Evaluation of a CONUS-wide ECOSTRESS DisALEXI evapotranspiration product

Kerry Cawse-Nicholson, Martha Anderson, Yang Yang, Yun Yang, Simon J. Hook, Joshua Fisher, Gregory H. Halverson, Glynn Hulley, Chris Hain, Dennis Baldocchi, Nathaniel A. Brunsell, Ankur Desai, Timothy Griffis, Kimberly Novick

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The Atmosphere-Land Exchange Inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ~1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This work evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions.

Bibliographical note

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  • Atmospheric modeling
  • evapotranspiration 30
  • Heating systems
  • Land surface
  • Land surface temperature
  • Soil
  • Stress
  • Temperature sensors
  • Validation


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