Wetland Vegetation Species Classification Using Optical and SAR Remote Sensing Images: A Case Study of Chongming Island, Shanghai, China

Mudflat vegetation plays a crucial role in the ecological function of wetland environment, and obtaining its fine spatial distribution is of great significance for wetland protection and management. Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area. However, the imaging of optical sensors is easily restricted by weather conditions, and the backscattered information reflected by Synthetic Aperture Radar (SAR) images is easily disturbed by many factors. Although both data sources have been applied in wetland vegetation classification, there is a lack of comparative study on how the selection of data sources affects the classification effect. This study takes the vegetation of the tidal flat wetland in Chongming Island, Shanghai, China, in 2019, as the research subject. A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source (Sentinel-2) and SAR data source (Sentinel-1), respectively. The performance of optical and SAR data and their feature parameters in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations. Furthermore, by simulating the scenario of missing optical images, the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed. Results show that: 1) under the same classification algorithm, the Overall Accuracy (OA) of the combined use of optical and SAR images was the highest, reaching 95.50%. The OA of using only optical images was slightly lower, while using only SAR images yields the lowest accuracy, but still achieved 86.48%. 2) Compared to using the spectral reflectance of optical data and the backscattering coefficient of SAR data directly, the constructed optical and SAR feature parameters contributed to improving classification accuracy. The inclusion of optical (vegetation index, spatial texture, and phenology features) and SAR feature parameters (SAR index and SAR texture features) in the classification algorithm resulted in an OA improvement of 4.56% and 9.47%, respectively. SAR backscatter, SAR index, optical phenological features, and vegetation index were identified as the top-ranking important features. 3) When the optical data were missing continuously for six months, the OA dropped to a minimum of 41.56%. However, when combined with SAR data, the OA could be improved to 71.62%. This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing, especially in regions with long-term cloud cover.