Ground-based measurements of land-surface temperature (LST) performed in a homogeneous site of rice crops close to Valencia, Spain, were used for the validation of the calibration and the atmospheric correction of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) thermal band. Atmospheric radiosondes were launched at the test site around the satellite overpasses. Field-emissivity measurements of the near-full-vegetated rice crops were also performed. Seven concurrences of Landsat-7 and ground data were obtained in July and August 2004-2007. The ground measurements were used with the MODTRAN-4 radiative transfer model to simulate at-sensor radiances and brightness temperatures, which were compared with the calibrated ETM+ observations over the test site. For the cases analyzed here, the differences between the simulated and ETM+ brightness temperatures show an average bias of 0.6 K and a rootmean-square difference (rmsd) of ±0.8 K. The ground-based measurements were also used for the validation of LSTs derived from ETM+ at-sensor radiances with atmospheric correction calculated from the following: 1) the local-radiosonde profiles and 2) the operational atmospheric-correction tool available at http://atmcorr.gsfc.nasa.gov. For the first case, the differences between the ground and satellite LSTs ranged from -0.6 to 1.4 K, with a mean bias of 0.7 K and an rmsd = ±1.0 K. For the second case, the differences ranged between -1.8 and 1.3 K, with a zero average bias and an rmsd = ±1.1 K. Although the validation cases are few and limited to one land cover at morning and summer, results show the good LST accuracy that can be achieved with ETM+ thermal data.

A split-window equation is derived for land surface temperature, yielding T = T₄ + A(T₄ − T₅) + B(ε), where T is the true surface temperature, T₄ and T₅ are the brightness temperatures measured in AVHRR channels 4 and 5, A is a coefficient related to the atmospheric transmittances in AVHRR channels 4 and 5, being dependent on the atmosphere type and independent on surface emissivity, and B(ε) takes into account the emissivity effect, which depends on both the channel surface emissivities (ε₄ and ε₅) and the atmosphere type. The atmospheric dependence of split-window coefficients, A and B(ε), is discussed by means of satellite measurements simulations and in situ data. It is shown that linear, global-scale algorithms can produce inaccurate estimates of surface temperature when they are applied to areas of reduced atmospheric variability. A simple, non-linear split-window algorithm has been proposed, in which A = a₀+ a₁(T₄− T₅). The constants a₀and a₁ have been calculated from a set of in situ and coincident satellite measurements of sea surface temperatures taken over a wide range of latitudes. A linear split-window algorithm optimized for mid-latitude conditions has been also derived. The combined effects of atmospheric attenuation and surface emissivity on T₄ − T₅ have been discussed. Moreover, the theoretical results derived are applied to a NOAA-9 image, for which a procedure for estimating the channel emissivity difference (ε₄ − ε₅) using satellite sensor data is suggested.