Actual evapotranspiration is assessed via surface energy balance at an hourly rate. However, a robust estimation of daily evapotranspiration from hourly values is required. Outcomes of surface energy balance are frequently determined via measures of eddy covariance latent heat flux. Surface energy balance can be applied on images acquired at different times and spatial resolutions. In addition, hourly actual evapotranspiration needs to be integrated at a daily rate for operational uses. Questions arise whether the validation of surface energy balance models can benefit from complementary in situ measures of latent heat flux and sap flow transpiration. Here, validation was driven by image acquisition time, spatial resolution, and temporal integration. Thermal and optical images were collected with a proximity-sensing platform on an olive orchard at different acquisition times. Actual latent heat fluxes from canopy and sap flux at tree trunks were measured with a flux tower and heat dissipation probes. The latent heat fluxes were then further analyzed. A surface energy balance was applied over proximity sensing images re-sampled at different spatial resolutions with resulting latent heat fluxes compared to in situ ones. A time lag was observed and quantified between actual latent heat fluxes from canopy and sap flux at the tree trunk. Results also indicate that a pixel resolution comparable to the average canopy size was suitable for estimating the actual evapotranspiration via a single source surface energy balance model. Images should not be acquired at the beginning or the end of the diurnal period. Findings imply that sap flow transpiration can be used to measure surface energy balance at a daily rate or when images are found at an hourly rate near noon, and the existing time lag between the latent heat flux at the canopy and the sap flow at the trunk does not need to be taken into account.

Quantifying actual evapotranspiration (ET_a) over natural vegetation is crucial in evaluating the water status of ecosystems and the water-use patterns in local or regional hydrological basins. Remote sensing-based surface energy balance models have been used extensively for estimating ET_a in agro-environments; however, the application of these models to natural ecosystems is still limited. The surface energy balance algorithm for land (SEBAL) physical-based surface energy balance model was applied to estimate the actual evapotranspiration over a heterogeneous coverage of Mediterranean maquis in a natural reserve in Sardinia, Italy. The model was applied on 19 Landsat 5 and 8 images from 2009 to 2014, and the results were compared to the data of a micrometeorological station with eddy covariance flux measurements. Comparing the SEBAL-based evaporative fraction (Λ_S) to the corresponding tower-derived evaporative fractions (Λ_T) showed good flux estimations in the Landsat overpass time (Coefficient of determination R² = 0.77, root mean square error RMSE = 0.05 and mean absolute error MAE = 0.076). Three methods were evaluated for upscaling instantaneous latent heat flux (λE) to daily actual evapotranspiration (ET_a,D). The upscaling methods use the evaporative fraction (Λ), the reference evapotranspiration fraction (EF_r) and the ratio of daily to instantaneous incoming shortwave radiation (Rs₂₄/Rs_i) as upscaling factors under the hypothesis of diurnal self-preservation. A preliminary analysis performed using only in-situ measured data demonstrated that the three factors were relatively self-preserved during the daytime, and can yield good ET_a,D estimations, particularly when obtained at near the Landsat scene acquisition time (≈10:00 UTC). The upscaling factors obtained from SEBAL retrieved instantaneous fluxes, and some ancillary measured meteorological data were used to upscale SEBAL-estimated instantaneous actual λ to daily ET. The Λ EFr and Rs₂₄/Rs_i methods on average overestimated the measured ET_a,D by nearly 20, 61 and 18%, respectively. The performance of the Λ and Rs₂₄/Rs_i methods was considered satisfactory, bearing in mind the high variable ground cover and the inherent variability of the biome composition, which cannot be properly represented in the Landsat moderate spatial resolution. In this study, we tested the potential of the SEBAL model application in a complex natural ecosystem. This modeling approach will be used to represent the spatial dynamics of ET, which will be integrated into further environmental and hydrological applications.

In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R² = 0.84; RMSE = 0.103 cm³ cm⁻³), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems.