Location: Hydrology and Remote Sensing Laboratory
Title: Sharpening landsat thermal infrared imagery for mapping evapotranspiration at field scale Authors
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: September 4, 2012
Publication Date: October 25, 2012
Citation: Gao, F.N., Kustas, W.P., Anderson, M.C. 2012. Sharpening landsat thermal infrared imagery for mapping evapotranspiration at field scale[abstract]. 2012 Western States Remote Sensing of ET Workshop. 2012 CDROM. Technical Abstract: Thermal infrared (TIR) imagery provides estimates of land-surface temperature (LST) that can be used for mapping land surface energy fluxes and evapotranspiration (ET). For the ET mapping at the field scale, TIR data are required at relatively fine pixel resolution similar to the resolution of Landsat shortwave sensors (30 m). To improve spatial resolution in LST retrievals (60 m for ETM+ and 120 m for TM), a new data mining sharpener (DMS) has been recently developed. The DMS approach sharpens TIR imagery using relationships between LST and shortwave spectral band reflectance built from a data mining technique (regression tree technique). The DMS approach has been compared to the classic thermal sharpening technique (TsHARP) that related LST to fractional vegetation cover. A comparison of sharpening techniques was applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska. In general, the DMS model provides better estimates of the sharpened LST in applications using Landsat TM, ETM+ and airborne data from the different locations and seasons. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water, thin clouds or complex terrain. While the DMS technique can provide fine resolution Landsat TIR imagery, there are limitations to the sharpening ratios that can be reasonably implemented. Furthermore, subpixel variability in LST that are uncorrelated with signals captured in the shortwave bands will not be recovered (e.g., due to sub-pixel variations in surface moisture conditions). Consequently, sharpening approaches need to be further evaluated for ET mapping at field scale especially for the complex regions containing a patchwork of irrigated and non-irrigated fields and crop types. In this presentation, we will provide a detailed evaluation of the sharpening results using simulated Landsat TM and ETM+ data. The potential for the DMS approach to be used operationally will be discussed.