Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/11/2012
Publication Date: 11/11/2012
Publication URL: http://handle.nal.usda.gov/10113/58483
Citation: Bolten, J., Crow, W.T. 2012. Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture. Geophysical Research Letters. 39(19):L19406. Interpretive Summary: The ability to accurately monitor the onset of agricultural drought (i.e., a lack of root-zone soil water for adequate crop and forage production) at large scales is critical for drought mitigation and humanitarian applications. Recent research has focused on the use of surface soil moisture retrievals acquired from satellite-based microwave radiometers to enhance global agricultural drought monitoring efforts. However, the added benefit associated with leveraging such observations has not yet been quantified. This paper applies a novel evaluation technique (based on remotely-sensed vegetation indices) in order to quantify the added benefit of satellite-based surface soil moisture retrievals for global-scale agricultural drought monitoring. Results show clear added benefits - especially in data-poor areas prone to food insecurity (e.g. the Horn of Africa) - and provide strong evidence for the potential utility of microwave remote sensing for this key agricultural application.
Technical Abstract: The additive 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 model-based soil moisture obtained before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). 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 soil moisture retrievals substantially improves the performance of a global drought monitoring system particularly in data-poor areas of the world where high-quality rainfall observations are unavailable.