# Monitoring satellite-based crop irrigation water requirements for maize, cotton and hay in distinct Mediterranean regions

Nicholas Dercas, Georgios A Tziatzios, Pantelis Sidiropoulos, Sofia Sarchani, Ioannis Faraslis, Gilles Belaud, Kevin Daudin, Marios Spiliotopoulos, Stavros Sakellariou, Nikolaos Alpanakis, et al.

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Nicholas Dercas, Georgios A Tziatzios, Pantelis Sidiropoulos, Sofia Sarchani, Ioannis Faraslis, et al. Monitoring satellite-based crop irrigation water requirements for maize, cotton and hay in distinct Mediterranean regions. Agricultural Water Management, 2025, 319, pp.109719. 10.1016/j.agwat.2025.109719. hal-05233290

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# Monitoring satellite-based crop irrigation water requirements for maize, cotton and hay in distinct Mediterranean regions

Nicholas Dercas<sup>a, *, b</sup>, Georgios A. Tziatzios<sup>b, c</sup>, Pantelis Sidiropoulos<sup>c, d</sup>, Sofia Sarchani<sup>a, d</sup>, Ioannis Faraslis<sup>d, e</sup>, Gilles Belaud<sup>e, d</sup>, Kevin Daudin<sup>e, d</sup>, Marios Spiliotopoulos<sup>b, d</sup>, Stavros Sakellariou<sup>b, d</sup>, Nikolaos Alpanakis<sup>b, d</sup>, Georgios Bourazanis<sup>a, d</sup>, Nicolas Dalezios<sup>b</sup>

## ARTICLE INFO

### Keywords:

Actual evapotranspiration

Remote sensing

Sentinel

Experiments

Maize

Cotton

Hay

## ABSTRACT

The water requirements of plants are crucial for the sustainable water resources management since in the Mediterranean region, agriculture is the major consumer of water. In recent decades, remote sensing (Landsat, Sentinel, etc) has been used with appropriate methodologies that allow the estimation of evapotranspiration. The proposed methodology in this research has been developed in parallel with a similar methodology by the European Space Agency (ESA), based on a double energy balance. The proposed method has received an improvement as compared to the ESA's method, since the Weather Research & Forecasting Model (WRF) is used instead of ERA5, the fifth generation ECMWF, for better estimation of the meteorological conditions. Despite the improvement, the method still presents a systematic overestimation in relation to the field values mainly in the second half of the growing season. To address this problem and to successfully estimate daily actual evapotranspiration (ETa) values, the calibration and validation of the method for two crops (maize, cotton) and hay (mixture of forage crops) is presented. The first two crops were based on experiments carried out in central Greece and the third (hay) in experimental fields in southern France. Soil-water balance models, AquaCrop (for Greece) and PILOTE (for France), are used to monitor the daily crop irrigation water requirements throughout the growing season. The adopted satellite ETa adjustment factor in all crops is close to 0.59–0.63 and R² ranges from 0.87 to 0.97. The results show that a reliable estimation of ETa can be obtained using satellite data.

## 1. Introduction

As global population grows with rapidly increasing demands (increased food production, urbanization and industrial development), agriculture, the world's largest water consumer representing around 70% of global freshwater use, faces challenges as water resources are becoming scarce and under pressure due to climate change/extreme climate variability, worldwide competition and increased agriculture adaptation. Therefore, there is a critical need for sustainable management practices in agriculture (Aguirre-García et al., 2021; Kourgialas, 2021; Kisekka et al., 2022).

A universal call to action, the Sustainable Development Goals (SDGs) direct to "protect the planet and ensure that all humanity enjoys peace and prosperity." These goals are all linked and integrated, balancing the social, economic, and environmental aspects of sustainable development, to guarantee their accomplishment. Many of the 17 SDGs, entitled No Poverty (SDG1), Zero Hunger (SDG2), Gender Equality (SDG5), Responsible Consumption and Production (SDG12), Climate Action (SDG13), Life Below Water (SDG14), and Life on Land (SDG15), are related to agriculture and climate change as well. Precision agriculture, which aims to assure resilient and sustainable food production and climate adaptation, is required to meet the objectives while addressing

<footnote><sup>a</sup> Laboratory of Agricultural Hydraulics, Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, Athens 11855, Greece</footnote>

<footnote><sup>b</sup> Laboratory of Hydrology and Aquatic Systems Analysis, Department of Civil Engineering, School of Engineering, University of Thessaly, Volos 38221, Greece</footnote>

<footnote><sup>c</sup> Laboratory of Hydraulic Works and Environmental Management, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</footnote>

<footnote><sup>d</sup> Laboratory of Geoinformatics, Department of Environmental Sciences, School of Technology, University of Thessaly, Gaiopolis Campus, Larissa 41500, Greece</footnote>

<footnote><sup>e</sup> UMR G-Eau, Montpellier SupAgro, Univ. of Montpellier, 2 place Pierre Viala, Montpellier 34060, France</footnote>

<footnote>* Corresponding author.
E-mail address: ndercas1@aua.gr (N. Dercas).</footnote>
the problems caused by climate change (Grigorieva et al., 2023). Novel irrigation management strategies in precision agriculture synthesize data from ground sensor networks, remote sensing and agro-hydrological modeling as well, to achieve sustainable water resources management and sustainable agriculture (Adla et al., 2024).

As mentioned above, in the Mediterranean region and globally, agriculture is the dominant consumer of surface and groundwater systems. Additional challenges arise in agriculture from climate change/climate variability and consequent extreme weather events. On the one hand, reduced rainfall leads to drought events, on the other hand, high intensity rainfall leads to pluvial flooding events. As a result, sustainable agriculture is a big, debated issue (IPCC et al., 2021). A characteristic example is the recent extreme flooding event caused by Tropical Cyclone Daniel in the Thessaly region, which led to catastrophic damages to urban and rural areas, as well as to crops. Storm Daniel occurred between 3 and 8 September 2023 and affected also extensive parts of the Mediterranean region (Dimitriou et al., 2024). The total flooded area by the storm “Daniel” in Thessaly was estimated at 949.52 km², with the floodwaters inundating a large part of the Thessaly plain on the eastern and western hydrogeological basin (Falaras et al., 2025). According to Mourelatos et al. (2025), Lake Karla’s surface area expanded from 35 km² to 190 km². Regarding the crop category, according to He et al. (2024), cotton was the most obviously affected crop by the flooding, 282 km² of its cultivation area being flooded; this covers 30.5% of the entire cotton area in the Thessalian plain. Areas that were cultivated with rainfed crops, such as durum wheat and other winter wheats, were also flooded. The inundated area was estimated at 57.5 km² and 55 km², accounting for 11.7% and 7.1%, respectively, of the planted area with these crops. Moreover, other irrigated crops, such as fodder plants and grain maize, were significantly affected by the flooding, with 30.9 and 23.3 km², or 4% and 12.9%, respectively, of their farming areas being inundated. In addition, a relatively large portion of pasture was covered by water (19.6 km²), which represents only 1% of the total pasture area. On the other hand, the flooded areas of seed production and processing tomato (which is also an irrigated crop) were low, at 7.1 and 3.5 km², respectively, but they covered 11.5% and 12% of the total planted areas with these crops. On this point, it is essential to mention that Storm ‘Daniel’ modified the chemical properties of the soil as well. The causality analysis showed that the sediment deposition affected the CaCO₃ concentration in the soil. Furthermore, the statistical analysis indicated that maize cultivating in soils with a sediment over 10 cm due to the Storm ‘Daniel’ requires a 31.8% increase in the Phosphorus (P) rate to prevent yield decline (Iatrou et al., 2024). Lagarias (2025) represents a critical analysis of the Masterplan (HVA, 2025). According to the Masterplan, the main cause of groundwater depletion is that most of the Thessalian farmers do not apply the rules of Good Agricultural Practice. Most of the Thessalian farmers are drilling their private pumping wells (which are tens of thousands, most of them are unregistered), to ever greater depths to ensure an adequate water supply for the irrigation of their fields. They are encouraged to this unstable practice due to low electricity prices prevailing for irrigated agriculture. The risk of serious, irreversible damage to agriculture is recognized as groundwater presents significant problems on groundwater quality as well, such as nitrate pollution and salinization. Finally, Lagarias (2025) characterizes the case of the Thessalian plain as a typical example of the tragedy of the commons, from Hardin’s scientific report, which was published in 1968 with the same title. Therefore, the necessity to improve water resources management is more urgent than ever (IPCC et al., 2021).

Improving water resources management in agriculture, which is the main objective of this paper, can be achieved in various ways; we can use more efficient irrigation systems, more efficient transport and distribution networks, less water-intensive crops and crop varieties, improved irrigation planning, and targeted irrigation according to local conditions (precision agriculture). A basic component of the whole effort in any case must be the most accurate assessment of the water needs of the crops. We must be able to estimate this parameter reliably,

both accounting the needs of the previous period and the ones of the upcoming period with satisfactory forecast methods using sophisticated weather forecasting models. It is also necessary that these estimates refer to wider areas to make a better use of the irrigation infrastructures, and to use these data for the holistic approach of water resources management (irrigation, urban, industrial water use) (Dercas, 2020; Dercas et al., 2022).

Within the framework of such an approach, methods based on satellite data have been developed which allow us to estimate the water consumption of plants in wider areas under different water stress regimes. The Energy Balance (EB) algorithms, and especially the residual methods, are the most predominant group of Earth Observation (EO) methodologies to compute actual daily evapotranspiration (ETd) (Gowda et al., 2008). Remotely sensed EB algorithms convert satellite-sensed radiances into land surface features, such as albedo, Leaf Area Index (LAI), vegetation indices, surface roughness, surface emissivity and surface temperature, to calculate ET as a “residual” of the land surface Energy Balance equation (Bastiaanssen, 2000; Calera et al., 2017). In recent years, EB models differ principally in the way that Sensible Heat (H) is computed (Gowda et al., 2008). These models comprise the Two-Source Model (TSM) in which the Energy Balances of soil and vegetation are simulated individually and then their combination leads to the estimation of the total latent heat flux (LE), the Surface Energy Balance Algorithm for Land (SEBAL) and the Mapping Evapotranspiration with Internalized Calibration (METRIC). The latter utilize hot and cold pixels within the satellite images, contrasting dry and wet areas respectively, to develop an empirical temperature difference equation, and the Surface Energy Balance Index (SEBI) (Bastiaanssen et al., 1998a, 1998b; Allen et al., 2007). Other variations in SEBI comprise the Simplified Surface Energy Balance Index (S-SEBI) and the Surface Energy Balance System (SEBS) (Roerink et al., 2000; Su, 2022). The applied approach was first operated by the European Space Agency (ESA) using the Sen-ET plug-in (ESA, 2020), and the proposed methodology is an improvement of ESA’s method.

The method was improved using a sophisticated/efficient/credible/ reliable weather forecasting system (Spiliotopoulos et al., 2023).

The daily actual evapotranspiration estimated using remote sensing (ETa,sat) estimation model must be calibrated with water consumption measurements (actual evapotranspiration) from field experiments to produce reliable results. This article presents the calibration and validation of the method using field data from experimental fields in Greece (experimental maize and cotton fields in Thessaly, central Greece). Also, we present the calibration of the method in France (experimental hay field around Crau).

