1059 An Open Source Implementation of the DisALEXI ET Data Fusion Suite

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Mitchell Andrew Schull, CICS, College Park, MD; and C. Hain, M. C. Anderson, F. Gao, C. M. U. Neale, and X. Zhan

Handout (18.2 MB)

Thermal remote sensing can be an effective tool for mapping evapotranspiration (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 absolution temperature retrieval.  The DisALEXI model then disaggregates the regional ALEXI ET to finer scales using MODIS (1km) or VIIRS (375m; both near daily) or 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.  To date the DisALEXI data fusion package has relied on proprietary software and is therefore not easily transferable to the ET community and water management stakeholders.  The purpose of the presented work is to illustrate a new completely open source implementation of DisALEXI called PyDisALEXI.  As the names indicates the new open source platform relies on Python.  PyDisALEXI is built on the core python modules of NumPy, Pandas and SciPy as well as other highly specific modules (landsat-util, Pydap, Pytroll etc.,) that build on the core modules.  The basic framework of the new platform is presented as well as the implementation and some initial results.
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