Wetlands not only affect the local hydrology and ecosystems, but also regulate the conditions of human-environment. However, the availability of accurate wetland data remains a key challenge in wetland research. This study attempts to address this problem through a novel mapping framework that is based on the Google Earth Engine (GEE), feature optimization, and the random forest (RF) model (GFORF). This framework was built to map high-accuracy wetland data on the headwaters of the Brahmaputra, Ganges, and Indus rivers (HBGIR) in the western Tibetan Plateau (TP). Four time periods were examined: 1990, 2000, 2010, and 2017. Our results showed that the overall accuracy for the acquired wetland data was 82.73%, 83.16%, 82.47%, and 88.14% in 1990, 2000, 2010, and 2017, respectively. Furthermore, the feature optimization results showed that the spectral indices feature was the main contributor to the accuracy of wetland mapping, with the highest value being 26.9%. The seasonal factors, surface reflectance, auxiliary data, and texture contributed 21.8%, 21.6%, 21.5%, and 8.1%, respectively. Combining the seasonal features and auxiliary data of distances to rivers significantly improved the mapping accuracy of the wetlands by approximately 14%, 24%, 11%, and 10% in 1990, 2000, 2010, and 2017, respectively. In addition, our analysis showed that the wetland areas in the HBGIR amounted to 5177.39 km², accounting for 5.82% of the total area. Over the 30-year observation period, the overall consolidation of the wetlands was characterized by a slight expansionary phase, with an average increase of 0.16% per year from 1990 to 2017. As a result of the improvement in the accuracy of wetland mapping in alpine areas, the change dynamics of wetlands was revealed, which provides justification for implementing ongoing wetland ecological services and protection measures.

湿地不仅影响当地的水文和生态系统，而且还调节人类环境条件。然而，准确的湿地数据的可用性仍然是湿地研究中的一个关键挑战。本研究试图通过一种基于谷歌地球引擎 (GEE)、特征优化和随机森林 (RF) 模型 (GFORF) 的新型映射框架来解决这个问题。该框架旨在绘制青藏高原西部 (TP) 雅鲁藏布江、恒河和印度河 (HBGIR) 源头的高精度湿地数据。检查了四个时间段：1990年、2000年、2010年和2017年。我们的结果表明，1990年、2000年、2017年和2017年获得的湿地数据的总体准确率分别为82.73%、83.16%、82.47%和88.14%。分别。此外，特征优化结果表明，光谱指数特征是湿地制图精度的主要贡献者，最高值为26.9%。季节性因素、表面反射率、辅助数据和纹理分别贡献了 21.8%、21.6%、21.5% 和 8.1%。1990、2000、2010和2017年，结合季节特征和河流距离辅助数据，湿地的测绘精度分别提高了约14%、24%、11%和10%。此外，我们的分析表明，HBGIR 湿地面积达 5177.39 平方公里 1990、2000、2010和2017年，结合季节特征和河流距离辅助数据，湿地的测绘精度分别提高了约14%、24%、11%和10%。此外，我们的分析表明，HBGIR 湿地面积达 5177.39 平方公里 1990、2000、2010和2017年，结合季节特征和河流距离辅助数据，湿地的测绘精度分别提高了约14%、24%、11%和10%。此外，我们的分析表明，HBGIR 湿地面积达 5177.39 平方公里²、占总面积的5.82%。在30年的观测期内，湿地整体巩固呈现出一个小幅扩张阶段，1990年至2017年平均每年增加0.16%。区，揭示了湿地的变化动态，为实施持续的湿地生态服务和保护措施提供了依据。