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Land use and water resources are closely linked. Every single type of land use has a different influence on the hydrologic cycle, consequently impacting the people and the natural resources. The use of advanced technologies, for example monitoring the agricultural resources with remote sensing, offers the possibility to assess the water demand, to know the total cultivated area with the precise distribution of crops and enables the regularly acquisition of data distributed in space and time. The citrus sub-sector is of paramount importance in the Tunisian agricultural sector. The Cap Bon region has the main production area with 75 % of the total citrus area. The possibility of classifying citrus crops is important for water resource management at regional scale and for economic stability. Given the socio-economic importance of the citrus sector in the Cap Bon region, it is very important to have accurate estimation of the total area of citrus plots in this region. Therefore, the main objectives of this current work are: To integrate multitemporal synthetic aperture radar SAR data, Sentinel-1, and optical data Sentinel-2, together to determine the best machine learning algorithm that allowed obtaining the most accurate citrus crop classification in the region. To study and analyze the temporal signatures of the Normalized Difference Vegetation Index (NDVI) of the classified crops, mainly the citrus, with the purpose to provide the maximum amount of information that allow the differentiation between the crops. To study the potential relation between NDVI and Actual Evapotranspiration (ETa) fluxes measured with the eddy covariance method for a citrus orchard to extrapolate the eddy tower measurements to greater scales. To achieve these objectives, we evaluated the performance of 22 nonparametric classifiers during the period September 2020–June 2021. Additionally, ET measured by the eddy covariance method was available for the same period, so we tried to find the potential relation between NDVI and Actual Evapotranspiration (ETa). The results revealed that the best performing classifier is the Support Vector Machine SVM with an accuracy around 91 %. Consequently, our results provided a significant contribution to the citrus classification in the Cap Bon region but can be further improved. Also, the obtained results highlighted the potential to extrapolate accurate ET estimation to larger scales using the vegetation index obtained from Sentinel-2 data.

Under climate change conditions, optimizing water resources management in rainfed agricultural production systems requires the reasonable choice of crops. In this context, the adoption of crops diversification is promoted to increase the agricultural production and the added value per cubic meter of rain water (green water) used by crops. Contributing, therefore, to increase agricultural production and to preserve soil and water resources. The objective of this study is: (i) to identify mixed crops within agricultural fields and, (ii) to evaluate the biomass production and the water productivity in the Lebna watershed (Cap-Bon, Tunisia) using remote sensing and field measurements. The study area, covering 210 km2, is characterized by the predominant of cereals, legumes and fodder cropping systems. The experiments allowed the quantification of crop evapotranspiration and the observed biomass production at the agricultural field plots. The use of the sentinel images and the observations at different agricultural fields allowed to produce NDVI maps. The results first confirmed a good correlation between biomass production and NDVI values. The exponential relationships showed a values of R2 greater than 0.7. The use of sentinel images and GIS allowed to compute water productivity from field to watershed scale. The results revealed a considerable spatial variation in water productivity values for different crops. Compared to a single crop, the cereal-legume mixture cropping improved the water productivity. The maximum value with 9.07 kg m−3 is observed for the mixture crops. The lowest value (0.12 to 2.40 kg m−3) was obtained for the cereal crop. These results help to recommend adaptation measures in agricultural production systems to climate change.

Estimating evapotranspiration in hilly watersheds is paramount for managing water resources, especially in semiarid/subhumid regions. The eddy covariance (EC) technique allows continuous measurements of latent heat flux (LE). However, time series of EC measurements often experience large portions of missing data because of instrumental malfunctions or quality filtering. Existing gap-filling methods are questionable over hilly crop fields because of changes in airflow inclination and subsequent aerodynamic properties. We evaluated the performances of different gap-filling methods before and after tailoring to conditions of hilly crop fields. The tailoring consisted of splitting the LE time series beforehand on the basis of upslope and downslope winds. The experiment was setup within an agricultural hilly watershed in northeastern Tunisia. EC measurements were collected throughout the growth cycle of three wheat crops, two of them located in adjacent fields on opposite hillslopes, and the third one located in a flat field. We considered four gap-filling methods: the REddyProc method, the linear regression between LE and net radiation (Rn), the multi-linear regression of LE against the other energy fluxes, and the use of evaporative fraction (EF). Regardless of the method, the splitting of the LE time series did not impact the gap-filling rate, and it might improve the accuracies on LE retrievals in some cases. Regardless of the method, the obtained accuracies on LE estimates after gap filling were close to instrumental accuracies, and they were comparable to those reported in previous studies over flat and mountainous terrains. Overall, REddyProc was the most appropriate method, for both gap-filling rate and retrieval accuracy. Thus, it seems possible to conduct gap filling for LE time series collected over hilly crop fields, provided the LE time series are split beforehand on the basis of upslope–downslope winds. Future works should address consecutive vegetation growth cycles for a larger panel of conditions in terms of climate, vegetation, and water status.

Water scarcity is already set to be one of the main issues of the 21st century, because of competing needs between civil, industrial, and agricultural use. Agriculture is currently the largest user of water, but its share is bound to decrease as societies develop and clearly it needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important, but translating this at the farm/landscape level presents considerable challenges. As we move up from the scale of cells, organs, and plants to more integrated scales such as plots, fields, farm systems, and landscapes, other factors such as trade-offs need to be considered to try to improve WUE. These include choices of crop variety/species, farm management practices, landscape design, infrastructure development, and ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyse approaches to addressing WUE at these different scales, including definitions of the metrics of analysis and consideration of trade-offs. The equations we present in this perspectives paper use similar metrics across scales to make them easier to connect and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Experimental Biology.)