SAI: A <i>Spartina alterniflora</i> Index Based on Sentinel-2 Multispectral Imagery for <i>Spartina alterniflora</i> Mapping

Spartina alterniflora has become the most problematic invasive species in China’s coastal regions due to its rapid growth, robust reproductive capacity, and extensive adaptability. It has importantly disrupted the structure and function of coastal wetland ecosystems, thereby posing a serious threat to the ecological security of these wetlands . China is currently engaged in a nationwide initiative to manage the invasive species S. alterniflora . An accurate and up-to-date understanding of the current distribution and dynamic changes of S. alterniflora is essential for formulating effective control measures. Remote sensing technology has enabled the rapid, large-scale monitoring of S. alterniflora . However, traditional remote sensing methods typically focus on single-period images of specific small- to medium-scale areas and depend heavily on a substantial number of training samples. Consequently, these methods exhibit weak model transferability and poor generalization capabilities, rendering them unsuitable for the fine-scale identification of S. alterniflora across extensive regions. This research proposed an S. alterniflora index (SAI) derived from Sentinel-2 imagery. The SAI was constructed using the Sentinel-2 Red and near-infrared (NIR) bands, formulated as (Red-NIR)/NIR, to accentuate the distinctions in greenness and moisture between S. alterniflora and other land cover types. This study surveyed 6 representative S. alterniflora distribution areas along the coastal regions of China. We compared the S. alterniflora extraction results using SAI with those obtained using common vegetation indices, sensitive bands, and classic machine learning-based methods. The results demonstrate that SAI surpasses other vegetation index and sensitive bands in extracting S. alterniflora , showing performance comparable to that of support vector machine. Furthermore, we applied this index to Landsat-8 images to test its performance on different datasets. We also validated its effectiveness for both native and invasive Spartina spp. habitats worldwide. Finally, we conducted S. alterniflora extraction across coastal regions of China, acquiring a 2020 dataset with a 10-m resolution. Comparative analysis with official statistics and existing datasets yielded favorable results. Therefore, the proposed method in this study shows promising potential for application in S. alterniflora monitoring, providing technical support for effective management and enhanced protection of coastal wetlands.