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The trade-off between increasing agricultural production and groundwater conservation, which is often the only irrigation source in arid regions, is a major challenge for water managers, decision-makers and the scientific community. Monitoring of the water use efficiency (WUE) provides a measure of the water lost from a canopy to the atmosphere relative to the biomass produced by the plants, and can be used to inform efforts that seek to reduce irrigation without impacting upon the yield. In this study, WUE was computed as the ratio between the gross primary productivity (GPP) and the evapotranspiration (ET). Both GPP and ET can be measured at the local scale using eddy covariance (EC) systems. However, in order to spatially evaluate WUE for larger areas or regions, local measurements are rarely representative. The spatiotemporal covariation of ET, GPP and WUE remains understudied, particularly at the scale of operational agroecosystems. In this study, Sentinel-2 optical bands were used to provide high spatiotemporal resolution estimates of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. A key soil water stress constraint in PT-JPL was linked to the Modified Soil-Adjusted Vegetation Index (MSAVI), while estimates of the GPP were delivered via the Vegetation Photosynthesis Model (VPM). In order to evaluate the water consumption and irrigation efficiency over an irrigated plantation of very high-density olive trees, ET, GPP and WUE were computed for a 1.5 year period, covering January 2019 to June 2021. Model estimates of both ET and GPP were compared to the EC measurements, with results providing a root mean square error (RMSE) of 63.3 W/m2 and a coefficient of determination (R2) of 0.74 for ET and 2.3 g C/m2/day and 0.73 for GPP, respectively. Based on these estimates, WUE was then calculated as the ratio of the GPP and ET. Relative to in situ measured data, the results were biased, with ET overestimated and GPP underestimated. An alternative measure for WUE was determined based on a linear regression model of meteorological data derived from ERA5-Land and vegetation indices derived from Sentinel-2 data. Overall, the best results were identified using an NDVI-based linear regression model, with an RMSE of 0.9 g C/Kg H2O and an R2 of 0.76. These results show the potential of high-resolution satellite data to assess WUE over agroecosystems, helping to overcome the limitations presented by in-situ measurements and supporting agricultural production and water resources management in water-limited regions.

Land Surface Temperature (LST) is a key variable used across various applications, including irrigation monitoring, vegetation health assessment and urban heat island studies. While satellites offer moderate-resolution LST data, unmanned aerial vehicles (UAVs) provide high-resolution thermal infrared measurements. However, the continuous and rapid variation in LST makes the production of orthomosaics from UAV-based image collections challenging. Understanding the environmental and meteorological factors that amplify this variation is necessary to select the most suitable conditions for collecting UAV-based thermal data. Here, we capture variations in LST while hovering for 15–20 min over diverse surfaces, covering sand, water, grass, and an olive tree orchard. The impact of different flying heights and times of the day was examined, with all collected thermal data evaluated against calibrated field-based Apogee SI-111 sensors. The evaluation showed a significant error in UAV-based data associated with wind speed, which increased the bias from −1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. Different surfaces, albeit under varying ambient conditions, showed temperature variations ranging from 1.4 to 6 °C during the flights. The temperature variations observed while hovering were linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. Irrigation and atmospheric conditions (i.e., thin clouds) also contributed to observed temperature variations. This research offers valuable insights into LST variations during standard 15–20 min UAV flights under diverse environmental conditions. Understanding these factors is essential for developing correction procedures and considering data inconsistencies when processing and interpreting UAV-based thermal infrared data and derived orthomosaics.

Understanding the plant water-uptake dynamics and behavior in olive orchards is an area of much interest, particularly for efforts to optimize the use and application of water resources. However, plant water-uptake information is rarely available or consistently monitored for large agricultural areas, especially in developing countries. Here we evaluate the potential of using synthetic aperture radar (SAR) images to monitor the water-uptake rate in super high-density olive orchards located in the hot and arid desert climate of Saudi Arabia. The experiment was performed using Sentinel-1 data acquired between 2019 and 2020 in concert with in situ sap flow measurements. To date, no study has explored the potential of Sentinel-1 SAR data to assess the water-uptake rate in olive orchards. The results demonstrate that the SAR backscatter increased between January and July/August, and then decreased during the remainder of the year, following a broadly Gaussian distribution. Of key interest is the strength of the relationship between the increasing and decreasing trends of SAR backscatter and the variation in the coincident water-uptake rate measured in situ, producing a coefficient of determination of 0.81 in the VV polarization. As the relationship between the SAR backscatter and water-uptake rate was established for plots with similar tree type and planting density, further exploration of orchards with different characteristics and planting densities is needed to fully understand the potential of SAR data to estimate plant water-uptake. Overall, the study demonstrates that SAR data can track variation in locally measured water-uptake and illustrates the potential to assist with enhancing irrigation management.

An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult in complex terrain. The main objective of this work was to investigate the potential of a Two-Source-Energy-Balance model (TSEB) driven by the Landsat and MODIS data for estimating ET over a complex mountain region. The complexity is associated with the type of the vegetation canopy as well as the changes in topography. For validating purposes, a large-aperture scintillometer (LAS) was set up over a heterogeneous transect of about 1.4 km to measure sensible (H) and latent heat (LE) fluxes. Additionally, two towers of eddy covariance (EC) systems were installed along the LAS transect. First, the model was tested at the local scale against the EC measurements using multi-scale remote sensing (MODIS and Landsat) inputs at the satellite overpasses. The obtained averaged values of the root mean square error (RMSE) and correlation coefficient (R) were about 72.4 Wm−2 and 0.79 and 82.0 Wm−2 and 0.52 for Landsat and MODIS data, respectively. Secondly, the potential of the TSEB model for evaluating the latent heat fluxes at large scale was investigated by aggregating the derived parameters from both satellites based on the LAS footprint. As for the local scale, the comparison of the latent heat fluxes simulated by TSEB driven by Landsat data performed well against those measured by the LAS (R = 0.69, RMSE = 68.0 Wm−2), while slightly more scattering was observed when MODIS products were used (R = 0.38, RMSE = 99.8 Wm−2). Based on the obtained results, it can be concluded that (1) the TSEB model can be fairly used to estimate the evapotranspiration over the mountain regions; and (2) medium- to high-resolution inputs are a better option than coarse-resolution products for describing this kind of complex terrain.

The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( σ v v ∘ ) and V H ( σ v h ∘ ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the σ v v ∘ was well correlated with SSM compared to the σ v h ∘ , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of σ v v ∘ were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about − 0.7 dB and − 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of σ v v ∘ observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and − 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products.