Laboratory experiments show a linear relationship between the total heat flux from a water surface to air and the standard deviation of the surface temperature field, σ, derived from thermal images of the water surface over a range of heat fluxes from 400 to 1800 Wm -2 . Thermal imagery and surface data were collected at two power plant cooling lakes to determine if the laboratory relationship between heat flux and σ exists in large heated bodies of water. The heat fluxes computed from the cooling lake data range from 200 to 1400 Wm -2 . The linear relationship between σ and Q is evident in the cooling lake data, but it is necessary to apply band pass filtering to the thermal imagery to remove camera artifacts and non-convective thermal gradients. The correlation between σ and Q is improved if a correction to the measured σ is made that accounts for wind speed effects on the thermal convection. Based on more than a thousand cooling lake images, the correlation coefficients between σ and Q ranged from about 0.8 to 0.9.

An apparatus to measure the skin temperature and related variables on inland lakes is described. The apparatus is a transparent frame with sensors to measure the skin and bulk water temperature, the wind velocity, and the air temperature and humidity for periods of several days. The sensors are positioned within 1 m of the air-water interface and sample boundary layer variables every 2 s.Data for a 4-h period at midday are discussed, and the vertical fluxes of heat and momentum are calculated using bulk relationships for 1-and 5-min periods. It is shown that the measured water temperature at a depth of 1 cm correlates well with estimates based on the bulk heat flux.The skin temperature depression is calculated from the bulk heat and momentum fluxes and is found to vary between 0.4°and 0.5°C for the 4-h period and was in good agreement with the measured values. However, the calculated and measured skin temperatures were poorly correlated for both the 1-and 5-min averages. This is believed to be due to departures from the steady-state assumptions or to deficiencies in the theory.

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Until recently, most thermal infrared measurements of natural scenes have been made at disparate scales, typically 10 -3 -10 -2 m (spectra) and 10 2 -10 3 m (satellite images), with occasional airborne images (10 1 m) filling the gap. Temperature and emissivity fields are spatially heterogeneous over a similar range of scales, depending on scene composition. A common problem for the land surface, therefore, has been relating field spectral and temperature measurements to satellite data, yet in many cases this is necessary if satellite data are to be interpreted to yield meaningful information about the land surface. Recently, three new satellites with thermal imaging capability at the 10 1 -10 2 m scale have been launched: MTI, TERRA, and Landsat 7. MTI acquires multispectral images in the mid-infrared (3-5µm) and longwave infrared (8-10µm) with 20m resolution. ASTER and MODIS aboard TERRA acquire multispectral longwave images at 90m and 500-1000m, respectively, and MODIS also acquires multispectral mid-infrared images. Landsat 7 acquires broadband longwave images at 60m. As part of an experiment to validate the temperature and thermal emissivity values calculated from MTI and ASTER images, we have targeted the summit region of Mauna Loa for field characterization and near-simultaneous satellite imaging, both on daytime and nighttime overpasses, and compare the results to previously acquired 10 -1 m airborne images, ground-level multispectral FLIR images, and the field spectra. Mauna Loa was chosen in large part because the 4x6km summit caldera, flooded with fresh basalt in 1984, appears to be spectrally homogeneous at scales between 10 -1 and 10 2 m, facilitating the comparison of sensed temperature. The validation results suggest that, with careful atmospheric compensation, it is possible to match ground measurements with measurements from space, and to use the Mauna Loa validation site for cross-comparison of thermal infrared sensors and temperature/emissivity extraction algorithms.