Work Packages

The project activities are articulated in the following Work Package:


Task 0.1 – Project coordinating and monitoring

This task aims to monitor and control the project during its execution. It includes the following: follow up activities/tasks execution and update the project plan;  guarantee that the project is running on time, on budget and on scope; scheduling of periodic partners meetings; data management plan which addresses crucial management aspect of data that will be collected and produced in the course of the project.

Task 0.2 – Deployment of Pilots

This task aims at deploying the project actions in each selected pilot areas in Spain, Italy, Lebanon and Jordan, enabling to address actual needs of users and stakeholders as well as to fine-tune the pilot’s activities. Feedback collected from users during the pilot campaigns will be validated in collaboration with all partners during scheduled partners meetings.



Task 1.1: Mapping irrigated areas

The accurate identification of irrigated areas is not a trivial issue. This information is seldom available with the precision that would be required for a reliable planning of water resources (Al-Bakri et al., 2016). In most cases, data are aggregated (not geolocated) and not up-to-date. Multitemporal EO data can be processed for discriminating crops accordingly to their seasonal development or phenology. This is usually based on monitoring of seasonal pattern of changes in leaf area index (LAI) as measured by vegetation index.

Keeping this in mind, the detection of irrigated areas can be made assuming that in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation (Lockwood, Sarteel, Mudgaln, Osann, & Calera, 2014). Practically, this means that a detailed knowledge of the spatial distribution of the different crops is not required. Rather, it is enough to take into account the temporal evolution of spectral indices representing the vegetative vigor, such as NDVI. From an operational point of view, the procedure consists of the following stages:

  • Production of multi-time series of vegetation index NDVI maps;
  • Masking of areas not of interest, typically urban, mountain and wetlands such as rivers, lakes and water basins;
  • Segmentation and object based unsupervised classification (clustering) applied to time series of NDVI index maps.
  • Automatic extraction of temporal NDVI index pattern.
  • Labelling of areas with active vegetation growth, with a NDVI pattern compatible only with irrigation;
  • Supervised classification; this is done using such training pixels of irrigated areas identified via the multi-time classification of NDVI index combined with on field inspection and ground trues; this phase is aimed at improving the accuracy of the unsupervised classification.
  • Mapping of irrigated areas along with possible integration with other GIS data.

In crop types where the use of EO is less adequate (e.g. woody crops with sparse ground cove, like vine or olive), ancillary information, like cartography based on very high resolution orthophotos, can be used to identify the plots where this practice occurs.

Task 1.2: Mapping crop development and irrigation requirement

The processing chain for crop water requirements (as implemented in IAS) include the derivation of canopy parameters which are relevant in the estimation of crop water requirements such as vegetation indexes, crop coefficients, Leaf Area Index and surface albedo. These canopy parameters together with meteorological data (either from ground-based stations either from reanalysed climatic models) represent the input for standard procedures of crop water requirements calculations such as FAO-56 (Allen et al., 1998). The procedures embedded in the mentioned services are considering the evapotranspiration ETp of fully irrigated crops, i.e. growing under standard conditions as defined by FAO Paper 56. The maps ETp can be calculated accordingly to FAO-56 by knowing meteorological data and the crop parameters (albedo, crop height and Leaf Area Index, or the so-called crop coefficient). Maps of these crop parameters can be obtained from the processing of satellite images. This approach has been validated by using micrometeorological techniques in well-watered plots (D’Urso et al., 2010), as well as cross-comparison of different methods in irrigated crops (Rubio et al., 2005). A review of the procedures – which will also be applied in this project- can be found in Vuolo et al. (2015) and Calera et al. (2017).

In our experience, most farmers are not only interested in knowing the irrigation volumes but also the spatial and temporal distribution of some crop characteristics (i.e. crop vigour) over the growing season for each one of their fields; the information on canopy development, which is the result of all agronomic practices, can also be used for a better management of all crop inputs.

From the water managers perspective, this information has to be aggregated at the irrigation scheme level in order to reduce operation and maintenance (O&M) costs, depreciation of infrastructures and administration costs. At irrigation district and catchment scale, the knowledge of the spatial distribution of crop water requirements enables the water manager to better allocate the intra-scheme water distribution and to monitor and control the water exploitation plans.

Task 1.3: Mapping actual evapotranspiration

When water stress conditions may occur, additional information i.e. related to soil water status is needed. However, the occurrence of crop water stress conditions can be assessed by means of remote sensing, i.e. in the thermal and spectral microwave regions, without additional input concerning soil properties and irrigation scheduling.

Actual evapotranspiration ETact (diversely from the evapotranspiration under standard conditions ETp used for Task 1.2) can be determined by applying surface energy balance methods based on satellite observations in the thermal region (Bastiaanssen et al., 1998; Allen et al., 2007; Kustas and Anderson, 2009). These methods estimate the instantaneous value of actual evapotranspiration at the time of satellite acquisition (for Landsat around 10:00 AM at Mediterranean latitudes); this value is the extrapolated to the corresponding day by means of additional data and assumptions. These methods have been widely used in assessing the water accounting in many irrigated areas, but their use is constrained by the limited spatial and temporal resolution of EO-based thermal observations (100 m resolution is only provided by Landsat 8 sensors).

Very recently an experimental study based on shortwave infrared observations (SWIR) available from Sentinel-2 (bands 11 and 12, resolution 20 m) and Landsat-8 (bands 6 and 7, resolution 30 m) has evidenced the significative correlation between the spectral response at these wavelengths with soil water status (Sadeghi et al., Remote Sensing of Environm., 2017). This approach – still needing further validation exercises – looks very promising to evaluate a water stress coefficient for reducing the crop evapotranspiration evaluated for standard condition to the actual value. The classical Penman-Monteith approach which constitutes the basis of FAO-56 model can hence be further developed to assimilate SWIR data in the ET estimation based on the methodology described in Task 1.2.

