TY - JOUR
T1 - A vegetation index based technique for spatial sharpening of thermal imagery
AU - Agam, Nurit
AU - Kustas, William P.
AU - Anderson, Martha C.
AU - Li, Fuqin
AU - Neale, Christopher M.U.
N1 - Funding Information:
This research was supported by Vaadia-BARD Postdoctoral Fellowship Award No. FI-371-2005 from BARD, The United States–Israel Binational Agricultural Research and Development Fund. Funding provided by NASA Grant NRA 00-OES-07 from the NASA Terrestrial Hydrology Program made the SMACEX project possible. Logistical support from the USDA-ARS National Tilth Lab and the SMEX02 project were critical in the success of the SMACEX field campaign.
PY - 2007/4/30
Y1 - 2007/4/30
N2 - 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 0.67-1.35 °C compared to the 'observed' temperature fields, directly aggregated to 250 m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4 °C, 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.
AB - 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 0.67-1.35 °C compared to the 'observed' temperature fields, directly aggregated to 250 m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4 °C, 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.
KW - Image sharpening
KW - Land surface temperature
KW - Radiometric temperature
KW - Spatial resolution
KW - Thermal remote sensing
UR - http://www.scopus.com/inward/record.url?scp=34047128598&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2006.10.006
DO - 10.1016/j.rse.2006.10.006
M3 - Article
AN - SCOPUS:34047128598
SN - 0034-4257
VL - 107
SP - 545
EP - 558
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 4
ER -