This study focuses on applying advanced tools, such as soil moisture sensors, remote sensing and agro-hydrological models, in precision agriculture to monitor plant growth, to estimate water balance parameters, and promote sustainable management of water demands. The procedure applied involved the following steps: (a) the estimation of satellite daily actual evapotranspiration from a proposed methodology in parallel with another one developed by ESA, based on a double energy method by using the WRF model instead of ERA5, and (b) the monitoring of daily crop irrigation requirements throughout the growing season. Our research had two objectives. The main objective was to calibrate and validate the proposed satellite method, improved using WRF, with field measurements and simulations in three monitored experimental fields (maize, cotton, hay), accompanied with soil-water balance modeling, in order to estimate a reliable actual evapotranspiration. To this aim, adjustment factors were applied to improve the accuracy of the satellite method. The second objective was to study the effect of different irrigation strategies on each experimental field by monitoring two separate plots, one based on traditional agricultural practices, and another according to reduced water consumption and at the same time increased water use efficiency. This practice could be useful for an appropriate farmers’ irrigation scheduling, as well as an overall management of water resources in wider areas.
In all three case studies, the behaviour of the method was similar in relation to the field values (based on measurements and simulations), and the estimation of an adjustment factor allowed a fairly accurate estimation of the water consumption values of the three crops. Finally, it is shown that the estimation of actual evapotranspiration with satellite data and with a reliable weather prediction model is feasible and effective.

## 2. Materials and methods

In the “HubIS” research project (HubIS, 2020), advanced irrigation management tools and collaborative methodologies for precision agriculture, such as ground sensor networks (Teros 10 soil sensors and ZL6 data logger basic edition), remote sensing data (Sentinel 2 and Sentinel 3 images) and agro-hydrological modeling (AquaCrop and PILOTE simulation models) as well, were used to examine and achieve sustainable water resources management and sustainable agriculture. Three experimental fields were used for this objective: a field cultivated with maize and another one cultivated with cotton in Falani, Thessaly (central Greece), as well as a hay field in the region of Crau in southern France.

### 2.1. State-of-the-art

In this research, combined Sentinel-2 and Sentinel-3 images are used to calculate daily ETa,satactual crop evapotranspiration. The proposed methodology estimates ETa,sat for each field using Sentinel-2 MSI radiometric data and Sentinel-3 SLSTR thermal data. The simulation program utilized is the Sen-ET SNAP software (ESA, 2020). The initial version of Sen-ET plugin used in SNAP, developed by ESA, uses the

two-source Energy Balance (TSEB) model (at bare soil and canopy level) (Norman et al., 1995) with Sentinel-2 and Sentinel-3 images, as well as meteorological data from ECMWF (Alpanakis et al., 2022; ESA, 2020; Ruescas and Peters, 2022). The adopted methodology in the current approach follows the initial 17 steps based on the TSEB model, as published in Spiliotopoulos et al. (2023), where Sentinel 2 images are resampled, specifying the pixel resolution to 20 m.

Yet, a modification of the initial Sen-ET SNAP plugin utilized in the current study takes advantage of meteorological data driven from the Weather Research & Forecasting Model (WRF), instead of ECMWF Reanalysis v5 (ERA5), improving the original plug-in. In particular, ERA5 covers a 30 km grid, while the introduced to the suggested methodology real-time data generated by the WRF model have a resolution of 2 km × 2 km, eventually calculating improved spatial ETa,sat values in mm/day. Fig. 1 presents a flow diagram of the process followed in Sen-ET SNAP plugin. Table 1 describes briefly the 17 steps procedure (ESA, 2020).

#### 2.1.1. Weather research and forecasting (WRF) model

The Weather Research and Forecasting (WRF) Model is a regional atmospheric modeling system designed for both research and arithmetical weather prediction. WRF is an open-source model, and it has been tested and applied for research at universities and at governmental laboratories, for operational forecasting by state and private organizations, and for commercial applications by industry. WRF was developed by the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA) (Skamarock et al., 2019).

The Advanced Research (ARW) dynamical core of WRF version 4.2

<figure><img src="image_3.png" /><figcaption>Fig. 1. Flow diagram of the process to estimate daily ETa,sat from Sen-ET SNAP plugin</figcaption></figure>
(Skamarock et al., 2019) is selected for the present simulations. The nested domain approach is used in the dynamic downscaling for the 2 km × 2 km domain. For the Thessaly plain spatial distribution is 106 × 121 grid points, while for the La Crau plain spatial distribution is 101 × 101 grid points.

The initial and boundary conditions for the five-day daily forecast are provided by the US National Aeronautics and Space Administration (NCAR) via the 1200UTC forecast cycle of the Global Forecast System (GFS), (2025). The Global Forecasting System is a weather forecasting model developed by the National Centre for Environmental Prediction (NCEP) (2025).

The ARW parameterizes, into internal modules, the main atmospheric governing equations: (1) convective processes, (2) cloud microphysics, for which ETa (Ferrier) scheme was selected in this case (NOAA, 2001), (3) transport in the planetary boundary layer (PBL), for which Mellor-Yamada-Janjic Scheme (MYJ) was selected (Janjić, 1994), (4) short and longwave radiative processes (SW, LW-Radiation), for which Rapid Radiative Transfer Model (RRTMG) for Global Circulation Models (GCMs) (2025) schemes was selected.

For the surface level, the ETa surface layer scheme was selected (Janjic, 2019). Finally (5) Unified Noah Land Surface Model (2025) was selected for land-surface processes (Cortés-Ramos et al., 2025).

## 2.2. Thessaly case study (Central Greece)

The Thessaly region is situated in the centre of Greece (Fig. 2), covering an area of about 13,700 Km². The 50 % of the region consists of mountainous and semi-mountainous areas, with the agricultural plain located in the centre. The altitude ranges from 0 to 2900 m. Thessaly's climate is characterized as continental climate with cold and wet winters and hot and dry summers. Especially in July and August, the maximum

temperature often exceeds 40 °C on plain areas. The mean annual precipitation varies from about 400 mm at the central plain area to more than 1800 mm at the mountainous areas. Thessaly is characterized by intensive agriculture in the plain. It produces the 14 % of Greece's agricultural products in its 410,000 ha cropland. Around 55 % of the agricultural land is irrigated. In Thessaly there is a main river, Pineios, and many branches of the Pineios river. The alluvial soils of Pineios basin make Thessaly a vital agricultural area, particularly to produce annual crops (wheat, cotton, corn, cereals), tree crops (almond, olive, pistachio), vineyard and many protected destinations of origin (PDO) products that include local cheeses, wines and spirits. The croplands receive large amounts of fertilizers increasing the yields but contaminating the water resources. Moreover, in Thessaly the water overexploitation is a common practice (the water balance is negative) that affects extremely the natural resources (soil and water resources). These conditions lead to soil degradation (desertification) and significant losses of crop yields. If this approach does not change, it could cause a real problem with the viability of farms and irrigated agriculture in Thessaly (Approval of the 2nd Revision of the River Basin Management Plan of the Water District of Thessaly 2024).

The innovative irrigation tools and methodologies were applied to two irrigated crops. The first one, Maize (Zea mays L), is a spring grain crop with particularly high water and fertilization demands in the Mediterranean regions (Farré and Faci, 2006). The second one, Cotton (Gossypium herbaceum), is a fiber crop with high-water demands as well, whose cultivation is very important for the economy of Thessaly, since a large part of the rural population of Thessaly cultivates cotton as the main irrigated crop (Darawsheh et al., 2022). Within the framework of the "HubIS" research project, in the region of Falani near Larissa, in the years 2021 and 2022, two experimental fields were established, one for maize (4.6 ha) and one for cotton (5.8 ha). As part of the crop

<figure><img src="image_4.png" /><figcaption>Fig. 2. Thessaly study area (Greece).</figcaption></figure>
rotation in 2023, the maize field changed crops and was sown with cotton. This field was the experimental cotton field (4.6 ha) for 2023, while a new field in the same area was the new experimental maize field (5.88 ha). In each experimental field we established two experimental plots with different irrigation treatments. One experimental plot was irrigated by the farmer according to traditional practices, while in the other plot, irrigation scheduling was applied according to the HubIS approach, which aimed at reducing water consumption and increasing water use efficiency.

### 2.2.1. *In situ* measurements

The appropriate equipment for in-situ measurements starts with the installation of a meteorological station in the wider study area depending on agroclimatic classification and zoning such as proposed by Faraslis et al. (2023), to monitor and predict extreme weather phenomena regarding the microclimate of the study area. A typical example is early warning systems (Díaz et al., 2025).

An up-to-date data logger for the soil moisture recording, which will be connected to Bluetooth and/or Wi-Fi/mobile network is also necessary in the fields in conjunction with remote sensing (Ghosh and Sharma, 2021; Mkhwenkwana et al., 2025; Wang et al., 2025). In our study we set ZL6 Basic Simple Data Logger by Metergroup (2025).

The number of sensors is related to the soil depth where soil moisture will be examined, as well as the crops being studied (Díaz et al., 2025; Maraveas et al., 2025). For example, if the study concerns maize crop or cotton crop, the depth of position of the soil moisture sensors will differ because the root system is different. The root system of maize is on the surface of the soil (Feldman, 1994), while the root system of cotton is deep (McMichael, 1986; Ghimire et al., 2025). For maize crop, it is important to study the soil moisture at the soil depth of 15 cm and 30 cm. Therefore, it would be acceptable to set up one soil moisture sensor at 15 cm and another one at 30 cm. Conversely, for cotton crop it would be acceptable to set up one soil moisture sensor at 30 cm and another one at 60 cm. In our field experiment, at each experimental plot, capacitance sensors TEROS10 at 15 cm, 30 cm, 60 cm and 90 cm, and the ZL6 Basic Simple Data Logger, were installed to record the soil moisture in the root zone in an hourly step (Fig. 3).

In addition, the number of sensors also contributes to the investigation of soil variability within the same field. It is critical to take soil samples to define the soil texture, the Electrical Conductivity (EC) value, which indicates soil salinity, and pH pointing whether the soil is acidic or alkaline. Moreover, nutrient content in the soil implies the content and amount of fertilizer application to protect the groundwater systems since soil nitrogen (N) losses under drip irrigation water and fertilizer management have become a key issue in global environmental N pollution (Singh, 2021; Wei et al., 2024; Díaz et al., 2025; Bharti et al.,

2025). During the installation of the sensors, soil samples were collected for soil texture analysis, pH, EC, and CaCO₃ measurements. The results are shown in Table 1. In the experimental field where maize is grown, the soil of the first two soil moisture sensors depth (0–30 cm) installed in both plots is clay loam (CL). The third soil moisture sensor depth (30–60 cm) installed is loam (L) in the plot of HubIS and clay loam (CL) in the plot of Farmer. The deeper soil moisture sensor depth (60–90 cm) installed is sandy clay loam (SCL) in the plot of HubIS and clay loam (CL) in the plot of Farmer.

In the experimental field where cotton is cultivated, the soil of all layers in both plots (HubIS and Farmer) is clay loam (CL), except for the soil moisture depth of 30–60 cm and 60–90 cm of the HubIS plot that are clay (C). Regarding the chemical properties, pH is alkaline in all experimental plots and EC is measured at low to moderate levels. Soil moisture measurements on soil moisture sensors depth are shown in Table 2. This value was used for soil moisture calibration. Bulk density measurements related to soil layers are not related to the depth of soil moisture sensors set. The measurements of bulk density are presented at Table 3.

Technologies such as the installation of sensors in the field, remote sensing, and agronomic simulation provide high spatial and temporal resolution data, as well as information about the variability and needs of the field at each point, so that the application of inputs to the field to be more accurate and more effective (Pandey et al., 2021; Raj et al., 2021; Chaves et al., 2025; Díaz et al., 2025; Yang et al., 2025).

Table 1 presents the results of soil analysis from soil samples taken in 2021.

