Remote sensing-based evapotranspiration (ET) can be derived using various methods, from soil moisture accounting to vegetation-index based approaches to simple and complex surface energy balance techniques. Due to the complexity of fully representing and parameterizing ET sub-processes, different models tend to diverge in their estimations. However, most models appear to provide reasonable estimations that can meet user requirements for seasonal water use estimation and drought monitoring. One such model is the Operational Simplified Surface Energy Balance (SSEBop). This study presents a formulation of the SSEBop model using the psychrometric principle for vapor pressure/relative humidity measurements where the “dry-bulb” and “wet-bulb” equivalent readings can be obtained from satellite-based land surface temperature estimates. The difference in temperature between the dry (desired location) and wet limit (reference value) is directly correlated to the soil-vegetation composite moisture status (surface humidity) and thus producing a fractional value (0-1) to scale the reference ET. The reference ET is independently calculated using available weather data through the standardized Penman-Monteith equation. Satellite Psychrometric Approach (SPA) explains the SSEBop model more effectively than the energy balance principle because SSEBop does not solve all terms of the surface energy balance such as sensible and ground-heat fluxes. The SPA explanation demonstrates the psychrometric constant for the air can be readily adapted to a comparable constant for the surface, thus allowing the creation of a “surface” psychrometric constant that is unique to a location and day-of-year. This new surface psychrometric constant simplifies the calculation and explanation of satellite-based ET for several applications in agriculture and hydrology. The SPA formulation of SSEBop was found to be an enhancement of the ET equation formulated in 1977 by pioneering researchers. With only two key parameters, improved model results can be obtained using a one-time calibration for any bias correction. The model can be set up quickly for routine monitoring and assessment of ET at landscape scales and beyond.