Wednesday, 9 January 2019: 10:45 AM
North 127ABC (Phoenix Convention Center - West and North Buildings)
Evapotranspiration (ET) from vegetation is an important hydrologic process used to establish the water balance in basins, estimate water demand of irrigation systems, as well as crop biomass production and yield at field scales. Thermal remote sensing can be an effective tool for mapping ET using the physical connection that exists between land surface temperature (LST) and evaporative cooling and the concepts of the Two-Source Energy Balance (TSEB) approach. The information needed to map high spatial resolution ET at higher frequency cannot be achieved by one satellite system alone. High frequency geostationary satellites generally have low spatial resolution (>1 km, 15 min) while moderate/high spatial resolution polar orbiting thermal imaging systems have infrequent repeat times (1km/30m, daily/16 day). The Atmosphere Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) multi-scale ET and energy balance mapping system exploits this range in thermal imaging capacity. Combined, they enable a data fusion approach that optimizes the characteristics of both systems to provide high spatial and temporal resolution ET coverage. The ALEXI ET model specifically uses time differential LST measurements from geostationary or moderate resolution polar orbiting platforms to generate regional ET maps, reducing sensitivity to errors in single overpass temperature retrieval. The DisALEXI model then disaggregates the regional ALEXI ET to finer scales using Landsat (30 m; biweekly). The DisALEXI modeling suite employs the Data Mining Sharpener (DMS) technique to sharpen Landsat’s thermal infrared (TIR) data from its native resolution of 60-120 m to the 30 m resolution of the visible/near infrared bands. The MODIS/VIIRS/Landsat disaggregated ET is then fused to generate daily ET maps at 30m resolution, capable of resolving individual farm fields. Here we describe a new satellite-based daily ET product that was developed for the Middle East and North Africa (MENA) region, using the ALEXI/DisALEXI modeling suite consisting of a regional product (375 m) estimation based on the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor and a high-resolution field scale disaggregation (30 m) of the ALEXI product using the Landsat platform. Estimation of ET at field scales using the DisALEXI downscaling approach has been developed over the past 15 years in various open (Fortran and Perl) and proprietary (IDL and Modtran) software programs and as such is difficult to share with colleagues and stakeholders. Therefore, a pythonic implantation of the DisALEXI disaggregation scheme has been developed (PyDisALEXI) to downscale the regional ALEXI ET to field scale resolutions using the Landsat platform. A description and initial results from the new open source algorithm will be shown over the MENA and CONUS regions.
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