【Objective】This study aims to reveal the dynamic evolution patterns of reed wetland land types in Zhalong Wetland and quantify the spatiotemporal variations of their coverage characteristics, providing scientific basis for regional wetland ecological conservation and sustainable development.【Method】Based on Sentinel-2 multispectral remote sensing imagery from 2017 to September 2023 in Zhalong Wetland, a multi-temporal remote sensing dataset was constructed, containing five land categories: lake, reed bed, construction land, arable land, and saline-alkali land. An integrated classification approach combining the attention-based Vmamba model with NDWI water mask was proposed to extract spatial distributions and area changes of each land type. Meanwhile, the fractional vegetation cover (FVC) was inverted using the dimidiate pixel model, and leaf area index (LAI) and ecosystem quality index (EQI) were calculated.【Result】Classification results demonstrated that the overall accuracy (OA) of our algorithm was 80.85%, the mean intersection over union (MIoU) was 71.59%, and macro-F1 score (MF1) was 79.93%. During the study period, lake and reed bed areas in Zhalong Wetland showed expanding trends, while cultivated land and built-up land areas continuously decreased. Saline-alkali land area fluctuated dynamically. Both FVC and EQI exhibited a first-increasing-then-decreasing trend, which was generally consistent with the content of Chinese Climate Bulletin.【Conclusion】The proposed change-monitoring model integrating attention-based Vmamba and water mask demonstrates high applicability in wetland information extraction, significantly improving classification accuracy and dynamic monitoring precision. The collaborative monitoring results of FVC, LAI, and EQI provides reference for wetland resource management and sustainable utilization.