| Summary: | Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ET<sub>a</sub>) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ET<sub>a</sub> estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ET<sub>a</sub> estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ET<sub>a</sub> fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ET<sub>a</sub> of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ET<sub>a</sub> to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ET<sub>a</sub> to grass reference evapotranspiration (ET<sub>r</sub>), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ET<sub>a</sub> showed strong correlations against measured ET<sub>a</sub>, UAV- and Landsat 8-based METRIC ET<sub>a</sub>. Also, visual comparison of the MSDF-ET ET<sub>a</sub> maps was indicative of a promising performance of the method. In sum, the resulting ET<sub>a</sub> had a higher spatial resolution compared with thermal-based ET<sub>a</sub> without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ET<sub>a</sub> estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.
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