 27（6）：1281-1299 DOI： 10.11834/jrs.20232620.

摘要

湿地位于陆地和水生生态系统之间的过渡地带，具有维持生态平衡，保护生态多样性、涵养水源等重要作用。高光谱遥感具有波段窄、波段数多和信息丰富等优势，已经成为湿地监测的重要技术手段。本文首先梳理了2010年以来的湿地高光谱中英文期刊论文，分析了作者所属国家/机构、研究热点和关键词等信息。其次，从载荷平台、信息处理、遥感制图和定量反演4个方面，厘清了当前国内外湿地高光谱遥感的研究现状。最后，提出了湿地高光谱遥感亟待解决的问题和未来发展方向。本文研究旨在为高光谱遥感和湿地应用的交叉研究指明方向，对湿地遥感前沿理论和技术发展具有重要意义。

Abstract

Wetlands are transitional zones between terrestrial and aquatic ecosystems and play important roles in maintaining ecological balance

protecting ecological diversity

conserving water sources

and regulating climate. However

traditional field investigations and panchromatic and multispectral remote sensing technologies cannot meet the practical needs of current wetland monitoring. Hyperspectral remote sensing technology has become an important approach for wetland monitoring owing to its advantages of high spectral resolution and rich spectral information. This review summarizes the related literature on the hyperspectral application of wetlands from 2010 to the present.

First

the literature was analyzed using CiteSpace software. Then

the country/institution of authors

international cooperation

keywords

research hotspots

and research trends were clarified. Finally

the feature extraction and dataset processing methods of hyperspectral datasets and their progress in wetland mapping and quantitative inversion were determined.

China and the United States are the top two countries in terms of the number of hyperspectral wetland studies

but only a few international collaborations have been pursued. In addition

the classification of vegetation in wetlands is a hot research topic. Spartina alterniflora

reed

water quality

and soil properties have become the focus of hyperspectral wetland research. Machine learning methods represented by Random Forests (RFs) play an important role in wetland hyperspectral research. However

studies on classification and inversion based on deep learning methods are limited. Furthermore

under the background of global warming

coastal wetlands have received widespread attention from researchers worldwide. For hyperspectral remote sensing sensors

China’s spaceborne hyperspectral platforms have developed rapidly

but foreign countries have dominated ground and near-ground hyperspectral remote sensing platforms

with a spectral coverage range of 350—1000 nm. In terms of hyperspectral information extraction and image processing

studies have mainly focused on traditional feature extraction and classification methods

such as PCA

MNF

decision trees

and spectral angle mappers. The processing and feature extraction of hyperspectral data based on deep learning feature extraction is expected to be an important research direction in the future. Hyperspectral wetland mapping mainly focuses on wetland vegetation

mangroves

and salt marsh vegetation. Nonetheless

the scale of existing research has been limited to small areas

such as nature reserves or national wetland parks

and the mapping algorithm continues to rely on traditional methods

such as RF and support vector machines. More refined tree species identification and mapping from the use of hyperspectral images is a relevant future research direction. Research on hyperspectral wetland quantitative inversion has mostly focused on chlorophyll and aboveground biomass. In the inversion process

the sensitive band is determined using the correlation coefficient between the ground measurement and the hyperspectral band or spectral index. Simple models

such as linear

quadratic polynomial

and logarithmic functions

are subsequently constructed to obtain the estimated biophysical parameters. Deep learning algorithms have good application prospects in hyperspectral band feature selection and inversion estimation models. Moreover

given the complexity of wetland vegetation

small-scale or point-scale parameter inversion is taken as the main research scale. Large-scale hyperspectral wetland quantitative inversion is difficult to implement due to the existence of high wetland heterogeneity. The resolution of hyperspectral images is not high enough

and mixed pixels exist.

The fusion of multisource remote sensing data

such as multispectral-hyperspectral fusion

to improve the resolution or the development of corresponding spectral unmixing algorithms is the future direction of quantitative analysis for hyperspectral remote sensing applications in wetlands.

关键词

Keywords

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