Irrigation efficiency as well as drainage may differ at different points within the same field. For this reason, in addition to remote sensing data, it is also essential to install caudalimeters in the pumping wells so that the amount of water applied in each irrigation event is recorded accurately, as well as at the end of the irrigation period in total, especially in arid regions (Díaz et al., 2025; Franco-Navarro et al., 2025). Caudalimeters were installed in drip pipes to measure the amount of water being applied in each irrigation event (Fig. 4) The irrigation system in all the experimental plots was initially sprinkler irrigation (travel ramp), to establish the plantation, and then drip irrigation (one dripline for two crop lines). During the growing season, irrigations were recorded in the HubIS and Farmer plots. At the end of each growing season, the yields achieved were recorded. These yields were used to calculate the efficiency of water use and as a control parameter for the model.

Fig. 5a and Fig. 5b present the historical total annual average temperature trends and the historical annual precipitation trends respectively, from 1955 to 2004, via the Larisa Meteorological Station (Klein Tank et al., 2002).

The meteorological data of the wider area, for the period March –

<figure><img src="image_5.png" /><figcaption>Fig. 3. Data logger and sensors of soil moisture in the root zone.</figcaption></figure>
<table><caption>**Table 1**
Soil analysis of maize and cotton experimental fields for 2021.</caption><thead><tr><th rowspan="2">Type of crop</th><th colspan="4">Maize</th><th colspan="4">Cotton</th></tr><tr><th>HubIS</th><th>30-60</th><th>60-90</th><th>Farmer</th><th>30-60</th><th>60-90</th><th>HubIS</th><th>20-30</th><th>30-60</th><th>60-90</th><th>Farmer</th></tr></thead><tbody><tr><td>Depth (cm)</td><td>0-30</td><td>30-60</td><td>60-90</td><td>0-30</td><td>30-60</td><td>60-90</td><td>10-20</td><td>20-30</td><td>30-60</td><td>60-90</td><td>10-20</td><td>20-30</td><td>30-60</td><td>60-90</td></tr><tr><td>Sand (%)</td><td>43.4</td><td>46.7</td><td>54.4</td><td>31.7</td><td>31.2</td><td>41</td><td>29</td><td>32</td><td>26</td><td>34</td><td>30</td><td>24</td><td>28</td><td>36</td></tr><tr><td>Clay (%)</td><td>27.9</td><td>25.3</td><td>26.4</td><td>32.7</td><td>36.2</td><td>29.3</td><td>39</td><td>35</td><td>43</td><td>41</td><td>33</td><td>37</td><td>33</td><td>31</td></tr><tr><td>Silt (%)</td><td>28.7</td><td>28</td><td>19.2</td><td>35.6</td><td>32.6</td><td>29.7</td><td>32</td><td>33</td><td>31</td><td>25</td><td>37</td><td>39</td><td>39</td><td>33</td></tr><tr><td>Soil type</td><td>CL</td><td>L</td><td>SCL</td><td>CL</td><td>CL</td><td>CL</td><td>CL</td><td>CL</td><td>C</td><td>C</td><td>CL</td><td>CL</td><td>CL</td><td>CL</td></tr><tr><td>pH</td><td>8</td><td>8.2</td><td>8.3</td><td>8.2</td><td>8.3</td><td>8.3</td><td>8</td><td>8.1</td><td>8.2</td><td>8.6</td><td>8.1</td><td>8</td><td>8.5</td><td>8.6</td></tr><tr><td>E. Conductivity (µS/cm)</td><td>512</td><td>380</td><td>432</td><td>395</td><td>437</td><td>432</td><td>396</td><td>377</td><td>498</td><td>487</td><td>423</td><td>417</td><td>340</td><td>347</td></tr><tr><td>CaCO<sub>3</sub> (%)</td><td>4</td><td>19</td><td>15.4</td><td>12.9</td><td>15.4</td><td>14</td><td>2.5</td><td>3</td><td>3.5</td><td>4.1</td><td>3.5</td><td>5.5</td><td>4.4</td><td>3.8</td></tr><tr><td>Organic Matter (%)</td><td>0.9</td><td>0.7</td><td>0.8</td><td>0.6</td><td>0.3</td><td>0.3</td><td>0.6</td><td>&lt; 0.39</td><td>-</td><td>-</td><td>0.67</td><td>0.50</td><td>-</td><td>-</td></tr></tbody></table>

September 2021, are presented in Fig. 6a (maximum, average and minimum temperature) and Fig. 6b (rainfall), on a daily time step, as emerged from the nearest meteorological station, which is Larissa station (Meteo, 2021). The total rainfall was 307.4 mm, and the temperature ranged from -0.3 °C (March minimum) to 44.9 °C (August maximum).

## 2.3. La Crau case study (France)

The study area (called “Domaine du Merle”) is an experimental farm located in southeast France (centred at 43.64°N, 5.00°E). The total area of the farm is 400 ha, with 150 ha of irrigated grass-land plots (50 plots) (Fig. 7). The climate is Mediterranean with a rainy season between September and November. Over the study area, the average cumulative rainfall reached 457.5 mm in 2013 and varied between 350 and 800 mm over the past 20 years. The mean air temperature is approximately 8°C and 24°C during winter and summer, respectively (Courault et al., 2010). The topsoil texture of the irrigated plots is stony loam (15–20 % pebbles) with depths that vary from 30 to 80 cm, depending on the plot age (between 10 years and 3 centuries) (Mailhol and Merot, 2008). The soil has a moderate retention capacity with a highest density of vegetation roots in the upper 30 cm (Merot et al., 2008). Plots were levelled with a very gentle slope to enable irrigation by gravity (border irrigation). Irrigation is applied by means of canals, which bring water to the highest extremities of the plots. Each plot is irrigated every 10 days on average, from April to September. Plots are harvested three times per year, in May, July, and September.

## 2.4. In situ measurements

In situ campaigns were conducted in February and October 2013 to collect ground-truth measurements of Total Dry Matter (TDM) and Leaf Area Index (LAI) in ten training plots. The areas of the training plots varied between 2.13 and 7.23 ha. For the TDM, two vegetation samples were first clipped, using shears, over 50 cm × 50 cm squares, in each of the ten training plots. The vegetation samples were subsequently dried at a temperature of 70°C and weighed to measure the TDM. Moreover, twenty to thirty hemispherical images were acquired at nadir for each plot by means of a fisheye lens. These photos were processed using the CAN-EYE (2015) software (http://www6.paca.inra.fr/can-eye) to estimate the in-situ LAI values.

## 2.4. Model simulation

In both study areas, crop growth models were used to simulate plant growth and at the same time to estimate water balance parameters in the field. The main objective of this research was to have a very good estimation of ETa,sat using ground data (soil moisture), yield data and satellite data (LAI) in order to use this for model calibration. In the case of maize and cotton, the AquaCrop model was used, while in the case of hay, the PILOTE model was used.

## 2.5. AquaCrop model description

AquaCrop model was developed by the Land and Water Division of FAO, as an evolution from the original FAO Irrigation and Drainage Paper 33 (Doorenbos and Kassam, 1979), which simulates the yield response to water consumption as a linear, crop-specific function of the ratio of actual to maximum evapotranspiration over a crop cycle (Raes et al., 2009). Steduto et al. (2009) reported a detailed description of the model’s conceptual design.

AquaCrop simulates soil water balance and crop growth processes as a function of climate, crop, management, and soil input data, on a daily time step (Foster et al., 2017). Moreover, AquaCrop simulates soil evaporation (E) and crop transpiration (Tr) explicitly as separate processes. Crop yield response is calculated as a function of water supply (Steduto et al., 2008). The calculation cycle contains a simulation of green canopy cover (CC), crop transpiration (Tr), aboveground biomass production (B), and crop yield (Y) (Steduto et al., 2008; Raes et al., 2018). AquaCrop uses four routines: (i) the climate (minimum and maximum temperature, rainfall, reference evapotranspiration (ET₀), and carbon dioxide (CO₂) concentration), (ii) the crop (crop growth calendar and yield), (iii) the management (major agronomic practices such as fertilizer application, mulches covering, and weed management, as well as any applied irrigation), and (iv) the soil (soil water balance and capillary rise from groundwater table) (Mkhabela, Bullock, 2012; Sandhu and Irmak, 2019a).

The AquaCrop model is a water-driven model since it simulates above-ground biomass in exchange for water transpired by the crop according to the following relation (Steduto et al., 2007; Steduto et al., 2008; Vanuytrecht et al., 2014) (Eq. (1)):

$$B_n = WP^* \times \sum_{i=1}^{n} \left( \frac{Tr_i}{ET_{O_i}} \right) \quad (1)$$

where $B_n$ is the cumulative above-ground biomass produced after n days (g m⁻²), $Tr_i$ is the crop transpiration per day (mm day⁻¹), $ET_{O_i}$ is the daily reference evapotranspiration (mm day⁻¹), i = 1 ... n are the days for $B_n$ production, and $WP^*$ is the normalized crop water productivity (g m⁻²).

The model simulates dry yield as well, through the above-ground biomass (Bn) and a harvest index (HI), the latter being a fraction of Bn, as follows (Eq. (2)):

$$Y = B_n \times HI \quad (2)$$

The crop canopy development and growth stages are based on the temperature. The crop water transpiration is specified through the green canopy coverage (Vanuytrecht et al., 2014).

Moreover, the model simulates daily water movement in and out of the soil layers accounting various processes, i.e. surface runoff, drainage, infiltration, capillary rise, soil evaporation, and crop transpiration. Specifically, the simulation begins by computing the soil profile drainage, as well as the soil water infiltration, after subtracting the surface runoff, or the water movement upwards due to capillary rise from a shallow groundwater table. Then, the model calculates the
<table><caption>Table 2<br>Soil moisture measurements at the soil moisture sensors' depth.</caption><thead><tr><th rowspan="2">Type of crop<br>Type of field</th><th colspan="2">Maize</th><th colspan="2">Farmer</th><th colspan="2">Cotton<br>HubIS</th><th colspan="2">Farmer</th></tr><tr><th>Depth (cm)<br>Soil Moisture</th><th>HubIS</th><th>30<br>13.28 %</th><th>60<br>11.99 %</th><th>90<br>9.35 %</th><th>30<br>15.10 %</th><th>60<br>14.98 %</th><th>90<br>15.52 %</th><th>30<br>20.23 %</th></tr></thead><tbody><tr><td></td><td>15<br>13.11 %</td><td>30<br>13.28 %</td><td>15<br>14.87 %</td><td>30<br>15.10 %</td><td>15<br>14.87 %</td><td>15<br>15.52 %</td><td>30<br>20.23 %</td><td>15<br>20.59 %</td><td>30<br>21.06 %</td></tr></tbody></table>

amount of water loss due to soil evaporation and crop transpiration. In each subroutine, the soil water content is updated at the end of the time step and at each layer of the soil profile, resulting in the final daily water content (Raes et al., 2018). When calculating the soil-water balance, the water storage in the root zone can be calculated through an equivalent water depth (W<sub>r</sub>) or via root zone depletion (Steduto et al., 2012). This procedure leads to the model calibration and validation considering soil moisture measurements taken in the field, as well the final crop yields and LAI.

Around the world, AquaCrop has been successfully applied for various crops under various hydro-meteorological situations, for example, potato (Vanuytrecht et al., 2016), sunflower (Todorovic et al., 2009; Stricevic et al., 2011), tomato (Katerji et al., 2013), cotton (Farahani et al., 2009; García-Vila et al., 2009; Hussein et al., 2011), maize (Heng et al., 2009; Hsiao et al., 2009; Stricevic et al., 2011; Abedinpour et al., 2012; Shrestha et al., 2013; Shirazi et al., 2022), quinoa (Geerts et al., 2009a, 2009b), sugar beet (Stricevic et al., 2011), barley (Abrha et al., 2012), tef (Tsegay et al., 2012, 2015), rice (Maniruzzaman et al., 2015), soybean (Adeboye et al., 2017, 2021), and wheat (Shrestha et al., 2013; Mustafa et al., 2017; Abedinpour, 2021; Amiri et al., 2021; Abdou et al., 2022; Shirazi et al., 2022). According to all these studies, the AquaCrop model is capable of properly simulating grain yield (GY), crop biomass (B), canopy cover (CC), and soil water content under various irrigation and fertilization circumstances. The model has also been tested in West Africa (Akumaga et al., 2017; Alvar-Beltrán et al., 2023), Eastern Africa (Araya et al., 2010a, 2010b; Van Gaelen et al., 2014) and Southern Africa (Bello, Walker, 2017). These investigations demonstrated that the model was applied under various stresses such as full and deficit irrigation, rainfed, and soil fertility (Van Gaelen et al., 2014; Akumaga et al., 2017).

