Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ET_a) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ET_a 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_a 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_a fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ET_a 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_a 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_a to grass reference evapotranspiration (ET_r), 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_a showed strong correlations against measured ET_a, UAV- and Landsat 8-based METRIC ET_a. Also, visual comparison of the MSDF-ET ET_a maps was indicative of a promising performance of the method. In sum, the resulting ET_a had a higher spatial resolution compared with thermal-based ET_a 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_a 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.

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day⁻¹, coefficient of determination (R²) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.

To bypass the thermal data requirement for actual evapotranspiration (ETa) estimation in satellite remote sensing, two general approaches have been taken into practice based on previous efforts: (1) Multi-sensor data fusion for thermal sharpening and (2) the use of the process-based models such as the Penman-Monteith and Shuttleworth-Wallace equations augmented with satellite-based crop parameters. To address this issue, this study introduced an optical satellite data-based ETa estimation model, OPTRAM-ET, based on the optical trapezoid model (OPTRAM) estimates of soil moisture. The new model has been applied to Sentinel-2 and Landsat-8 images over 16 eddy covariance flux towers in the United States and Germany. The flux towers were chosen in a way to cover different ranges of landcover types, e.g., agriculture, orchard, permanent wetlands, and foothill forests. In order to assess the model in comparison to a thermal-based conventional method, the land surface temperature (LST)-vegetation index (VI) model was utilized. The results of the proposed OPTRAM-ET model showed promising performance in all the studied regions. While agricultural sites showed higher correlation due to their wider range of ETa values, error indicators were lower in foothill forests because soil moisture changes were smaller compared to irrigated and wet lands. In addition, the OPTRAM-ET model showed comparable performance to the conventional LST-VI model. The OPTRAM-ET model however does not need thermal data, and it benefits from higher spatial and temporal resolution data provided by ever-increasing drone- and satellite-based optical sensors to predict crop water status and demand. It is worth noting that the thermal sharpening step was excluded in this model which subsequently makes the model substantially less computationally demanding than a thermal-based model. Unlike the LST-VI model, which needs to be calibrated for each satellite image, a temporally-invariant region-specific calibration is possible in the OPTRAM-ET model. Importantly, the model requires further enhancement due to limitations caused by the simplified basic assumptions.