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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>MacKenzie Friedrichs</dc:contributor>
  <dc:contributor>Charles Morton</dc:contributor>
  <dc:contributor>Gabriel Edwin Lee Parrish</dc:contributor>
  <dc:contributor>Matthew Schauer</dc:contributor>
  <dc:contributor>Kul Bikram Khand</dc:contributor>
  <dc:contributor>Stefanie Kagone</dc:contributor>
  <dc:contributor>Olena Boiko</dc:contributor>
  <dc:contributor>Justin Huntington</dc:contributor>
  <dc:creator>Gabriel B. Senay</dc:creator>
  <dc:date>2022</dc:date>
  <dc:description>&lt;div id="abstracts" class="Abstracts u-font-serif"&gt;&lt;div id="ab0005" class="abstract author" lang="en"&gt;&lt;div id="as0005"&gt;&lt;p id="sp0080"&gt;&lt;span&gt;The estimation and mapping of actual&amp;nbsp;evapotranspiration&amp;nbsp;(ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from&amp;nbsp;Landsat&amp;nbsp;thermal imagery-using the Operational Simplified&amp;nbsp;Surface Energy&amp;nbsp;Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000&amp;nbsp;Landsat satellite&amp;nbsp;images were used to produce 10&amp;nbsp;years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly&amp;nbsp;Eddy Covariance&amp;nbsp;(EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&amp;nbsp;&amp;lt;&amp;nbsp;0.6) correlation on sparsely vegetated surfaces such as grasslands or woody&amp;nbsp;savanna&amp;nbsp;and stronger correlation (R&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&amp;nbsp;&amp;gt;&amp;nbsp;0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&amp;nbsp;of 0.82,&amp;nbsp;root mean square error&amp;nbsp;of 29&amp;nbsp;mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&amp;nbsp;&amp;gt;&amp;nbsp;0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and&amp;nbsp;U.S.&amp;nbsp;Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;ul id="issue-navigation" class="issue-navigation u-margin-s-bottom u-bg-grey1"&gt;&lt;/ul&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1016/j.rse.2022.113011</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Elsevier</dc:publisher>
  <dc:title>Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>