## 2.6. The PILOTE crop model

The PILOTE model was chosen for the application case of France, since this model was already calibrated on the study area. The Crau hay version (Mailhol and Merot, 2008) of the PILOTE crop model has been utilized and adapted for this study to fit with the new auto PILOTE procedure (Cheviron et al., 2016), which enables the generation and analysis of multiple scenarios in procedures that are intended for constrained optimization. The model requires four climatic forcing (rain and/or irrigation, temperature, potential evapotranspiration, and total incoming radiation) at a daily timestep, which is also the requirement for the predictions it delivers; water reserve and drainage, soil evaporation, plant transpiration, LAI and total dry matter (TDM) are some of the daily updated calculated variables. PILOTE is a true crop model because a reciprocal control loop exists between the evolution of the water reserve in soils and crop growth. The status of the water reserve (i. e., filling of the superficial, root zone and deep reservoirs) is updated from rain and/or irrigation events using the values of a series of soil parameters (e.g., field capacity and depths of the previously mentioned reservoirs). The dynamics of plant growth are controlled by the accumulation of degrees-days (a sum of mean daily air temperature), the values of several shape parameters (that dictate the shape of the LAI curve with increasing degrees-days) and the occurrence of water stresses (especially harmful for this crop). Root development is calculated in coherence with the increase of LAI, which allows the estimation of soil evaporation and plant transpiration from the climatic demand (potential evapotranspiration). This closes PILOTE's calculation loop: root water uptake and water losses by evaporation (or drainage) provide the last update to the water reserve terms, at the end of the calculation step, within a single day. The production of TDM is calculated from the LAI values and the incoming radiation, with the key parameter being the radiation use efficiency, while the water stresses have a negative impact on the process. An additional feature of the latest (unpublished yet) version of the model is the possibility to "overrule" predicted leaf area index values by independent leaf area values, obtained from field
<table><caption>**Table 3**
Bulk density and soil moisture measurements at the soil layers.</caption><thead><tr><th>Type of crop</th><th colspan="4">Maize</th><th colspan="4">Cotton</th></tr><tr><th>Type of field</th><th>HubIS</th><th>Farmer</th><th>HubIS</th><th>Farmer</th><th>HubIS</th><th>Farmer</th><th>HubIS</th><th>Farmer</th></tr></thead><tbody><tr><th>Depth (cm)</th><td>0-35</td><td>35-47</td><td>47-90</td><td>0-35</td><td>35-47</td><td>47-90</td><td>0-35</td><td>35-47</td></tr><tr><th>Soil Moisture (%)</th><td>12.96 %</td><td>12.89 %</td><td>10.78 %</td><td>14.34 %</td><td>15.28 %</td><td>15.30 %</td><td>20.15 %</td><td>20.45 %</td></tr><tr><th>Bulk density (g/cm<sup>3</sup>)</th><td>1.61</td><td>1.68</td><td>1.64</td><td>1.6</td><td>1.42</td><td>1.58</td><td>1.66</td><td>1.69</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Caudalimeters in drip irrigation.</figcaption></figure>

campaigns or remote sensing.

For PILOTE model running, thirty optical images were acquired with high spatial resolution by the SPOT-4/5 and LANDSAT-7/8 satellites over the study site. The spatial resolutions are 10 m × 10 m for SPOT-5, 20 m × 20 m for SPOT-4, and 30 m × 30 m for LANDSAT-7/8. The

optical images were calibrated, corrected for atmospheric effects and ortho-rectified. The calibration of SPOT-4 images was performed by the CESBIO (2015) in the framework of the Take 5 experiment. SPOT-4 images were corrected for atmospheric effects according to the method described in the study by Hagolle et al. (2008). For SPOT-5 and

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Historical (a) Temperature Trends, and (b) Precipitation Trends, from 1955 to 2004 via the Larissa Meteorological Station.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Daily values of (a) maximum, average and minimum temperature, and (b) rainfall, for the period March – September 2021 from the Larissa meteorological station.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 7. La Crau, French study area (South France).</figcaption></figure>

LANDSAT-8 images, the Simplified Method of Atmospheric Correction model (SMAC) (Rahman and Dedieu, 1994) was employed to correct for atmospheric effects. The input variables to the SMAC (aerosol optical thickness of 550 nm and water vapor contents of g/m²) were obtained from the AERONET (AErosol Robotic NET-work) (AERONET, 2015). LANDSAT-7 images that were previously corrected for atmospheric effects were directly downloaded from the NASA (NASA, 2015). SPOT-5 images were ortho-rectified using ERDAS IMAGINE (ERDAS IMAGINE, 2024) software, whereas the LANDSAT-7/8 images were previously ortho-rectified. The LAIs of the studied grassland were derived from the calibrated optical images (SPOT-4/5, LANDSAT-7/8) using the Bio-physical Variables Neural NETwork (BV-NNET) tool that was developed based on algorithms proposed by Baret et al. (2007) and optimized via the SIRRIMED project (SIRRIMED project, 2015).

## 3. Results and discussion

The experiments carried out both in Falani (Larisa/Greece) and in the Crau region in France allowed the actual evapotranspiration to be estimated in the field. The measured parameters: soil moisture, yield, and LAI allowed a reliable calibration of the models used (AquaCrop and PILOTE) and the estimation of the actual evapotranspiration in the field. For the case of the experiments in Falani (Thessaly, Greece) regarding maize and cotton, the 2021 cropping season was used for AquaCrop calibration, and the 2022 and 2023 cropping seasons for the model validation. In the case of hay in France, the PILOTE model was used with data from 2021.

### 3.1. Thessaly, Greek study area

#### 3.1.1. Spatial distribution of ETa Remote Sensing in the maize field

During the 2021 growing season, 15 Sentinel images were down-loaded with a maximum cloud cover of 20 % during the irrigation period (including sprinkler irrigation applied for the spring up process). The 15 Sentinel images were analyzed for ETa,sat estimation in the experimental field in the Thessaly plain.

Table 4 and Table 5 present the variations of maximum, mean and minimum daily ETa,sat for the experimental maize field “HubIS” and the experimental maize field “Farmer”, correspondingly. Fig. 8 shows the spatial distribution of daily ETa,sat of maize fields. The maximum value of daily ETa,sat, 8.53 mm, both for HubIS and Farmer plots, is estimated during the warmest part of the irrigation season on the 1st of August,

<table><caption>Table 4
Daily ETa,sat variances of maize hubis experimental field in 2021 (mm).</caption><thead><tr><th rowspan="2">Date</th><th colspan="5">Daily ETa,sat for Maize HubIS Experimental Field (mm)</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Median</th><th>Standard deviation</th></tr></thead><tbody><tr><td>3 Apr 2021</td><td>2.55</td><td>3.63</td><td>3.22</td><td>3.23</td><td>0.18</td></tr><tr><td>13 May 2021</td><td>1.52</td><td>3.02</td><td>2.60</td><td>2.80</td><td>0.44</td></tr><tr><td>23 May 2021</td><td>3.56</td><td>5.84</td><td>5.12</td><td>5.17</td><td>0.42</td></tr><tr><td>28 May 2021</td><td>1.47</td><td>3.52</td><td>2.24</td><td>2.22</td><td>0.43</td></tr><tr><td>27 Jun 2021</td><td>5.78</td><td>7.71</td><td>6.05</td><td>5.89</td><td>0.40</td></tr><tr><td>7 Jul 2021</td><td>5.93</td><td>7.45</td><td>6.65</td><td>6.49</td><td>0.32</td></tr><tr><td>12 Jul 2021</td><td>6.89</td><td>8.32</td><td>7.87</td><td>7.87</td><td>0.35</td></tr><tr><td>22 Jul 2021</td><td>5.72</td><td>7.13</td><td>6.53</td><td>6.50</td><td>0.22</td></tr><tr><td>1 Aug 2021</td><td>7.06</td><td>8.53</td><td>8.26</td><td>8.41</td><td>0.34</td></tr><tr><td>11 Aug 2021</td><td>6.65</td><td>7.60</td><td>7.39</td><td>7.43</td><td>0.17</td></tr><tr><td>16 Aug 2021</td><td>5.43</td><td>6.80</td><td>6.15</td><td>6.17</td><td>0.37</td></tr><tr><td>26 Aug 2021</td><td>3.18</td><td>5.06</td><td>4.33</td><td>4.39</td><td>0.40</td></tr><tr><td>31 Aug 2021</td><td>2.43</td><td>3.98</td><td>2.88</td><td>2.80</td><td>0.32</td></tr><tr><td>10 Sep 2021</td><td>3.33</td><td>3.87</td><td>3.52</td><td>3.52</td><td>0.08</td></tr><tr><td>15 Sep 2021</td><td>1.12</td><td>2.88</td><td>1.93</td><td>1.44</td><td>0.68</td></tr></tbody></table>

and the minimum on the 15th of September.

#### 3.1.2. Spatial distribution of ETa remote sensing in the cotton field

Table 6 and Table 7 present the variations of maximum, mean and minimum daily ETa,sat for the experimental cotton field “HubIS” and the experimental cotton field “Farmer”, respectively. Fig. 9 shows the spatial distribution of daily ETa,sat of cotton fields. The maximum value of daily ETa,sat, 9.46 mm for the HubIS plot and 9.63 mm for the Farmer plot, is observed during the warmest period of the irrigation season on the 1st of August, and the minimum on the 28th of May.
<table><caption>**Table 5**
Daily ETa,sat variances of maize farmer experimental field in 2021 (mm).</caption><thead><tr><th rowspan="2">Dates</th><th colspan="5">Daily ETa,sat for Maize Farmer Experimental Field (mm)</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Median</th><th>Standard deviation</th></tr></thead><tbody><tr><td>3 Apr 2021</td><td>2.43</td><td>4.37</td><td>3.57</td><td>3.64</td><td>0.28</td></tr><tr><td>13 May 2021</td><td>1.54</td><td>4.03</td><td>2.68</td><td>2.93</td><td>0.58</td></tr><tr><td>23 May 2021</td><td>3.71</td><td>5.46</td><td>4.86</td><td>4.87</td><td>0.29</td></tr><tr><td>28 May 2021</td><td>1.46</td><td>3.07</td><td>2.25</td><td>2.23</td><td>0.40</td></tr><tr><td>27 Jun 2021</td><td>5.39</td><td>7.95</td><td>6.48</td><td>6.52</td><td>0.43</td></tr><tr><td>7 Jul 2021</td><td>4.40</td><td>7.63</td><td>7.09</td><td>7.13</td><td>0.38</td></tr><tr><td>12 Jul 2021</td><td>4.16</td><td>8.50</td><td>8.12</td><td>8.29</td><td>0.60</td></tr><tr><td>22 Jul 2021</td><td>4.43</td><td>7.25</td><td>6.66</td><td>6.72</td><td>0.38</td></tr><tr><td>1 Aug 2021</td><td>4.45</td><td>8.53</td><td>8.20</td><td>8.33</td><td>0.54</td></tr><tr><td>11 Aug 2021</td><td>4.75</td><td>7.68</td><td>7.25</td><td>7.40</td><td>0.51</td></tr><tr><td>16 Aug 2021</td><td>3.03</td><td>7.11</td><td>6.10</td><td>6.25</td><td>0.73</td></tr><tr><td>26 Aug 2021</td><td>1.78</td><td>5.98</td><td>4.55</td><td>4.71</td><td>0.85</td></tr><tr><td>31 Aug 2021</td><td>2.00</td><td>4.28</td><td>2.98</td><td>2.86</td><td>0.48</td></tr><tr><td>10 Sep 2021</td><td>3.24</td><td>3.70</td><td>3.53</td><td>3.54</td><td>0.10</td></tr><tr><td>15 Sep 2021</td><td>0.98</td><td>2.99</td><td>2.12</td><td>2.62</td><td>0.73</td></tr></tbody></table>

### 3.1.3. AquaCrop simulation model: the case of maize and cotton

3.1.3.1. Cultivation period 2021 (Calibration process). The first-year data (2021 data) was used to calibrate the AquaCrop model. Afterwards, the ETa AquaCrop results were used to calibrate the estimated ETa,sat applying the remote sensing 17-steps method utilizing Sentinel data. Table 8 shows the statistical criteria of soil moisture agreement between measured and estimated values for the calibration of hydraulic parameters of AquaCrop, as extracted by the model. The validation process was conducted during 2022 and 2023.

