联合SAR与光学遥感数据的山区积雪识别模型研究

Research on identification of snow surface type and snow wetness state is of great significance to the local climate research and hydrological process simulation. Optical remote sensing is difficult to differentiate snow from cloud, meanwhile, the backscatter signal of Synthetic Aperture Radar (SAR) is severely affected by the rugged mountain terrain. During the completion of doctoral dissertation, the applicant selected a typical mountain area in Manasi River Basin of Tianshan Mountains, Xinjiang Province as the study area, investigated the method of snow cover recognizing based on SAR and optical remote sensing data, and proved the feasibility of the model of snow recognizing in mountain areas using combined SAR and optical remote sensing data. Therefore, this proposal will based on the preliminary research, firstly using the ground synchronization observations to analyze the characterization of SAR and optical remote sensing images for snow cover information and explore the factors affecting the snow identification method and its influence mechanism, then using the complementarity of the two types of sensors to overcome the influence of cloud and terrain, and finally build a probability graph model based on the geospatial context knowledge, and realize the simultaneous recognition on snow surface type and snow wetness state in rugged mountain terrain. The expected achievements may be regarded as a theoretic reference for the combined application of multi-source remote sensing data for snow monitoring, and applied in snow melting process simulation and snow water management, which will have a great theoretical and practical value.

山区积雪表面类型和干湿状态的识别，对局地气候研究和水文过程模拟具有重要意义。光学遥感难以识别云覆盖区积雪，而合成孔径雷达（SAR）后向散射信号受地形影响严重，因此，申请人在攻读博士学位期间，以新疆玛河流域山区作为研究区，在多次实地考察与地面同步观测基础上，探索了SAR与光学遥感联合识别积雪的方法，证明了二者联合识别山区积雪模型构建的可能性。本项目旨在前期研究基础上，结合地面观测，深入分析SAR与光学遥感图像积雪表征信息，探讨二者识别山区积雪所受影响因素及其影响机制；然后利用二者的互补性，克服光学遥感识别积雪受云的限制和SAR数据识别山区积雪受地形的影响；最后结合山区地理空间上下文知识，构建二者联合识别山区积雪的概率图模型，并采用全局推理，实现积雪表面类型和干湿状态的同时识别。研究成果可为多源遥感数据监测积雪提供理论依据，为融雪过程模拟和水资源管理提供科学参考，具有较大的理论与实际应用价值。

积雪是冰冻圈的重要组成部分，也是地表极为活跃且具有多重属性的自然要素之一，季节性积雪是我国西北干旱、半干旱地区主要的淡水资源，同时，积雪灾害也是主要的气象灾害之一。积雪识别研究，尤其是积雪表面类型和干湿状态的同时识别，对山区融雪过程监测、局地气候研究、积雪灾害评估、雪水资源管理等具有重要意义。本项目按照“卫星地面同步观测——SAR与光学遥感图像积雪特征表征分析——SAR图像积雪物理状态类型和光学图像几何形态识别——联合SAR与光学遥感数据的山区积雪识别模型构建”的思路，提出了联合SAR与光学遥感数据识别山区积雪的方法，构建了SAR与光学遥感数据联合识别山区积雪的模型，克服了光学遥感数据识别积雪受云限制、SAR数据识别积雪受地形影响的技术难题，提高了积雪识别的精度，实现了积雪表面类型和干湿状态的同时识别。.在科学问题探讨和创新性方面，针对山区积雪状态识别问题，通过充分发挥SAR与光学遥感数据的互补性，研究并建立一套完整的基于SAR与光学遥感数据联合识别积雪的模型方法，实现了多传感器在山区积雪识别上的互补，具有一定的理论和应用创新性。同时，通过联合SAR与光学遥感数据，消除了光学遥感数据识别积雪受云的限制，克服了SAR数据识别积雪受地形的影响，实现了积雪的表面类型和干湿状态的同时识别，具有一定的技术方法创新性。项目研究结果可应用于山区融雪过程监测、局地气候研究、雪水资源管理等领域。随着国产高分辨率SAR与光学遥感卫星的陆续发射，后续有望联合在时间上加密观测的光学和微波对地观测卫星，实现山区积雪几何状态和物理参数状态的高时间分辨率监测。