基于多源遥感的积雪反照率时空重建方法研究

积雪反照率在全球气候系统中的作用显著。由于目前遥感手段的限制，积雪反照率遥感产品存在显著的数据缺失和误差不确定性。本研究提出了一种综合遥感数据与气象要素数据，结合辐射传输正向模型和反演模型的积雪反照率时空重建方案。该方案的核心和创新之处是,使用有着明确物理机制的积雪过程模型和数据同化方法来估计遥感缺失地区的积雪信息，从而为积雪反照率重建提供可靠的数据来源。主要内容包括：1）对遥感积雪反照率反演模型进行精度评估，提炼出一套针对不同环境因素、卫星数据质量以及积雪覆盖比例的优化反演方法；2）使用气象要素驱动的积雪过程模型来模拟遥感数据缺失地区的积雪信息，并将遥感反演的积雪面积与粒径同化到模型中，提高积雪属性模拟的精度；3）使用遥感数据进行正向模型的空间参数标定，用同化后的积雪信息来驱动正向模型，进而模拟资料缺失地区的积雪反照率，并将其与精度较优的反演结果相结合，实现积雪反照率的时空无缝重建。

Snow albedo plays an important role in global climate system. There are notable data missing and error uncertainties, attributing to the limit of remote sensing technology. This project suggests an spatial-temporal reconstruction of snow albedo utilizing radiation transferring model and remote sensing retrieve model, based on remote sensing data and meteorological data. The key and the innovation of this project is to estimate snow information under data missing area, utilizing physically based snow processes model and data assimilation method. The estimated snow information can be used as reliable driven data for snow albedo reconstruction. The main issues of this project include, 1) accuracy evaluation of snow albedo retrieve model, and optimized retrieve method for different environment, remote sensing data quality and fractional snow cover; 2) simulation of snow information in regions where remote sensing data were missing, using snow processes model driven by spatially distributed meteorological data. And to assimilate remotely sensed snow cover and grain size into snow processes model, for better accuracy of simulated snow information; 3) calibration of spatial parameters of snow radiation transfer model using remote sensing data, and simulation of snow albedo in data missing area based on the assimilated snow information using snow radiation transfer model. The simulated snow albedo are combined into remotely sensed snow albedo with good accuracy, and finally obtain the seamless spatiotemporal snow albedo.