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Satellite-based Surface Energy Flux/Soil Moisture Estimation

The ALEXI Water and Energy Budget ModelSoil Moisture Retrievals
Drought Monitoring: United States, European & Nile River Studies

The ALEXI Water and Energy Budget Model

This research has been supported by NASA, NOAA, and via collaboration with the U.S. Department of Agriculture Agricultural Research Service (USDA ARS), since 2003. The goals of this research are the development of accurate daily land-surface energy fluxes at 5-10 km horizontal resolution, and flux climatology based on these daily fluxes. This research is unique in that the ALEXI model is driven primarily by remote sensing inputs, namely GOES-10 through -14 satellite radiances and cloud information, as well as Normalized Differenced Vegetation Index (NDVI) information derived with data from the Advance Very High Resolution Radiometer (AVHRR) polar orbiting satellites, and now the MODerate resolution Imaging Spectroradiometer (MODIS). Not only does our ALEXI model derive fluxes (sensible, latent, ground heat and net radiation) during "clear sky" conditions, yet new methods allow for flux estimations beneath cloud cover, provided a priori knowledge of previous-day flux partitioning, rainfall and soil moisture conditions.

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI), spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites.  An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions of on the order of 10 m.  The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the U.S. Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the U.S., Europe, Africa and other continents with geostationary satellite coverage.

Main Results
ALEXI was formulated as an extension to the two-source energy balance (TSEB) model of Norman et al. (1995), which in turn was developed to address many of the documented disadvantages of monitoring surface energy fluxes from remote sensing platforms. The two-source approximation treats the radiometric temperature (TRAD) of a vegetated surface as the ensemble average of the individual temperatures of the soil (Ts) and vegetation (Tc) components, partitioned by the fractional vegetation cover [f(φ)] apparent from the sensor view angle (φ). The TSEB separately balances energy budgets for the soil and vegetation components of the system, and also solves for total system fluxes of net radiation RN, LE, H, and G.

Regional application is achieved by coupling the TSEB with an atmospheric boundary layer (ABL) model to internally simulate land-atmosphere feedback on near-surface air temperature [Figure 1; Anderson et al. 1997]. In this coupled mode, the ALEXI model simulates air temperature (Ta) at the blending height internally within the ABL model, ensuring that Ta is consistent with the modeled surface fluxes from the TSEB. The TSEB is applied at two times during the morning hours, at approximately 1.5 hours after local sunrise (t1) and 1.5 hours before local noon (t2), using radiometric temperature estimates derived from a geostationary satellite platform (e.g., GOES, MSG, MTSAT). The ABL component of ALEXI relates the rise in Ta in the mixed layer over the time interval (t1 to t2) to the time-integrated influx of H from the surface, thus providing a means for energy closure from the TSEB [McNaughton and Spriggs 1986; Anderson et. al 1997]. McNaughton and Spriggs [1986] found a relationship between the rise in boundary layer height and potential temperature of the mixed layer with the time-integrated H from the surface. The coupling of the ABL within ALEXI is advantageous because it moves the TSEB upper boundary condition in temperature from the near-surface to the ABL, where conditions are more uniform at a spatial scale of a geostationary satellite thermal pixel. Further description of ALEXI and ancillary input requirements can be found in Anderson et al. [1997], Mecikalski et al. [1999] and Anderson et al. [2007a].

ALEXI is constrained to operate on spatial scales of 5-10 km, where atmospheric forcing by uniform land-surface behavior becomes effective.  Anderson et al. (2003) summarize ALEXI validation experiments employing a spatial flux disaggregation technique (DisALEXI; Norman et al. 2003), which uses air temperature diagnoses from ALEXI along with higher resolution TIR imagery presently only available from aircraft or polar orbiting systems such as Landsat, ASTER, or MODIS to downscale the GOES-based flux estimates (10 km resolution) to the flux measurement footprint (on the order of 100-1000 m). Typical root-mean-square-deviations in comparison with tower flux measurements (30-min averages) of H and lE are 35-40 Wm-2 (15% of the mean observed flux) over a range in vegetation cover types and climatic conditions. Disaggregation also facilitates high spatial resolution assessment of moisture flux and stress conditions, but is constrained in temporal resolution by the overpass frequency of the polar orbiting satellite. Together, ALEXI/DisALEXI facilitate scalable flux and moisture stress mapping using thermal imagery from a combination of geostationary and polar orbiting satellites, zooming in from the national scale to sites of specific interest.