地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (7): 941-950.doi: 10.3724/SP.J.1047.2016.00941

• 遥感科学与应用技术 • 上一篇    下一篇

基于多源数据的青藏高原雪深重建

唐志光1(), 李弘毅2, 王建2, 梁继1, 李朝奎1, 车涛2, 王欣1   

  1. 1. 湖南科技大学 地理空间信息技术国家地方联合工程实验室,湘潭 411201
    2. 中国科学院寒区旱区环境与工程研究所,兰州 730000
  • 收稿日期:2015-06-29 修回日期:2015-10-28 出版日期:2016-07-15 发布日期:2016-07-19
  • 作者简介:

    作者简介:唐志光(1985-),男,湖南邵阳人,博士,讲师,主要从事冰冻圈遥感研究。E-mail: tangzhg11@lzb.ac.cn

  • 基金资助:
    国家自然科学基金项目(41501070、41271091、31400409);湖南科技大学校级科研项目(E51520)

Reconstruction of Snow Depth over the Tibetan Plateau Based on Muti-source Data

TANG Zhiguang1,*(), LI Hongyi2, WANG Jian2, LIANG Ji1, LI Chaokui1, CHE Tao2, WANG Xin1   

  1. 1. National-Local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
  • Received:2015-06-29 Revised:2015-10-28 Online:2016-07-15 Published:2016-07-19
  • Contact: TANG Zhiguang E-mail:tangzhg11@lzb.ac.cn

摘要:

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型(SnowModel)相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。

关键词: 青藏高原, 多源遥感数据, 雪深, 重建

Abstract:

Due to the variability and complexity of the topography in the Tibetan Plateau, the snow cover over most area of Tibetan Plateau is thin and revealing a high temporal and spatial heterogeneity. The passive microwave remote sensing greatly limits the precision of retrieved snow depth over the Tibetan Plateau, on account of its low spatial resolution and the uncertainty existed during snow depth retrieval. This paper attempts to reconstruct higher quality of snow depth data over the Tibetan Plateau through the fusion of multi-source remote sensing data, combining with a physic based snow model (SnowModel). This research mainly includes the following aspects: first of all, using the in-situ observed snow depth data and corresponding MODIS fractional snow cover data, the snow depletion curve of the study area is established. The MODIS fractional snow cover products (500 m) and passive microwave snow depth products (0.25°) are combined to produce the downscaled snow depth data (0.1°) using an empirical combination rule and the established snow depletion curve. Then, the downscaled snow depth data are assimilated into the SnowModel using the ensemble Kalman filter (EnKF) method. The accuracy of the downscaled snow depth data and the assimilated snow depth are analyzed through comparing them with the in situ observed snow depth data. The results show that there is an obvious depletion curving relationship between the snow depth and fractional snow cover area in the Tibetan Plateau. Using the root mean square error (RMSE) and correlation coefficients (R) as the evaluation standard, the assimilated snow depth is evaluated to be closer to the in-situ observed snow depth than the downscaled snow depth data.

Key words: Tibet Plateau, muti-source remote sensing data, snow depth, reconstruction