## 1 Introduction
\[\mathrm{LE}=\mathrm{R_{n}}-\mathrm{G}-\mathrm{H} \tag{2}\]  
where LE is the latent heat flux; \(\mathrm{R_{n}}\) is the net radiation flux; \(\mathrm{G}\) is the soil heat flux; and \(\mathrm{H}\) is the sensible heat flux. All terms in Equation (2) are given in \(\mathrm{W/m^{2}}\). The SEB LE flux is then converted to instantaneous crop \(\mathrm{ET_{a}}\) (e.g., mm/h) during the RS sensor overpass. There are two common methods to determine \(\mathrm{ET_{a}}\) using the SEB approach: (a) the one-source SEB (henceforth, OSEB), which considers the combined contributions of soil and vegetation to \(\mathrm{ET_{a}}\) rates [14; 15; 16; 17], and (b) the two-source SEB (or TSEB) that partitions heat fluxes and the crop \(\mathrm{ET_{a}}\) in a component related to the water transpired by the plants and another related to the evaporated water from the soil [18; 19; 20; 21]. The TSEB is a robust SEB approach suitable for estimating spatial \(\mathrm{ET_{a}}\) that was initially developed by [21]. The TSEB model has two different approaches for estimating the H flux in Equation (2): the parallel surface resistances TSEB (henceforth, TSEB\({}_{\mathrm{par}}\)) and the (in) series surface resistances TSEB (henceforth, TSEB\({}_{\mathrm{ser}}\)). The TSEB\({}_{\mathrm{par}}\) model considers the processes of heat transfer among plants, soil, and the air above the canopy as independent of each other with two surface resistances for heat transfer. The TSEB\({}_{\mathrm{ser}}\) method includes the concept of heat transfer interconnection in the soil-plant-atmosphere continuum through an additional surface resistance term and a parametrization of the aerodynamic surface temperature (T\({}_{\text{o}}\)) as a weighted-average temperature among soil, plant, and air temperatures with respective resistances as weights. Typical ET\({}_{\text{a}}\) estimation errors, when using TSEB\({}_{\text{par}}\) or TSEB\({}_{\text{ser}}\), were reported to be within 7% to 25% for row crops [22; 23; 24]. In regard to the desired frequency of ET\({}_{\text{a}}\) estimates, the study by [25] indicated that a four-day RS platform overpass frequency (of usable data) would be the minimum needed for current interpolation techniques to yield meaningful daily ET\({}_{\text{a}}\) estimates between acquired RS data. However, with high RS data acquisition frequencies, more reliable and accurate daily ET\({}_{\text{a}}\) estimations will be possible. Therefore, more timely and accurate irrigation water amounts would be delivered to surface and pressurized systems if accurate daily ET\({}_{\text{a}}\) maps were produced. Examining various RS platforms that offer multispectral images of cropland fields at diverse spectral and spatial resolutions is crucial for assessing the reliability of different ET\({}_{\text{a}}\) prediction algorithms and their accuracy when predicting ET\({}_{\text{a}}\) values in time and space [26]. Furthermore, accurate estimation of crop ET\({}_{\text{a}}\), when used to optimize the irrigation water amounts and timing of application, advances environmental sustainability by decreasing topsoil erosion in agricultural areas due to reduced field surface runoff and conserves water and soil nutrients within agricultural districts, protecting the environment by reducing groundwater withdrawn rates, maintaining ecological water table levels, and preserving adequate water quality of both aquifers and surface water bodies (e.g., lakes, artificial reservoirs, and rivers). However, there have been very few studies attempting to address the performance of the TSEB RS of ET\({}_{\text{a}}\) algorithms across different spectral and spatial scales. In a recent study, Ref. [27] explored the accuracy of the TSEB model developed by [21] using different small uncrewed aerial system (sUAS or drone) imagery pixel sizes, ranging from 0.10 m to 0.60 m, in a vineyard field located in California.