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有读书笔记A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China

唐唐 添加于 2011-12-6 04:41 | 2864 次阅读 | 0 个评论

  •  作 者

    Jia S, Zhu W, Lű A, Yan T

  •  摘 要

    The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present astatisticaldownscalingalgorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying adownscaling methodology, TRMM 3B43 0.25° × 0.25° precipitation fields were downscaled to 1 × 1 km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscalingalgorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11 years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r2 ranging from 0.72 to 0.96 at six different raingauge stations.

  •  详细资料

    • 文献种类: Journal Article
    • 期刊名称: Remote Sensing of Environment
    • 期刊缩写: Remote Sensing of Environment
    • 期卷页: 2011年  第115卷 第12期 3069-3079页
    • ISBN: 0034-4257

  • 学科领域 自然科学 » 地球科学

  •  所属群组

    环境科学  

  • 相关链接 DOI URL 

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    遥感降水降尺度研究获进展
    相对于其它遥感降雨产品而言，TRMM降雨产品由于其较高的准确性，已在全球多个区域得到广泛运用。然而，当运用到区域尺度时，由于其空间分辨率较低（0.25°×0.25°），往往无法满足流域尺度水文水资源研究的需要。空间降解是解决上述问题的有效办法。
     
    中科院地理科学与资源研究所贾绍凤研究团队以柴达木盆地降雨与地形、植被等其他环境因子的 密切关系为基础，在四个不同的空间尺度上建立了TRMM降雨数据与SRTM DEM和SPOT VEGETATION两个高分辨率遥感数据的回归关系，最终得到了柴达木盆地高空间分辨率（1km×1km）的年降雨数据，并利用研究区内的6个降雨站点 数据进行了验证。结果表明，研究人员建立的空间统计降解模型能够有效地反映研究区的降雨量的年变化趋势，取得了较好的效果。
     
    研究成果发表在期刊Remote Sensing of Environment上(Jia Shaofeng , Zhu Wenbin, L? Aifeng* , Yan Tingting. A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment, 2011.（12）：3069-3079 )。（来源：中科院地理科学与资源研究所）
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