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United States Department of Agriculture

Agricultural Research Service

Title: A Vegetation Index Based Technique for Spatial Sharpening of Thermal Imagery

item Agam, Nurit
item Kustas, William - Bill
item Anderson, Martha
item Li, Fuqin
item Neale, Christopher

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/8/2007
Publication Date: 4/5/2007
Citation: Agam, N., Kustas, W.P., Anderson, M.C., Li, F., Neale, C. 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment. 107:545-558.

Interpretive Summary: High spatial resolution ('102 m) thermal infrared (TIR) band imagery has utility in a variety of applications in environmental monitoring, which include detecting conditions conducive to wildfire, assessing ecosystem health and drought severity, monitoring volcanic eruptive activity and exploring urban heat island effects. A common use of thermal data is to derive surface energy budgets, with high-resolution thermal providing assessments of evapotranspiration (ET) down to scales of individual agricultural fields and evaporative losses along canals and riparian corridors. This type of information is needed to reliably plan water distribution in the western U.S. as well as in other arid and semi-arid regions around the world. Satellite-based thermal datasets currently available reflect a tradeoff between temporal and spatial resolution such that the systems have either high-spatial/low-temporal resolution or low-spatial/high-temporal resolution. The continuation of thermal band imaging on Landsat Data Continuity Mission platforms is currently under debate, and such high resolution thermal data may soon be unavailable at any temporal resolution. A technique to derive higher resolution land surface temperature (LST) from other available data is therefore highly desirable. A technique for sharpening LST using empirically derived vegetation index (VI)-LST relationships is refined, exploring alternative sharpening basis functions and evaluating their performance over an extensive corn/soybean production area in central Iowa during a period of rapid crop growth. Sharpening as applied to satellite thermal imagery is simulated using high-resolution aircraft and Landsat imagery aggregated to coarser resolutions. The results prove a great potential in significantly enhancing the thermal information available over this agricultural area, and indicating the potential for routine monitoring of ET and stress conditions for the two important crops (corn and soybean) produced in this region.

Technical Abstract: High spatial resolution (~100 m) thermal infrared band imagery has utility in a variety of applications in environmental monitoring. However, currently such data have limited availability and only at low temporal resolution, while coarser resolution thermal data (~1000 m) are routinely available, but not as useful for identifying environmental features for many landscapes. An algorithm for sharpening thermal imagery (TsHARP) to higher resolutions typically associated with the shorter wavebands (visible and near-infrared) used to compute vegetation indices is examined over an extensive corn/soybean production area in central Iowa during a period of rapid crop growth. This algorithm is based on the assumption that a unique relationship between radiometric surface temperature (TR) relationship and vegetation index (VI) exists at multiple resolutions. Four different methods for defining a VI-TR basis function for sharpening were examined, and an optimal form involving a transformation to fractional vegetation cover was identified. The accuracy of the high-resolution temperature retrieval was evaluated using aircraft and Landsat thermal imagery, aggregated to simulate native and target resolutions associated with Landsat, MODIS, and GOES short- and longwave datasets. Applying TsHARP to simulated MODIS thermal maps at 1-km resolution and sharpening down to ~250 m (MODIS VI resolution) yielded root-mean-square errors (RMSE) of 0.67-1.35oC compared to the ‘observed’ temperature fields, directly aggregated to 250m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4 oC, while sharpening simulated GOES thermal maps from 5 km to 1 km and 250 m yielded RMSEs of 0.98 and 1.97, respectively. These results demonstrate the potential for improving the spatial resolution of thermal-band satellite imagery over this type of rainfed agricultural region. By combining GOES thermal data with shortwave VI data from polar orbiters, thermal imagery with 250-m spatial resolution and 15-min temporal resolution can be generated with reasonable accuracy. Further research is required to examine the performance of TsHARP over regions with different climatic and land-use characteristics at local and regional scales.

Last Modified: 08/22/2017
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