Fig. 10 shows the soil moisture in the maize and the cotton root zone for 2021. The black points are the observed values of the soil water

content and the blue area represents the simulated soil water content in a soil profile of 1 m for 2021 (the left graph shows the soil moisture for maize, and the right graph shows the soil moisture for cotton). The calibration of the model was made not only based on soil moisture, but also based on the crop yields. Specifically, the model predicted that the maize yield reached 17.677 Mg/ha (Fig. 11a), while the cotton yield reached 4.943 Mg/ha (Fig. 11b), with the observed yields being equal to 15 Mg/ha and 4.5 Mg/ha respectively. These results highlight the very good setting of the model.

The results of 2021 indicate that the model can predict the soil moisture evolution in the root zone. In Fig. 11 the AquaCrop interface is presented, where the transpiration, canopy cover and soil moisture, as well as the biomass and dry yield produced for maize and cotton regarding 2021 are estimated.

Then the LAI values for Maize and Cotton regarding 2021, based on satellite images and on the canopy cover (CC) calculated by AquaCrop, are presented (Fig. 13). The LAI extracted by Sentinel was calculated through the formula of Boegh et al. (2002), which relates the LAI with the enhanced vegetation index (EVI), as given in Eq. (3).

$$ LAI = 3.618EVI - 0.118 \quad (3) $$

<table><caption>**Table 6**
Daily ETa,sat variances of cotton hubis experimental field in 2021 (mm).</caption><thead><tr><th rowspan="2">Date</th><th colspan="5">Daily ETa,sat for Cotton HubIS Experimental Field (mm)</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Median</th><th>Standard deviation</th></tr></thead><tbody><tr><td>3 Apr 2021</td><td>2.36</td><td>3.50</td><td>2.95</td><td>2.98</td><td>0.19</td></tr><tr><td>13 May 2021</td><td>1.11</td><td>2.97</td><td>2.43</td><td>2.48</td><td>0.32</td></tr><tr><td>23 May 2021</td><td>2.65</td><td>4.24</td><td>3.68</td><td>3.76</td><td>0.31</td></tr><tr><td>28 May 2021</td><td>0.00</td><td>2.49</td><td>1.67</td><td>1.83</td><td>0.53</td></tr><tr><td>27 Jun 2021</td><td>4.85</td><td>7.24</td><td>6.17</td><td>6.26</td><td>0.50</td></tr><tr><td>7 Jul 2021</td><td>4.80</td><td>8.36</td><td>7.33</td><td>7.41</td><td>0.81</td></tr><tr><td>12 Jul 2021</td><td>4.84</td><td>8.62</td><td>7.57</td><td>7.76</td><td>0.91</td></tr><tr><td>22 Jul 2021</td><td>5.24</td><td>9.30</td><td>8.63</td><td>8.99</td><td>0.92</td></tr><tr><td>1 Aug 2021</td><td>6.36</td><td>9.46</td><td>8.79</td><td>8.95</td><td>0.59</td></tr><tr><td>11 Aug 2021</td><td>6.18</td><td>8.75</td><td>8.09</td><td>8.25</td><td>0.58</td></tr><tr><td>16 Aug 2021</td><td>5.14</td><td>7.22</td><td>6.64</td><td>6.63</td><td>0.44</td></tr><tr><td>26 Aug 2021</td><td>3.80</td><td>6.47</td><td>4.99</td><td>4.99</td><td>0.71</td></tr><tr><td>31 Aug 2021</td><td>3.29</td><td>5.56</td><td>4.41</td><td>4.37</td><td>0.65</td></tr><tr><td>10 Sep 2021</td><td>3.43</td><td>4.92</td><td>4.01</td><td>4.07</td><td>0.38</td></tr><tr><td>15 Sep 2021</td><td>2.70</td><td>4.43</td><td>3.12</td><td>3.05</td><td>0.37</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Spatial distribution of daily ETa,sat for the Maize Experimental Field (HubIS and Farmer plots) in 2021.</figcaption></figure>
<table><caption>**Table 7**
Daily ETa,sat variances of cotton farmer experimental field in 2021 (mm).</caption><thead><tr><th rowspan="2">Date</th><th colspan="5">Daily ETa,sat for Cotton Farmer Experimental Field (mm)</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Median</th><th>Standard deviation</th></tr></thead><tbody><tr><td>3 Apr 2021</td><td>2.61</td><td>3.32</td><td>2.95</td><td>2.95</td><td>0.10</td></tr><tr><td>13 May 2021</td><td>1.16</td><td>2.94</td><td>2.42</td><td>2.39</td><td>0.19</td></tr><tr><td>23 May 2021</td><td>2.75</td><td>4.16</td><td>3.69</td><td>3.72</td><td>0.21</td></tr><tr><td>28 May 2021</td><td>0.37</td><td>2.23</td><td>1.57</td><td>1.71</td><td>0.42</td></tr><tr><td>27 Jun 2021</td><td>4.94</td><td>7.11</td><td>6.49</td><td>6.58</td><td>0.42</td></tr><tr><td>7 Jul 2021</td><td>5.38</td><td>8.36</td><td>7.65</td><td>7.87</td><td>0.65</td></tr><tr><td>12 Jul 2021</td><td>5.13</td><td>8.45</td><td>7.71</td><td>7.99</td><td>0.73</td></tr><tr><td>22 Jul 2021</td><td>5.88</td><td>9.45</td><td>8.81</td><td>9.00</td><td>0.65</td></tr><tr><td>1 Aug 2021</td><td>7.02</td><td>9.63</td><td>9.11</td><td>9.20</td><td>0.43</td></tr><tr><td>11 Aug 2021</td><td>6.45</td><td>8.48</td><td>7.92</td><td>8.02</td><td>0.36</td></tr><tr><td>16 Aug 2021</td><td>5.87</td><td>7.27</td><td>6.46</td><td>6.43</td><td>0.28</td></tr><tr><td>26 Aug 2021</td><td>3.22</td><td>5.30</td><td>4.41</td><td>4.41</td><td>0.50</td></tr><tr><td>31 Aug 2021</td><td>2.14</td><td>5.00</td><td>3.65</td><td>3.80</td><td>0.73</td></tr><tr><td>10 Sep 2021</td><td>3.21</td><td>5.01</td><td>3.67</td><td>3.56</td><td>0.37</td></tr><tr><td>15 Sep 2021</td><td>2.66</td><td>4.44</td><td>2.97</td><td>2.85</td><td>0.31</td></tr></tbody></table>

The LAI derived by the CC of maize as calculated by AquaCrop, was estimated through the equation of Hsiao et al. (2009), as shown in Eq. (4).

$$LAI = - \ln\left(1 - \left(\frac{CC}{1.005}\right)^{\frac{1}{1.2}}\right) / 0.6 \quad (4)$$

On the other hand, the LAI derived by the CC of cotton as computed by AquaCrop, was calculated via the formula of Farahani et al. (2009), as

depicted in Eq. (5).

$$LAI = - \ln(1 - CC)/0.77 \quad (5)$$

In Fig. 12, the LAI evolution calculated by satellite images was represented by a continuous line, while the corresponding one from AquaCrop was illustrated with an axial line (cultivation period 2021). It becomes clear that in the case of maize there is a similar trend of change in LAI calculated with the two methodologies. After all, this is also highlighted by the value of R²= 0.96 in the correlation of these values. The Root Mean Square Error (RMSE) was also estimated, equal to 0.81 m²/m², as well as the Relative RMSE (RRMSE) reaching 25.6 %. In the case of cotton, although the trend in the two curves of LAI is similar (Fig. 12), the values of LAI from AquaCrop in the first phase of the growing season are clearly lower, whereas higher in the second half. The coefficient of determination R² for the values of the two LAI curves for cotton is 0.84, while RMSE equals to 1.23 m²/m².

### 3.1.4. Correlation of ETa remote sensing and ETa AquaCrop

Since the estimated ETa by remote sensing is examined in Section 3.1.1 (maize field) and Section 3.1.2 (cotton field), the next step is to compare these results with the corresponding ETa results provided by AquaCrop. AquaCrop is considered a reliable model for estimating evapotranspiration for crops (Sandhu and Irmak, 2019a). The daily ETa, sat calculated through remote sensing applying SNAP Sen-ET software is overestimated based on its results on maize and cotton crops (Fig. 13 and Fig. 14). The WRF at spatial resolution of 2 km, used in this research, simulated (i) the coupled horizontal component of wind speed in x- direction at 100 m, (ii) the coupled horizontal component of wind speed in y- direction at 100 m, (iii) the dew point at 2 m, (iv) the air

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Spatial distribution of daily ETa,sat for the Cotton Experimental Field (HubIS and Farmer plots) in 2021.</figcaption></figure>

<table><caption>**Table 8**
Hydraulic parameter values and calibration soil moisture statistics of the “HubIS” fields for the two crops (maize and cotton) regarding 2021.</caption><thead><tr><th rowspan="2">Crop</th><th colspan="4">Hydraulic parameters<sup>a</sup></th><th colspan="4">Setup soil moisture statistics</th></tr><tr><th>Sat (mm)</th><th>FC (mm)</th><th>PWP (mm)</th><th>Ksat (mm/d)</th><th>Pearson Correlation Coefficient (r)</th><th>Root Mean Square Error - RMSE (mm)</th><th>Normalized Root Mean Square Error - CV(RMSE) (%)</th><th>Nash-Sutcliffe Model Efficiency Coefficient (EF)</th><th>Willmott's Index of Agreement (d)</th></tr></thead><tbody><tr><td>Maize</td><td>397</td><td>235</td><td>156</td><td>39.7</td><td>0.79</td><td>13.2</td><td>6.9</td><td>-0.57</td><td>0.76</td></tr><tr><td>Cotton</td><td>470</td><td>330</td><td>250</td><td>200</td><td>0.87</td><td>14.7</td><td>4.3</td><td>0.63</td><td>0.91</td></tr></tbody></table>

