Evaluation of machine learning algorithm capability for Bosten Lake Wetland classification based on multi-temporal Sentinel-2 data

As crucial carbon sinks within terrestrial ecosystems, wetlands have been extensively studied in terms of spatio-temporal distributions. However, existing methods for classifying wetlands are of limited accuracy, and it is difficult to acquire consistent samples over time. Therefore, precise classification methods are required to facilitate wetland conservation and ecological restoration. In this study, multiple machine learning (ML) algorithms in combination with feature sets based on Sentinel-2 data were used to accurately classify the land-use types (LUTs) of the Bosten Lake Wetland (BLW) in Xinjiang, China. The enhanced water index (EWI), modified normalised difference water index (MNDWI), and normalised difference water index (NDWI) were selected to extract water information and distinguish water bodies from land surfaces in the BLW. Three classification plans based on vegetation indices, water indices, and textural features were developed using artificial neural network (ANN), support vector machine (SVM), random forest (RF) algorithms. Plan 9 combined vegetation water and texture with the highest overall accuracy (OA) 91.02 % and kappa coefficient (KC) 0.89. This plan obtained a producer accuracy of over 90 % for lake wetlands, river wetlands, grassland wetlands, mud flats, and farmland and > 83 % for construction land and bareland. According to Plan 9, the wetland area during 2018–2023 showed noticeable seasonal fluctuations but stable interannual changes. Conversely, non-wetland areas demonstrated significant interannual fluctuations, particularly in bareland and farmland, which might have been influenced by urbanisation and ecological policies. This study provides insights into the data sources, feature selection, and methodological approaches for wetland information extraction in arid regions.