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

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, RF, 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. © 2023 National Remote Sensing Bulletin. All rights reserved.