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Anderson, MC, Norman, JM, Mecikalski, JR, Otkin, JA, Kustas, WP (2007). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 112(D11), D11112.

[1] Robust satellite-derived moisture stress indices will be beneficial to operational drought monitoring, both in the United States and globally. Using thermal infrared imagery from the Geostationary Operational Environmental Satellites (GOES) and vegetation information from the Moderate Resolution Imaging Spectrometer (MODIS), a fully automated inverse model of Atmosphere-Land Exchange (ALEXI) has been used to model daily evapotranspiration and surface moisture stress over a 10-km resolution grid covering the continental United States. Examining monthly clear-sky composites for April-October 2002-2004, the ALEXI evaporative stress index (ESI) shows good spatial and temporal correlation with the Palmer drought index but at considerably higher spatial resolution. The ESI also compares well to anomalies in monthly precipitation fields, demonstrating that surface moisture has an identifiable thermal signature that can be detected from space, even under dense vegetation cover. Simple empirical thermal drought indices like the vegetation health index do not account for important forcings on surface temperature, such as available energy and atmospheric conditions, and can therefore generate spurious drought detections under certain circumstances. Surface energy balance inherently incorporates these forcings, constraining ESI response in both energy- and water-limited situations. The surface flux modeling techniques described here have demonstrated skill in identifying areas subject to soil moisture stress on the basis of the thermal land surface signature, without requiring information regarding antecedent rainfall. ALEXI therefore may have potential for operational drought monitoring in countries lacking well-established precipitation measurement networks.



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