摘要
微波遥感能穿透云层,甚至可穿透一定程度的雨区,可以弥补热红外遥感的不足。发展基于被动微波遥感的地表温度反演算法可以全天候地为相关领域提供数据服务。根据前人研究,该文从方法论的角度将已有的反演方法分为统计模型法、物理模型法和神经网络算法3类,分析了每种方法的优缺点,并探讨了未来微波遥感反演地表温度的发展方向,以期为进一步研究提供参考。
Microwave remote sensing can penetrate the clouds and even to some extent the rain and can make up for the lack of thermal infrared light. In order to get land surface temperature （I_ST） under all weather conditions, it is essential to develop the inversion algorithm based on microwave remote sensing data. This paper reviews the former algorithms found in literature, which can be roughly categorized into the statistical - based and physical - based retrieval algorithms and neural network algorithm from a methodology perspective. The merits and disadvantages are summarized for each method. This paper also points out the direction of future development and provides a reference for future research.
出处
《国土资源遥感》
CSCD
北大核心
2014年第1期1-7,共7页
Remote Sensing for Land & Resources
基金
国家自然科学基金项目“全天候长时间序列观测角度和时间归一的地表温度遥感反演方法研究”(编号:41231170)资助
关键词
被动微波遥感
地表温度
统计模型
物理模型
神经网络算法
passive microwave remote sensing
land surface temperature
statistical algorithm
physical retrievalmodel
neural network algorithm
