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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.