Submitted to: Journal of Geophysical Research Atmospheres
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/14/2007
Publication Date: 6/9/2007
Publication URL: http://handle.nal.usda.gov/10113/59923
Citation: Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A., Kustas, W.P. 2007. A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing. II. Surface moisture climatology. Journal of Geophysical Research. 112, D11112. http://dx.doi.org/10.1029/2006JD007507. Interpretive Summary: Robust satellite-derived moisture stress indices will be beneficial to operational drought monitoring, both in the US and globally. Standard drought indices, such as the Palmer Indices, are typically based on ground-based observations of antecedent precipitation. Rain gauge networks, however, tend to have limited spatial resolution, and can be very sparse or non-existent in many parts of the world. Simple satellite-based indices, such as the Vegetation Health Index (VHI), can misdiagnose stress signatures under certain energy-constraint conditions. We present a new “evaporative stress index” (ESI), generated using evapotranspiration (ET) estimates from the remote-sensing based Atmosphere-Land Exchange Inverse (ALEXI) model. ALEXI uses surface temperature and vegetation index data generated from thermal and shortwave satellite data to constrain a surface energy balance computation, estimating both ET and soil moistures conditions. The ESI is given by 1 minus the ratio of actual to potential evapotranspiration, with a value of 0 where AET=PET, and 1 where AET=0. Given its basis in an energy balance context, the ESI should be a more robust satellite-based indicator of drought conditions than is the VHI. This paper describes a 3-year climatological study of ET and moisture stress at 10km resolution over the continental U.S. The ESI shows good spatial and temporal correlation with the Palmer Drought Index and antecedent precipitation, but is available at significantly higher spatial resolution. The ESI even detects stress conditions that developed on the East Coast during 2002, under dense vegetation cover conditions, where microwave soil moisture mapping techniques tend to break down. The ALEXI model execution has been fully automated, and therefore the ESI has good potential for operational applications.
Technical Abstract: Robust satellite-derived moisture stress indices will be beneficial to operational drought monitoring, both in the US 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 ET and surface moisture stress over a 10-km resolution grid covering the continental United States. Examining monthly clear-sky composites for Apr-Oct 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. 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 based on 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.