<footnote><sup>a</sup> Sat (mm) is the soil water content (SWC) at soil saturation, FC (mm) is the SWC at field capacity, PWP (mm) is the SWC at permanent wilting point, and Ksat (mm/day) is the saturated hydraulic conductivity</footnote>
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Values of the observed soil moisture (black points), and simulated soil moisture (blue area), in the root zone, in a soil profile of 1 m, for (a) Maize, and (b) Cotton, in 2021.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** The simulated yields for (a) the experimental “HubIS” field of maize, and (b) the experimental “HubIS” field of cotton, for 2021.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** LAI evolution for Maize and Cotton in 2021, by Sentinel (continuous line) and AquaCrop (axial line).</figcaption></figure>

temperature at 2 m, (v) the surface pressure, (vi) the surface solar radiation downwards, and (vii) the surface shortwave radiation in clear sky, probably resulting in overestimated predictions of some of the major meteorological parameters. This overestimation is also highlighted by several studies focusing on the comparison of the WRF

predictions with observations. In particular, the evaluation of the performance of WRF major parameters, such as the 2 m air temperature (Iriza et al., 2016), and particularly lower air temperature (Zhang et al., 2013; Chaskos et al., 2023; Liu, 2024), the relative humidity during the warm period (Sicard et al., 2021), seasonal precipitation (Sicard et al.,
<figure><img src="image_15.png" /><figcaption>Fig. 13. ETa AquaCrop (continuous line) and ETa,sat based on satellite data (points) in the Experimental Field "HubIS" for maize, in 2021.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 14. ETa AquaCrop (continuous line) and ETa,sat based on satellite data (points) in the Experimental Field "HubIS" for cotton, in 2021.</figcaption></figure>

2021; Sofiati et al., 2025), especially on spring (Varga and Breuer, 2024a), 10 m wind speed (Iriza et al., 2016; Pappa et al., 2023), wind speed under different planetary boundary layer (PBL) physics schemes (Yu et al., 2022), amplitude of soil moisture (Pappa et al., 2023), and solar radiation under a mean diurnal cycle, especially after midday (Pappa et al., 2023), at various spatial resolutions over several regions, resulted in their overestimation. In other studies, the WRF output overestimates the lower wind speeds, while underestimates the strong wind speeds (Giannaros et al., 2017; Solbakken and Birkelund, 2018). Solar radiation is a major weather parameter with increased difficulty in being downscaled from global to regional scale, having remarkable overestimation against observed data (Srivastava et al., 2015). Yet, the minimum temperature WRF underestimation was pointed by Varga and Breuer (2024b) in low-altitude regions in the presence of snow cover. In Fig. 13 and Fig. 14, the ETa (maize and cotton) calculated by the AquaCrop is represented by a continuous line, while the corresponding one from the satellite images is illustrated with points (growing season 2021), the latter being overestimated in comparison with the agronomic simulation. For this reason, a regression between AquaCrop and Sentinel ETa data was carried out and an adjustment factor was calculated to improve the accuracy of the satellite method. The two regressions are presented in Fig. 15 for maize and Fig. 16 for cotton, respectively.

3.1.4.1. Growing seasons 2022 and 2023 (Validation process). The calibrated AquaCrop model according to 2021 data was run for the 2022 and 2023 growing seasons, for which the soil moisture results in the root zone are presented in four individual diagrams of Fig. 17 (2022) and

<figure><img src="image_17.png" /><figcaption>Fig. 15. Regression between AquaCrop and Sentinel values for Maize, in 2021.</figcaption></figure>

Fig. 20 (2023). The statistical indices of measured and simulated soil moisture values are given in Table 9 (2022) and Table 10 (2023).

The soil moisture statistics for the growing seasons 2022 and 2023 -apart from the Maize field "HubIS" for 2023- are worse than the sensor installation's year 2021. This must be because the sensors remained in
<figure><img src="image_18.png" /><figcaption>Fig. 16. Regression between AquaCrop and Sentinel values for Cotton, in 2021.</figcaption></figure>

the field during the winter, resulting in micro-shifts and reduced sensor-soil contact due to agricultural machinery loads. Considering the crop yields of 2022, the simulated maize yield reached 17.427 Mg/ha for the HubIS plot and 16.798 Mg/ha for the Farmer plot, with the observed crop yield being equal to 14 Mg/ha and 17 Mg/ha respectively. For cotton, the AquaCrop yield was predicted equal to 5.549 Mg/ha for the HubIS plot and 5.893 Mg/ha for the Farmer plot, with the observed cotton yield being equal to 4.68 Mg/ha and 4.73 Mg/ha correspondingly.

Another index studied for the growing season of 2022 was the Water Use Efficiency (WUE) determined as the ratio of the observed crop yield per unit of water used (actual ET) (Zwart and Bastiaanssen, 2004; Hsiao et al., 2009; Katerji et al., 2013; Chai et al., 2016), as shown in Eq. (6).

$$WUE = \frac{Y}{ET_a} \qquad (6)$$

More recent studies denote that the ratio of the physical crop yield, or the marketable yield, over actual evapotranspiration, is referred to crop water productivity (CWP) (Blatchford et al., 2018; Fernández et al., 2020; Tarkalson et al., 2022; Mehmood et al., 2023).

For the 2022 growing season, regarding the maize field, irrigation planning led to WUE equal to 3.11 kg/m³ in the HubIS plot, whereas equal to 3.71 kg/m³ in the Farmer plot. As for the cotton field, the crop irrigation planning slightly improved the WUE (0.94 kg/m³ in the HubIS plot instead of 0.93 kg/m³ in the Farmer plot).

Regarding 2023, the simulated yields for both crops were increased enough in relation to the actual yields. The reduce of the observed yields of the studied fields is justified from the significant damages caused to the farms from the passage of storm Daniel over the Thessaly plain on September 4–7, 2023, leading to flooding.

The daily actual evapotranspiration with satellite ETa (sat) values for maize and cotton regarding 2022 and 2023, considering the respective adjustment factors (validation periods), are presented (HubIS and Farmer plots). The diagrams also show the corresponding field ETa values estimated through the calibrated AquaCrop model (Fig. 19, Fig. 20, Fig. 21, and Fig. 22).

Three parameters were considered in the calibration of the AquaCrop model for maize and cotton, regarding 2021: soil moisture, yield and LAI. In Fig. 10, we observe that the simulated soil moisture and the measured one have a satisfactory agreement. This is also shown by the statistical indicators presented in Table 8. In terms of yield the deviation (measured and simulated values) is 17.8 % & 9.8 %, for maize and cotton respectively. Other studies have also pointed the satisfactory performance of AquaCrop for simulating the maize yield compared to the measured one, for various treatments. Abedinpour et al. (2012) reported a prediction error ranging from 1.35 % to 9.9 %, while, Sandhu and Irmak (2019b) resulted in a yield deviation between –40.5 % and 8.21 % during three monitored years of normal precipitation. For

<figure><img src="image_19.png" /><figcaption>Fig. 17. Values of the observed soil water content (black points), and simulated soil water content (blue area), in the root zone, in a soil profile of 1 m, for: a) the experimental Maize field "HubIS", b) the experimental Maize field "Farmer", c) the experimental Cotton field "HubIS", and d) the experimental Cotton field "Farmer", regarding 2022.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Fig. 18.** Values of the observed soil water content (black points), and simulated soil water content (blue area), in the root zone, in a soil profile of 1 m, for: a) the experimental Maize field "HubIS", b) the experimental Maize field "Farmer", c) the experimental Cotton field "HubIS", and d) the experimental Cotton field "Farmer", regarding 2023.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Fig. 19.** Maize validation results of actual evapotranspiration from AquaCrop (continuous line) with the corresponding one from satellite observation (adjusted points), for "HubIS" and "Farmer" plot, in 2022.</figcaption></figure>

cotton, Tsakmakis et al. (2018) noted a yield deviation of simulations and measurements among -1.5% and 3.3% for two monitoring years, while, Masasi et al. (2020) reported a prediction error from -36 to 13% for various treatments, both for calibration validation periods. Also, regarding the LAI, it becomes clear that in the case of maize (Fig. 12), there is a similar trend of variation/change in the LAI values calculated with the two methodologies (satellite images and AquaCrop). After all, this is also highlighted by the value of R²= 0.96 in the correlation of these values concerning maize. In the case of cotton (Fig. 12), although the trend in the two curves is similar, the values in LAI from AquaCrop in the first phase of the growing season are clearly lower, while higher in the second half. The coefficient of determination R² of the values of the two LAI curves for cotton is 0.84. The growing seasons 2022 and 2023 were the validation periods of the AquaCrop model. In Fig. 17 and

Fig. 18, we observe a good coincidence (but not as in 2021) of the simulated soil moisture and the measured one (see also statistical indicators in Table 9 and Table 10). The statistical indicators are worse (in comparison to the corresponding of 2021) since the sensors remained on the ground during the winter period and their behavior was affected by the cultivation machinery loads (small displacements of sensors, reduction of sensor-soil contact). Regarding the yield (estimated and achieved), for 2022, it had 24.5% & -1.2% deviation in maize HubIS and Farmer plot respectively, while 18.6% & 24.6% deviation in cotton HubIS and Farmer plot correspondingly. For 2023, the observed yields of both crops decreased due to the great damages caused to the fields by the storm Daniel.

Considering the calibration results, ETa estimates from AquaCrop were considered reliable (hereafter referred also as 'field values') and
<figure><img src="image_22.png" /><figcaption>**Fig. 20.** Maize validation results of actual evapotranspiration from AquaCrop (continuous line) with the corresponding one from satellite observation (adjusted points), for "HubIS" and "Farmer" plot, in 2023.</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**Fig. 21.** Cotton validation results of actual evapotranspiration from AquaCrop (continuous line) with the corresponding one from satellite observation (adjusted points), for "HubIS" and "Farmer" plot, in 2022.</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Fig. 22.** Cotton validation results of actual evapotranspiration from AquaCrop (continuous line) with the corresponding one from satellite observation (adjusted points), for "HubIS" and "Farmer" plot, in 2023.</figcaption></figure>

became the basis for the calibration of the ETa,sat model. The first year (2021) was used for model calibration and the other two (2022 and 2023) for verification.

**Fig. 10** shows the spatial variation of ETa,sat in the experimental fields of Maize for 2021. During this year, there was no differentiation in

terms of irrigation. Values range from ≤ 2 mm/day (at the beginning and end of the growing season, namely on the 13th of May and 18th of September 2021), and up to > 8.0 mm/day in July and August 2021, the warmest period of the irrigation season.

As for the experimental fields of Cotton, in **Fig. 11**, the spatial
<caption>**Table 9**</caption>

Soil moisture statistical indices of maize and cotton “HubIS” and “Farmer” simulations for 2022.

<table><caption>**Table 10**</caption><thead><tr><th rowspan="2">Statistical indices</th><th colspan="2">Maize</th><th colspan="2">Cotton</th></tr><tr><th>HubIS</th><th>Farmer</th><th>HubIS</th><th>Farmer</th></tr></thead><tbody><tr><td>Pearson Correlation Coefficient (r)</td><td>0.57</td><td>0.65</td><td>0.35</td><td>0.28</td></tr><tr><td>Root Mean Square Error - RMSE (mm water)</td><td>24.1</td><td>17.8</td><td>30.7</td><td>29.9</td></tr><tr><td>Willmott's index of agreement</td><td>0.47</td><td>0.77</td><td>0.61</td><td>0.41</td></tr></tbody></table>

Soil moisture statistical indices of maize and cotton “HubIS” and “Farmer” simulations for 2023.

<table><thead><tr><th rowspan="2">Statistical indices</th><th colspan="2">Maize</th><th colspan="2">Cotton</th></tr><tr><th>HubIS</th><th>Farmer</th><th>HubIS</th><th>Farmer</th></tr></thead><tbody><tr><td>Pearson Correlation Coefficient (r)</td><td>0.87</td><td>0.33</td><td>0.50</td><td>0.29</td></tr><tr><td>Root Mean Square Error - RMSE (mm water)</td><td>13.8</td><td>18.9</td><td>23.3</td><td>23.2</td></tr><tr><td>Willmott's index of agreement</td><td>0.85</td><td>0.59</td><td>0.52</td><td>0.43</td></tr></tbody></table>

distribution of ETa,sat in 2021 is presented. Also, in this case there was no irrigation differentiation. Values of ETa range from ≤ 2 mm/day (limit values on 13 and 28 May 2021) to > 8.0 mm/day also in July and August 2021.

