The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices(VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agriculturaldrought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modernland surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisturecross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained fromcomplex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to asimplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relativeterms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired byaveraging across a multi-model ensemble.
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