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Wetland is a transitional zone between terrestrial and aquatic ecological systems and it plays an important role in maintaining ecological balance, protecting ecological diversity, conserving water sources, regulating climate, etc. However, traditional field investigation and tranditional panchromatic and multispectral remote sensing technologies cannot meet the practical needs of current monitoring of wetland. Hyperspectral remote sensing technology has become an important means for wetland monitoring due to its advantages of high spectral resolution and rich spectral information. This review summarizes the related literatures on hyperspectral application of wetland from 2010 to present. Firstly, literatures were analysed using Citespace software. And the author"s country/institution, international cooperation, keywords, research hotspots and research trend are clarified. Moreover, the feature extraction and dataset processing methods of hyperspectral dataset and its progress in wetland mapping and quantitative inversion are also analyzed. The study found that China and the United States are the top two countries of number of hyperspectral wetland research, but there are rare international cooperations. Besides, the classification of vegetation in wetlands is a hot topic. And spartina alterniflora, reed, water quality, and soil properties are also the interest targets of hyperspectral wetland researches. Machine learning methods represented by random forests (RF) play an important role in wetland hyperspectral research, but there are rare literatures on classification and inversion based on deep learning methods. In addition to that, under the background of global warming, coastal wetlands have received more attention from worldwide researchers. As for the hyperspectral remote sensing sensor, China"s spaceborne hyperspectral platforms have developed rapidly, but the ground and near-ground hyperspectral remote sensing platforms are dominated by foreign countries, with spectral coverage range of 350-1000 nm. In terms of hyperspectral information extraction and image processing, researches mainly focus on traditional feature extraction and classification methods, such as PCA, MNF, random forest, decision tree, spectral angle mapper, etc. The processing and feature extraction of hyperspectral data based on deep learning feature extraction is an important research direction in the future. Hyperspectral wetland mapping mainly focuses on wetland vegetation, mangrove and salt marsh vegetation. However, the existing research scale is limited to small areas such as nature reserves or national wetland parks and the mapping algorithm relies on traditional methods, such as random forest, support vector machine. On the other hand, more refined tree species identification and mapping using hyperspectral images is a future research direction that is worth exploring. The research focus of hyperspectral wetland quantitative inversion is mostly focus on chlorophyll and aboveground biomass. In the inversion process, the sensitive band were determined using correlation coefficient between ground measurement and the hyperspectral band or spectral index. Then, simple models such as linear, quadratic polynomial, and logarithmic function are constructed to obtain the estimated biophysical parameters. Deep learning algorithms have nice application prospects in hyperspectral band feature selection and inversion estimation models. In addition to that, due to the complexity of wetland vegetation, small-scale or point-scale parameter inversion are the main research scale. Large-scale hyperspectral wetland quantitative inversion is difficult due to the existence of high heterogeneity of wetland. The resolution of hyperspectral images is not high enough and the mixed pixels exists seriously. The fusion of multi-source remote sensing data such as multi-spectral-hyperspectral fusion to improve the resolution, or the development of corresponding spectral unmixing algorithms, is the future quantitative analysis direction for hyperspectral remote sensing application on wetland.