We notice that in Fig. 13 and Fig. 14, satellite approach leads to an overestimation in some phases of the growing season. In order the estimated ETa,sat values to be close to the field values (ETa AquaC) a linear correlation was made and a coefficient of determination R² was determined which was 0.87 for maize and 0.97 for cotton. The adjustment coefficients are 0.605 for maize and 0.63 for cotton (see Fig. 15 and Fig. 16). The correction on ETa,sat is necessary due to the already justified overestimation of major WRF parameters (air temperature, wind speed, solar radiation). Moreover, perhaps other Copernicus missions over Europe in the future, such as Land Surface Temperature Monitoring (LSTM) and Copernicus Imagine Microwave Radiometer (CIMR) might produce high-quality images in order to compare with the ones produced by Sentinel-2 and Sentinel-3.

Besides the fact that there is rich literature regarding the synergia of remote sensing and agronomic simulation but there are only a few papers to study irrigated crops such as cotton and maize and the irrigation requirements and the ETa as well. Battude et al. (2017) studies water needs and total irrigation depths of maize crop in the southwest of France using high spatial and temporal resolution satellite imagery (Formosat-2, SPOT, SPOT4-Take5, Landsat-8, and Deimos-1) and SAFY-FAO simulation model. Also, Han et al. (2020) uses maximum canopy coverage CCx* and the relative biomass Brel Remote Sensing (MODIS) and AquaCrop for maize seed in China. Li et al. (2020) uses the PSO algorithm to combine remote sensing data (crop canopy and biomass) and the AquaCrop model.

Akbari et al. (2024) study assimilating Satellite-Based biophysical variables Data into AquaCrop model for silage maize yield estimation using water cycle algorithm in Qaleh-Now County, southern Tehran, in 2019. Abi Saab et al. (2021), couple Remote Sensing Data and AquaCrop model for simulation of Winter Wheat growth under rainfed and irrigated conditions in a Mediterranean environment examining measured canopy cover and Leaf Area Index (LAI) data to remote sensing canopy cover. Sallah et al. (2019) study batch-processing of AquaCrop for rainfed maize using Remote Sensing derived Fractional Vegetation Cover data for the biomass assessment. Also, Panek-Chwastyk et al. (2024) study the assessment of grassland biomass Prediction Using AquaCrop coupling with Sentinel-2 Data and ground measurements in Wielkopolska and Podlasie Regions, in Poland. Parida et al. (2023) uses Sentinel-2 Data and AquaCrop Model for crop type discrimination and yield prediction respectively in Hazaribagh District, Jharkhand.

Dalla Marta et al. (2019), Ihuoma et al. (2021) and Pelosi et al. (2022) study the tomato water requirements using the synergia of Sentinel 2 and AquaCrop. Dalla Marta et al. (2019) and Pelosi et al. (2022) study the tomato water requirements in Italy, while Ihuoma et al. (2021) study the tomato water requirements in Canada.

### 3.1.5. Producers interest on precision agriculture

The increased climate variability leads to a vulnerable agricultural production, with the dominant issue being the serious shortage of water resources, while the water needs are constantly increasing. The aim of precision agriculture is to reduce the applied irrigation water. For this purpose, at the field level, crop monitoring systems are implemented in experimental fields, with the installation of meteorological stations, soil moisture sensors and hydrometers. Moreover, remote sensing tools are used to estimate the actual evapotranspiration of the monitored crop at a daily basis. Another very useful tool used, is agronomic simulation, in which ground-based monitoring data is introduced to manage irrigation water consumption based on the water needs, along with the estimation of the biomass and dry yield of a crop.

The scientific team that studies the application of the aforementioned innovative tools for the monitoring of typical fields of annual spring crops in the Thessaly region, has already organized several workshops to communicate the methodology to producers and agronomists. In these workshops, agricultural professionals have expressed their interest to include some of their fields in a monitoring program by our scientific team. Agronomists who have participated in the workshops understand the need to implement innovative solutions in agriculture in the context of water saving. Some farmers, mainly of older generations, still remain sceptical about the application of new technologies in their fields, given the costs of investing in monitoring tools. However, new producers seem more willing to be informed about alternatives to traditional agricultural practices that may help in efficient irrigation rates, as well as to the collaboration with agronomists for this purpose.

Apart from the useful tools for precision agriculture, with the aim of sustainable management and saving of irrigation water, our research team also created a supportive environment for farmers, through an online platform. On this platform, the producers are informed about the weekly water needs of each previous week (evaluation at the end of the week) and the corresponding next week (forecast at the beginning of the week), covering a period from early August until the harvest of each crop (3DSA General Aviation Applications, 2025). The water needs' forecast for each upcoming week is made using the WRF meteorological forecast model, which produces a high-resolution meteorological forecast of the next seven days.

## 3.2. La Crau, French study area

### 3.2.1. Spatial distribution of ETa Remote Sensing in the hay fields and PILOTE model

In the growing season of 2021, 22 Sentinel images with a maximum cloud cover threshold of 20 % were analysed for the daily ETa,sat estimation in the experimental field in the Crau area (Table 11). The spatial distribution of daily ETa,sat in the Hay experimental field is presented on Fig. 23. During May and at the end of July, the higher values of ETa, sat are observed. The ETa,sat was low during the first three months of observations, namely in January to March 2021.

### 3.2.2. Correlation of ETa remote sensing and ETa PILOTE

Fig. 24 presents the actual ETa from PILOTE simulation model calibrated using ground data and LAI satellite data (continuous line), as well as the corresponding one from satellite observation data (remote sensing method of 17 steps) (points) in the Hay fields, which is overestimated in relation to the PILOTE simulation. Fig. 25 shows the regression between the ETa PILOTE and the ETa, sat. In Fig. 25, the R² of the regression is presented, as well as the adjustment factor to the satellite estimations
<table><caption>**Table 11**
Daily ETa,sat variances of hay experimental field in 2021 (mm).</caption><thead><tr><th rowspan="2">Dates</th><th colspan="5">Daily ETa,sat variations (mm) for Hay Experimental Fields</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Median</th><th>Standard deviation</th></tr></thead><tbody><tr><td>14-Jan</td><td>0.00</td><td>0.70</td><td>0.07</td><td>0.00</td><td>0.16</td></tr><tr><td>19-Jan</td><td>0.99</td><td>1.25</td><td>1.11</td><td>1.11</td><td>0.04</td></tr><tr><td>24-Jan</td><td>0.75</td><td>1.31</td><td>0.99</td><td>0.99</td><td>0.10</td></tr><tr><td>21-Feb</td><td>0.85</td><td>2.13</td><td>1.48</td><td>1.45</td><td>0.21</td></tr><tr><td>10-Mar</td><td>1.62</td><td>3.55</td><td>2.53</td><td>2.53</td><td>0.31</td></tr><tr><td>20-Mar</td><td>0.00</td><td>3.01</td><td>0.78</td><td>0.79</td><td>0.67</td></tr><tr><td>25-Mar</td><td>2.66</td><td>4.77</td><td>3.86</td><td>3.83</td><td>0.61</td></tr><tr><td>30-Mar</td><td>1.90</td><td>4.70</td><td>3.42</td><td>3.44</td><td>0.56</td></tr><tr><td>2-Apr</td><td>3.54</td><td>5.68</td><td>4.68</td><td>4.67</td><td>0.58</td></tr><tr><td>7-Apr</td><td>0.68</td><td>3.40</td><td>1.64</td><td>1.61</td><td>0.49</td></tr><tr><td>12-Apr</td><td>3.87</td><td>5.43</td><td>4.69</td><td>4.73</td><td>0.23</td></tr><tr><td>12-May</td><td>5.07</td><td>7.16</td><td>6.07</td><td>6.14</td><td>0.40</td></tr><tr><td>27-May</td><td>5.57</td><td>7.44</td><td>6.30</td><td>6.21</td><td>0.31</td></tr><tr><td>3-Jun</td><td>4.54</td><td>6.19</td><td>5.53</td><td>5.50</td><td>0.26</td></tr><tr><td>13-Jun</td><td>2.26</td><td>5.22</td><td>3.58</td><td>3.63</td><td>0.65</td></tr><tr><td>18-Jul</td><td>3.34</td><td>6.68</td><td>5.22</td><td>5.28</td><td>0.60</td></tr><tr><td>28-Jul</td><td>5.32</td><td>8.40</td><td>6.19</td><td>5.98</td><td>0.59</td></tr><tr><td>10-Aug</td><td>3.85</td><td>6.06</td><td>5.15</td><td>5.18</td><td>0.39</td></tr><tr><td>17-Aug</td><td>3.14</td><td>6.35</td><td>4.78</td><td>4.73</td><td>0.67</td></tr><tr><td>30-Aug</td><td>2.48</td><td>6.68</td><td>4.39</td><td>4.29</td><td>0.87</td></tr><tr><td>1-Sep</td><td>3.91</td><td>6.61</td><td>5.47</td><td>5.32</td><td>0.46</td></tr><tr><td>11-Sep</td><td>3.95</td><td>6.09</td><td>5.39</td><td>5.25</td><td>0.39</td></tr></tbody></table>

obtained for hay using data from the 2021 growing season, in order that the accuracy of the sentinel observations is improved. Finally, Fig. 26 indicates the actual ET from PILOTE simulation model and the ETa, sat (adjusted) for the 2021 Hay growing season. Fig. 26 presents only the improvement of ETa, sat estimation; data from another cultivation period are needed for real validation.

A similar overestimation of ETa, sat in the middle stages of the

growing season, as in the case of maize and cotton in Greece, was also presented in the case of hay in France (La Crau experimental field area) (Fig. 26). The correlation performed among the ETa, sat values and the field values (ETa PILOTE) resulted in an adjustment coefficient of 0.59 with a coefficient of determination of R²= 0.88 (Fig. 27). It is noted that in the case of hay, the PILOTE model used was well calibrated and the results have already been published (El Haij et al., 2016). The applied correction on ETa, sat is of great requirement, since major weather parameters utilized by the WRF model are already justified to be overestimated in relation to observations. It becomes clear that in all cases (Falani area, Thessaly, Greece, for maize and cotton, and La Crau plain, southern France, for hay) the correlation leads to similar results with an adjustment coefficient ranging from 0.59 to 0.63 and a coefficient of determination R² varying in the range 0.87–0.97. Regarding the validation of the satellite model calculating ETa, sat with the remote sensing 17-steps method, we find that, the use of the corresponding adjustment factor, for maize and cotton in Falani (Central Greece), allows a satisfactory estimation of the field values although there are cases of significant deviation (mainly underestimation) (Fig. 21 to Fig. 24). In the case of hay in the La Crau region (France), the calculated adjustment factor improved the estimation of ETa, sat (Fig. 28). Of course, new measurements are necessary to validate the proposed method. A comparison between the LAIs derived from BV-NNET (using optical images) and the LAIs derived from hemispherical photos was performed. The results showed unbiased estimations of LAI with a Root Mean Square Error (RMSE) of 0.66 m²/m² and a Relative Root Mean Square Error (RRMSE) of 29 %.

