Measurement Uncertainty and Model Validation: An Irrigated Cotton Case Study

Remote sensing-based models represent the only practical approach for monitoring evapotranspiration (ET) at the field and sub-field scales needed for agricultural applications including irrigation management and crop yield forecasts. However, the accuracy of these models relies on the quality of the in-situ measurements used for their development, calibration, and validation. Therefore, one key to properly assessing the utility of remote sensing-based models is understanding the limitations and uncertainty associated with the underlying observational data. To illustrate this, a case study comparing the output of the Two-Source Energy Balance (TSEB) model with a pair of independent datasets collected over irrigated cotton during the 2008 Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08) was conducted. The ET measurements from the two datasets, which differed by as much as 4.5 mm d^-1, were collected via eddy covariance (EC) and lysimetry (LY), respectively. When the uncertainty associated with the two observational datasets was neglected, the ET output from the TSEB model agreed with the EC data to within 2% while underestimating the LY data more than 15%. As a result, different conclusions could be drawn regarding the utility of the model depending on which observational dataset is used for the evaluation. However, when the sources of error (e.g. spatial variability within the measurement footprint) associated with the observational datasets were fully accounted for, the mean discrepancy between the two datasets decreased to 6% or 0.4 mm d^-1. More importantly, the model output agreed with both in-situ datasets to within their measurement uncertainties, which was nominally 10%. Thus, the case study results demonstrate both the uncertainty inherent in field measurements and the need to carefully consider the complex factors affecting ET and its measurement when using observational data for model validation.