Results will be also compared with results from remote sensing based models that calculates actual ET (ETa). In Jordan, the Surface Energy Balance Algorithm for Land (SEBAL) will be used to obtain ETa that will be compared with the theoretical ET values produced by FAO-56 method.



Task 2.1: Mapping water productivity from crop models

The water productivity (Wpet) can be expressed by the harvestable grain yield (Yact) per unit of total water consumption, expressed as the evapotranspiration accumulated from crop establishment to harvest. This formulation has the advantage of expressing the water unit of the equation as a true consumption; the evapotranspiration process withdraws water from soil into the atmosphere, water that is no longer available for downstream users.

The FAO AquaCrop simulation model (Steduto et al., 2009) provides a sound theoretical framework to investigate crop yield response to environmental stress. This model has successfully simulated crop growth and yield as influenced by varying soil moisture environments for different crop types. The relatively small number of input data describes the soil–crop–atmosphere environment in which the crop develops, most of which can be derived by simple methods. AquaCrop simulates crop growth and yield based on the water-driven growth model that relies on the conservative behavior of biomass per unit transpiration relationship (Steduto et al., 2007).

However, the inherent uncertainty of the model deriving from input parameters – concerning especially the soil hydraulic properties or the actual irrigation volumes applied – can be reduced by assimilating observations such as the Leaf Area Index and fractional vegetation cover. The open-source version AQUACROP-OS (Foster et al., 2017) allows an easy integration with satellite-based canopy parameters. A validation of the assimilation procedure and the improvement that can derive in the accuracy of Aquacrop results will be carried out in selected plot where all the relevant input data are accurately measured. The validation exercise will provide a very useful insight for extending the approach in other area with limited availability of input data.

Task 2.2: Mapping water productivity from EO-based actual evapotranspiration

Although the combination of crop models with Earth Observation data is a powerful support for irrigation management in water scarce areas, its full implementation is feasible at pilot fields or technically advanced farmers. Given the definition of water productivity in the Task 2.1, and considering that the actual evapotranspiration is obtained in Task 1.3, water productivity can be derived if the harvestable yield is known. There are several models for calculating the total dry biomass and harvestable yield from EO data (in particular NDVI), such as the WATPRO model developed by Zwart et al. (2010). This approach has been further elaborated by FAO for producing the WaPOR database (F.A.O., 2017) from medium resolution data such as MODIS (250 m). In this project we will apply this approach to the Sentinel-2  data, with geometric resolution of 20 m. A cross-comparison with the values resulting from the application of Aquacrop (Task 2.1) and the WaPOR database will be carried out.



WP3 will utilise the product developed for analysing the irrigation performance. This will be done by means of a participatory evaluation of stakeholders for the considered pilot campaigns. The task will be the Benchmarking of irrigation performance. Additional analysis will include a cost-benefit analysis for the adoption of new technologies in selected pilot areas.

Task 3.1: Assessing irrigation performance and accounting

Performance assessment is an essential component of effective irrigation management. Regular feedback of information from the field into water management decision making can substantially improve the performance of water delivery services. However, obtaining repeated objective evaluations about actual field conditions is difficult. The data derived by using EO and crop models from previous tasks can be analysed in conjunction with measurements of delivered volumes for monitoring and assessing the performance of irrigation distribution systems. Regular performance assessments are necessary to maintain accountability, in order to assure that service specifications are met. Information from EO can play an important role in determining whether the actual service provided meets the targeted specification, with the important advantage that remotely sensed data is objective and unbiased. By means of the information derived in this project it will be possible to establish water accounting procedures.

In presence of limited flexibility in operations (i.e. fixed schedule), such monitoring would assist managers to identify persistent deviations from scheduled deliveries enabling more rapid diagnosis of causes of deviations from target.

Irrigation system water allocations are, most often, based on assumptions about the irrigated area, crop types, and the near-surface meteorological conditions that determine crop water requirements. Hydraulic designs for canals are based on the peak flow rate required to meet some minimum fraction of the crop water requirement. The information derived in previous tasks of this project enables regular updating of irrigated areas that tend to deviate substantially from the original estimates of irrigation command areas. Cropping patterns may have changed due to influences of market mechanisms, water logging, or scarcity due to restricted or unreliable canal water supply. Even in demand-based systems, knowledge from field conditions, including crop stress, can help managers forecast how much water should be released from the reservoir.



The management of dissemination and use will rely on the same regionalized structure as the overall coordination. The coordinator will be the overall responsible person, with regional dissemination and use managers appointed for each target country (coinciding with our regional managers for the sake of simplicity).

The website will be the core communications hub. It will be conceived as a portal of entry for all stakeholders, the general public, and the scientific community to the project itself, to a range of topical pages, and to the sample EO-assisted products generated for participatory evaluation with stakeholders. It will also hold an e-library of related documents.

Social media and blogs will be used, mainly focusing on Linked-in as professional network and twitter.

Conferences and meetings will form another backbone of the communication process. The first plenary meeting of all consortium partners (“kick-off meeting”) will serve for sharing information on past and ongoing projects of each partner and their activities.

Dialogue with policy/decision makers, scientific community, academia, and business will also be supported by specifically tailored dissemination material for each.

We will also elaborate strategies for developing an associated Training program, aiming at contributing to forming a new generation of interdisciplinary irrigation water managers.

On the academic level, the project will provide an opportunity to intensify the exchange of students and scientists, in particular through the summer school organisation.