Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture

被引：81

作者：

Bolten, J. D. ^{[1
]}

Crow, W. T. ^{[2
]}

机构：

[1] NASA, Hydrol Sci Lab, Goddard Space Flight Ctr, Greenbelt, MD 20705 USA

[2] USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA

来源：

GEOPHYSICAL RESEARCH LETTERS | 2012年 / 39卷

关键词：

RETRIEVAL; INDEXES;

D O I：

10.1029/2012GL053470

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable. Citation: Bolten, J. D., and W. T. Crow (2012), Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture, Geophys. Res. Lett., 39, L19406, doi: 10.1029/2012GL053470.

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