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Systematic wetlands monitoring is crucial for informed management, timely conservations actions and consistent temporal reporting. Recent global wetland assessments show that Southeast Asia is losing wetlands at faster than rest of world, with the floodplain wetlands declining by about 1.2% per year, primarily due to agricultural conversion. Thus, making wetlands monitoring and reporting crucial. The national wetland inventory of Thailand, last complied in 2002, requires an update. In this context, the present study introduces a satellite-based Wetland Monitoring Tool (WMT) for assessing five freshwater Ramsar wetlands in Thailand.The WMT integrates multispectral Sentinel-2 and Synthetic Aperture Radar (SAR) Sentinel-1 observations to classify wetlands during the pre-monsoon period (March to May) from 2019 to 2025 in Google Earth Engine (GEE). Spectral indices (NDWI, MNDWI, NDVI, NDMI, NBR, AWEI, and Tasseled Cap components) are derived to identify pure pixels using Otsu thresholding. Sentinel-1 observations are used to complement optical observation results by improving identification of inundated and flooded vegetation, particularly in scenarios characterized by denser canopy cover or under cloud interference. Final wetland classification is done using a harmonized pixel-based, rule-based framework with an adaptive water detection approach for enhanced class separation across heterogeneous wetland ecosystems. Classification performance is evaluated using Overall Accuracy (OA), Producer’s Accuracy (PA), and the Kappa coefficient.Results show vegetation increased across all five Ramsar wetlands, especially in Khao Sam Roi Yot (+823 ha) and Lower Songhkram River Wetland (+641 ha). Four of the five wetlands showed decline in open water, with Bung Khong Long Non-Hunting Area losing maximum area ( -163.4 ha; -13%) and Nong Bong Kai Non-hunting Area losing largest proportional area (-67.4 ha; -28%). Only Khao Sam Roi Yot shows increase in open water (+159.4 ha; 6.5%). Emergent and flooded vegetation declined significantly in smaller wetlands, especially in Nong Bong Kai, where they declined 86.2% and 73.4%, respectively. In contrast, land class declined significantly in larger wetlands, particularly in Khao Sam Roi Yot (−1,141.8 ha; -28.4%) and Lower Songkhram (−534.7 ha; −9.8%), indicating rise in wetland vegetation classes within Ramsar boundaries. The integrated optical-SAR approach in WMT enhances wetlands classification, can be scaled at national and regional level and demonstrates potential for standardized, long-term mapping and reporting for improved wetland management and decision making.Keywords: Wetlands, Ramsar, Sentinel-1, Sentinel-2, Synthetic Aperture Radar, Google Earth Engine, Thailand