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The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately.

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Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky