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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula
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Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula

机译:利用伊比利亚半岛的营养反应对TRMM降水进行空间缩减

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Precipitation data with accurate, high spatial resolution are crucial for improving our understanding of basin scale hydrology. We explore the relation between precipitation estimates derived from the Tropical Rainfall Monitoring Mission (TRMM) and the normalized difference vegetation index (NDVI) for different spatial scales on the Iberian Peninsula in southern Europe, using time series from 2001 to 2007 Analysis shows that NDVI is a good proxy for precipitation. On an annual basis an exponential function best describes the relation between NDVI and precipitation. The optimum relation between NDVI and precipitation is found at an approximate scale of 75-100 km. This is an intermediate scale and it is likely that at smaller scales NDVI is determined primarily by anthropogenic land use and at larger scales factors such as geology, soils, and temperature play an increasingly important role. The fact that both TRMM and NDVI are subject to bias due to orbital deviations, atmospheric conditions and imperfect retrieval algorithms could also influence the scale dependency. The derived relation between NDVI and precipitation is used to develop a new downscaling methodology that uses coarse scale TRMM precipitation estimates and fine scale NDVI patterns. The downscaled precipitation estimates are subsequently validated using an independent precipitation dataset. The downscaling procedure resulted in significant improvements in correlation, bias, and root mean square error for average annual precipitation over the whole period, for a dry year (2005), and a wet year (2003).
机译:具有准确,高空间分辨率的降水数据对于增进我们对流域尺度水文学的理解至关重要。我们使用2001年至2007年的时间序列,探索了热带雨量监测任务(TRMM)得出的降水估计与欧洲南部伊比利亚半岛不同空间尺度的归一化植被指数(NDVI)之间的关系。降水的良好代表。每年,指数函数可以最好地描述NDVI与降水之间的关系。发现NDVI与降水之间的最佳关系约为75-100 km。这是一个中等规模,较小规模的NDVI可能主要由人为土地利用决定,而较大规模的因素(例如地质,土壤和温度)起着越来越重要的作用。由于轨道偏差,大气条件和不完善的检索算法,TRMM和NDVI均会产生偏差,这一事实也可能会影响尺度的依赖性。 NDVI与降水之间的推导关系用于开发一种新的降尺度方法,该方法使用了粗尺度TRMM降水估计和精细尺度NDVI模式。随后使用独立的降水数据集验证了降尺度的降水估计。降尺度程序使整个干旱年份(2005年)和潮湿年份(2003年)的年均降水量的相关性,偏差和均方根误差显着提高。

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