Wetland contains various ground objects with high spectral similarity. How to accurately distinguish complex classes has become a challenge in wetland land cover classification. In this paper, low-rank representation with elastic net (ENLRR) and the kernel version of ENLRR (KENLRR) are proposed for coastal wetland land cover classification by using Gaofen-5 (GF-5) hyperspectral data of China. The main idea of ENLRR is to combine elastic net with low-rank representation, which replaces rank function with the combination of nuclear norm and Frobenius norm when constraining the coefficient matrix. The KENLRR method considers nonlinear characteristics of hyperspectral data, where a neighborhood filter (NF) kernel function is adopted to map the original data space into a higher dimensional feature space for better classification. In the experiments, three typical coastal wetlands in China: Yellow River Delta, Jiangsu Dafeng Natural Reserve, and Yangtze River Delta (Nantong) are adopted, and the proposed methods and seven comparison methods are used to conduct wetland land cover classification. The experimental results demonstrate that the proposed ENLRR and KENLRR are effective in accurately distinguishing wetland ground objects and reliably mapping their distribution. More specifically, the KENLRR method can provide the best performance, and the OAs are 96.63%, 96.76% and 87.67% for the three wetlands, respectively. The land cover distributions and spatial patterns of the three wetlands are studied as well. Yellow River Delta is a typical estuarine wetland with abundant landscapes, Dafeng Nature Reserve is a coastal wetland with the block regular feature distribution in spatial, and Yangtze River Delta (Nantong) mainly includes river and flood plain, whose ecological environment is deeply affected by human activities.