山水林田湖草生态保护修复是保障国家生态安全的重要举措，科学合理的生态保护修复项目监测和绩效评价方法，有助于保障生态保护修复工程的顺利实施和生态保护修复成果的可持续发展。本研究采用文献综述和案例分析相结合的方法，论述了遥感技术在山水林田湖草生态保护修复项目监测和绩效评价中的具体应用。从关注区域生态系统格局和质量两方面出发，选择遥感数据获取的土地覆盖类型、植被长势、水质参数作为评价指标，整体实现了山水林田湖草各生态要素的监测；从维护和提升区域生态系统服务出发，选择水源涵养、水土保持、生态系统承载力等指标，论述了遥感数据在辅助山水林田湖草生态保护修复项目绩效评价中的作用。最后指出积极探索新的遥感数据挖掘方法，并结合遥感技术与传统的调查方法，有望形成更客观、更快速且具备可执行性的山水林田湖草生态保护修复项目监测及绩效评价体系。

Ecological protection and restoration of mountains, rivers, forests, farmlands, lakes, and grasslands is an important measure to ensure national ecological security. In this study, we developed scientific and rational monitoring and performance evaluation methods to ensure successful implementation of ecological protection and restoration projects and sustainable development of ecological restoration achievements.This study uses a combination of literature review and case analysis to discuss the specific application of remote sensing technology in the monitoring and performance evaluation of ecological restoration of mountains, rivers, forests, farmlands, lakes, and grasslands.Based on the pattern and quality of regional ecosystem, the land cover type, vegetation growth, and water quality parameters obtained from remote sensing data are selected as evaluation indices to monitor ecological elements of mountains, rivers, forests, farmlands, lakes, and grasslands in general. To maintain and improve the regional ecosystem services, we select water conservation, soil erosion, ecosystem bearing capacity, and other indicators to discuss the role of remote sensing data in assisting the performance evaluation of ecological protection and restoration projects of mountains, rivers, forests, farmlands, lakes, and grasslands.Results show that actively exploring new remote sensing data mining methods, which combine remote sensing technology and traditional investigation methods, is expected to form a more objective and faster executable monitoring and performance evaluation system for ecological protection and restoration projects of mountains, rivers, forests, farmlands, lakes, and grasslands.