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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #284999

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Surface heterogeneity, measurement uncertainty, and the implications for using in-situ observations for model validation studies

item Alfieri, Joseph
item Kustas, William - Bill
item Anderson, Martha

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/26/2016
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: In-situ observations are critical for developing, calibrating, and validating the remote sensing-based models used to estimate and predict evapotranspiration (ET) along with the other components of the surface energy budget. Field measurements of the surface energy fluxes are collected using a variety of techniques including eddy covariance (EC), Bowen-ratio/energy balance (BREB), lysimetry (LY) and scintillometry (SC). Each of these methods is founded on a unique set of theoretical considerations and assumptions and, as a result, each method is susceptible to differing types of systematic and random errors that create uncertainty in the measurements. Unless steps are taken to characterize and account for measurement uncertainty, it will propagate into validation studies and, thereby, adversely impact both the accuracy and utility of model estimates of ET. Thus, it is essential that both the factors driving uncertainty for each the measurement techniques and the influence of this uncertainty on upscaling be fully understood. Focusing primarily on the effects of surface heterogeneity, the causes of the measurement uncertainty associated with various measurement techniques will be discussed using examples from a number of recent field studies. Additionally, the effects of measurement uncertainty on model validation will be explored using surface and airborne flux data collected during the 2002 International H2O Project (IHOP_2002) in combination with the Noah land surface model (Noah) and the thermal remote sensing-based ALEXI/DisALEXI model (ALEXI). An analysis of the airborne measurements linked variations in the surface conditions to the variability in the energy fluxes that approached 100 W m-2 across the study domain. A comparison of the airborne flux measurements and those computed by Noah showed this model not only tended to underestimate the moisture flux, it also failed to capture the spatial variability in observed in the airborne measurements due to its relatively coarse resolution (1 km). Conducting the simulations at higher resolutions (maximum resolution = 60 m) did improve the model results somewhat. In contrast, the ALEXI model, which produced output with a 60 m resolution, was able to match the spatial pattern and magnitudes of observed much more closely but still tended to underestimate ET slightly. Both analyses show that comparing model data to observations can lead to faulty conclusions if the measurement uncertainty of the observational data is not properly taken into account.