National-scale sub-meter mapping of Spartina alterniflora in mainland China 2020

Abstract

Current large-scale maps of Spartina alterniflora (S. alterniflora) with 10 m resolution hinder accurate delineation of community boundaries, detection of internal features such as creeks, and identification of small patches. These limitations further compromise the accuracy of spatial distribution extraction and subsequent analyzes. To this end, this study produced the first 2020 national-scale Sub-meter (0.9 m) S. alterniflora Map of Mainland China (CM-SSM), using an object- and sub-meter-enhanced pixel-based phenological feature composite method. The method integrates phenological features from Sentinel-2 with spatial and texture details from Google Earth imagery, improving the spectral separability and mitigating mixed-pixel effects. Compared to the 10 m S. alterniflora product of Mainland China (CMSA), CM-SSM improved overall accuracy by 14.60 % and the F1 score by 0.24. Although the total mapped areas of CM-SSM (59 371 ha) and CMSA (58 006 ha) differ by only 1365 ha, their spatial distributions diverge substantially. When benchmarked against CM-SSM, CMSA exhibited commission and omission errors totaling 34 273 ha (57.73 %). Moreover, the number of patches identified by CM-SSM (148 072) was over 17 times greater than that of CMSA, reflecting its superior capability in detecting fragmented distributions. In addition, Soil Organic Carbon (SOC) estimates derived from CM-SSM were 706.69 Gg (23.09 %) higher than those reported by the corresponding national SOC product for the same year, emphasizing the essential contribution of high-resolution mapping to accurate carbon accounting for S. alterniflora. These advances enhance understanding of S. alterniflora invasion dynamics, support carbon accounting, and inform evidence-based coastal wetland management and restoration.