Disaggregation of ALEXI Flux Predictions

ALEXI is constrained to work on large spatial scales of 5-10 km, where atmospheric forcing by uniform landsurface behavior becomes effective. This makes direct validation of ALEXI flux predictions difficult, as it is hard to obtain flux measurements on comparable scales.

The Disaggregated ALEXI (DisALEXI) algorithm uses high-resolution surface temperature and vegetation cover information to disaggregate the 5-10 km flux predictions from ALEXI down to the 30-300 m scale for direct comparison with ground-based measurements. DisALEXI constitutes a regional remote-sensing application of the two-source model of Norman et al. (1995).

Regional Application of the Two-Source Model

The two-source model, shown in Fig. 1, can be applied in two dimensions across a landscape, providing the upper boundary in air temperature is raised to a level where flux contributions from all landpatches are well-blended and conditions are relatively uniform across the modeling domain (see Fig. 6). The blending height will depend on the typical lengthscale of surface heterogeneity within the scene, but is generally on the order of 50 m.

Air temperature measurements near the blending height are not routinely available, and even if they were, there is no guarantee that they would be compatible with the remote surface temperature observations, which will probably have residual biases due to errors in atmospheric correction, sensor calibration, etc.

These types of errors can be disastrous to model flux estimates. Just a 1 deg C error in the assumed surface-to-air temperature gradient can translate into up to 100 Wm-2 error in the estimated sensible heat flux, depending on conditions. So instead of relying on local observations of air temperature to serve as an upper boundary, we model the air temperature at the blending height such that it is internally consistent with the remotely-sensed surface temperatures distribution. This air temperature estimate is provided by ALEXI.

Multi-Scale Mapping Procedure

The disaggegation procedure is two-staged (see Fig. 7):

In the first phase, the ALEXI model is run at low spatial resolution to model the air temperature at 50 m, at the interface between the surface and boundary layer submodels. This temperature is then held constant over the modeling domain while the two-source model is applied to high-resolution aircraft or satellite (Landsat, ASTER, MODIS) data.

As shown in Fig. 3, air temperature predictions from ALEXI adjust to time-independent biases in the remotely-sensed surface temperature data, leaving the true surface-to-air temperature difference relatively unaffected. To preserve this advantage in the disaggregation phase, the high-resolution radiometric temperature data are normalized with respect to the 5 km measurement used in the ALEXI phase.

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