## 4. Conclusion

In this research work, it was shown that it is possible to reliably estimate the daily actual evapotranspiration of maize, cotton and hay

<figure><img src="image_25.png" /><figcaption>**Fig. 23.** Spatial distribution of daily ETa, sat for the Hay Experimental Fields, in 2021.</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Fig. 24. ETa PILOTE (continuous line) and ETa, sat from satellite observation (points) for the Hay Experimental Fields, in 2021.</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Fig. 25. Regression between PILOTE and Sentinel values for Hay, in 2021.</figcaption></figure>

using satellite data. The ESA-developed method based on the dual energy balance was improved in two ways: 1) it used the reliable WRF model in place of ERA5, the fifth generation of ECMWF to estimate the meteorological parameters, and 2) it was calibrated and validated against field measurements and simulations in experimental fields in Greece (for maize and cotton), and in France (for hay). The results of satellite estimates and field estimates (through measurements and simulations) showed differences up to 50 %, while the improvement had already been made using WRF. By determining a correction/adjustment factor, which in all cases was ~0.60 (R²=0.87–0.97), a good estimate of the daily evapotranspiration for the three crops was achieved. Finally, this approach is particularly useful since it allows reliable estimates of actual crop water consumption, which will permit us to effectively guide farmers in their irrigation, while at the same time, it will help us in the overall management of water resources in wider areas.

However, the results for the method validation carried out lead us to conclude that the estimation of the actual daily evapotranspiration is likely to deviate significantly from the actual one in the field. Yet, for the calculation of a longer period, for example at a weekly step or more, the estimation of ETa will be improved.

In the context of climate change adaptation, future agronomic simulation scenarios might concern the application of the same irrigation rates in the monitored fields of annual spring crops which are cultivated in Thessaly (maize, cotton) under the climate scenarios of

<figure><img src="image_28.png" /><figcaption>Fig. 26. Hay results of actual evapotranspiration from PILOTE (continuous line) with the corresponding ETa, sat from satellite observation (adjusted points), in 2021.</figcaption></figure>
moderate and high greenhouse gas emissions, for the near term and mid-
term future periods. The projected driest years could be calculated from
projected meteorological data of the WRF model, being of great interest
so as to study the effect of future severe meteorological conditions on the
crop growth and crop yield, with the already calibrated and validated
agronomic model (AquaCrop).

Another frequently met crop in the Thessaly region is winter wheat,
which is a non-irrigated crop, yet, in the context of the expected increase
of the greenhouse gas emissions, under a future prospect, an agronomic
simulation could examine the possible requirement of this particular
crop to be supplementarily irrigated.

CRediT authorship contribution statement

**Nicholas Dercas:** Conceptualization, Data curation, Formal analysis, Investigation, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing. **Georgios A. Tziatzios:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing. **Pantelis Sidiropoulos:** Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review and editing. **Sofia Sarchani:** Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing. **Giannis Paraslis:** Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – review and editing. **Gilles Belaud:** Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources. **Marios Spiliotopoulos:** Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – review and editing. **Stavros Sakellariou:** Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – review and editing. **Nikolaos Alpanakis:** Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review and editing. **Georgios Bourazanis:** Formal analysis, Investigation, Resources, Validation, Writing – review and editing. **Nicolas Dalezios:** Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

The authors would like to thank the PRIMA foundation (Partnership for Research and Innovation in the Mediterranean Area) which funded HubIS (Open innovation Hub for irrigation system in Mediterranean agriculture) scientific project. The European Agricultural Fund for Rural Development (EAFRD) as well as the Greek Ministry of Rural Development and Food are gratefully acknowledged for partly funding this research through the Rural Development Program of Greece 2014–2020: Measure 16 “Cooperation”. The authors also wish to thank the General Secretariat for Research and Innovation (GSRI) of the Ministry of Development of Greece.

Data availability

Data will be made available on request.

References

3DSA General Aviation Applications. https://3dsameteo.gr/prothess/irrigation.php (accessed on 6 May 2025).

Abdou, S.M.M., Engel, B.A., Emam, S.M., El-Latif, Abd, K.M, 2022. AquaCrop model validation for simulation wheat productivity under water stress condition. Commun. Soil Sci. Plant Anal. 53, 281–292.

Abedinpour, M., 2021. The comparison of DSSAT-CERES and AquaCrop models for wheat under Water–Nitrogen interactions. Commun. Soil Sci. Plant Anal. 52, 2002–2017.

Abedinpour, M., Sarangi, A., Rajput, T.B.S., Singh, M., Pathak, H., Ahmad, T., 2012. Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agric. Water Manag 110, 55–66. https://doi.org/10.1016/j.agwat.2012.04.001.

Abi Saab, M.T., El Alam, R., Jomaa, I., Skaf, S., Fahed, S., Albrizio, R., Todorovic, M., 2021. Coupling remote sensing data and AquaCrop model for simulation of winter wheat growth under rainfed and irrigated conditions in a Mediterranean environment. Agronomy 11 (11). https://doi.org/10.3390/agronomy11112265.

Abraha, B., Delbecque, N., Raes, D., Tsegay, A., Todorovic, M., Heng, L.E.E., Vanutrecht, E., Geerts, S.A.M., Garcia-Vila, M., Deckers, S., 2012. Sowing strategies for barley (Hordeum vulgare L.) based on modelled yield response to water with AquaCrop. Exp. Agric. 48, 252–271.

Adeboye, O.B., Schultz, B., Adeboye, A.P., Adekalu, K.O., Osunbitan, J.A., 2021. Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Inf. Process. Agric. 8, 419–436.

Adeboye, O.B., Schultz, B., Adekalu, K.O., Prasad, K., 2017. Modelling of response of the growth and yield of soybean to full and deficit irrigation by using aquacrop. Irrig. Drain. 66, 192–205.

Adla, S., Bruckmaier, F., Arias-Rodriguez, L.F., Tripathi, S., Pande, S., Disse, M., 2024. Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance. J. Environ. Manag. 353, 120248.

AERONET. Aerosol Robotic Network. https://aeronet.gsf.nasa.gov/ (accessed on 22 February 2015).

Aguirre-García, S.D., Aranda-Barranco, S., Nieto, H., Serrano-Ortiz, P., Sánchez-Canete, E.P., Guerrero-Rascado, J.L., 2021. Modelling actual evapotranspiration using a two source energy balance model with sentinel imagery in herbaceous-free and herbaceous-cover Mediterranean olive orchards. Agric. For. Meteorol. 311, 108692.

Akbari, E., Boloorani, A.D., Verrelst, J., Pignatti, S., 2024. Assimilating satellite-based biophysical variables data into AquaCrop model for maize maize yield estimation using water cycle algorithm. Remote Sens. 16 (24). https://doi.org/10.3390/rs16244665.

Akumaga, A., Tarhule, A., Yusuf, A.A., 2017. Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa. Agric. For. Meteor. 232, 225–234.

Allen, R., Tasumi, M., Trezza, R., 2007. Satellite-Based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. J. Irrig. Drain. Eng. 133, 380–394. https://doi.org/10.1061/(asce)0733-9437(2007)133:4(380).

Alpanakis, N., Tziatzios, G., Faraslis, I., Spiliotopoulos, M., Sidiropoulos, P., Sakellariou, S., Blanta, A., Brisimis, V., Dalezios, N., Dercas, N., 2022. Modeling and Estimation of Actual Evapotranspiration in 3 Mediterranean agricultural areas in France, Greece, and Portugal by the aspect of the Sentinel 2 and 3 observations. In Proceedings of the 2nd AgroEcoInfo, Volos, Greece, 30 June–1 July 2022, ISBN: 978-618-84403-8-8.

Alvar-Beltrán, J., Saturnin, C., Grégoire, B., Camacho, J.L., Dao, A., Migraine, J.B., Dalla Marta, A., 2023. Using AquaCrop as a decision-support tool for improved irrigation management in the sahel region. Agric. Water Manag 287, 108430. https://doi.org/10.1016/j.agwat.2023.108430.

Amiri, E., Bahrani, A., Irmak, S., Mohammadiyan Roshan, N., 2021. Evaluation of irrigation scheduling and yield response for wheat cultivars using the AquaCrop model in an arid climate. Water Supply 22, 602.

Approval of the 2nd Revision of the River Basin Management Plan of the Water District of Thessaly 2024. (https://wfdver.typeka.gr/el/management-plans-gr/2revision-approved-management-plans-gr/approved-2revision-el08-gr).

Araya, A., Habtu, S., Hadgu, K.M., Kebede, A., Dejene, T., 2010a. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agric. Water Manag 97, 1838–1846.

Araya, A., Keesstra, S.D., Stroosnijder, L., 2010b. Simulating yield response to water of teff (Eragrostis tef) with FAO's AquaCrop model. Field Crops Res 10 (116), 196–204.

Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., Samain, O., Roujean, J.L., Leroy, M., 2007. LAI, fAPAR and fcover CYCLOPES global products derived from VEGETATION: part 1: principles of the algorithm. Remote Sens. Environ. 110 (3), 275–286.

Bastiaanssen, G.M.W., 2000. SEBAL-based sensible and latent heat fluxes in the irrigated gediz basin, Turkey. J. Hydrol. 229, 87–100.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998a. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J. Hydrol. 212, 198–212. https://doi.org/10.1016/S0022-1694(98)00253-4.

Bastiaanssen, W.G.M., Pelgrum, H., Wang, J., Ma, Y., Moreno, J., Roerink, G.J., van der Wal, T., 1998b. The surface energy balance algorithm for land (SEBAL): part 2 validation. J. Hydrol. 212-213, 213-229.

Battude, M., Al Bitar, A., Brut, A., Tallec, T., Huc, M., Cros, J., Weber, J.J., Lhuissier, L., Simonneaux, V., Demarez, V., 2017. Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal
resolution satellite imagery. Agric. Water Manag. 189. https://doi.org/10.1016/j.agwat.2017.04.018.

Bello, Z.A., Walker, S., 2017. Evaluating AquaCrop model for simulating production of amaranthus (Amaranthus cruentus) a leafy vegetable, under irrigation and rainfed conditions. Agric. For. Meter. 247, 300–310.

Bharti, A., Hooda, V., Singh, A., et al., 2025. Advancing sustainable crop yield enhancement through environmentally friendly nanoparticle synthesis: a comprehensive review. J. Soil Sci. Plant Nutr. 25, 2314–2345. https://doi.org/10.1007/s42729-025-02269-3.

Blatchford, M.L., Karimi, P., Bastiaanssen, W.G.M., Nouri, H., 2018. From global goals to local Gains—A framework for crop water productivity. ISPRS Int. J. Geolnf. 7 (11), 414. https://doi.org/10.3390/ijgii7110414.

Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K., Thomsen, A., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ. 81 (2-3), 179–193. https://doi.org/10.1016/S0034-4257(01)00342-X.

Calera, A., Campos, I., Osann, A., D'Urso, G., Menenti, M., 2017. Remote sensing for crop water management: from ET modelling to services for the end users. Sensors 17, 1104.

CAN-EYE. https://can-eye.paca.hub.inrae.fr/ (accessed on 22 February 2015).

CESBIO. Center for the Study of the Biosphere from Space. http://www.cesbio.ups-tlse.fr/ (accessed on 22 February 2015).

Chai, Q., Gan, Y., Zhao, C., Xu, H.-L., Waskom, R.M., Niu, Y., Siddique, K.H.M., 2016. Regulated deficit irrigation for crop production under drought stress. a review. Agron. Sustain. Dev. 36, 3. https://doi.org/10.1007/s13593-015-0338-6.

Chaskos, D.C., Lolis, C.J., Kotroni, V., Bartzokas, A., 2023. On the dependence of WRF model air temperature and precipitation forecast skill on the weather type for northwestern Greece. Environ. Sci. Proc. 26, 165. https://doi.org/10.3390/environsciproc2023026165.

Chaves, J.V.B., Rosas, C.L.G., Ferraz, C.P.A., Aiello, L.H.F., Peche Filho, A., Mota, L.T.M., Longo, R.M., Ribeiro, A.I., 2025. Soil conservation and information technologies: a literature review. Smart Agric. Technol., 100935 https://doi.org/10.1016/j.atech.2025.100935.

Chevron, B., Vervoort, R.W., Albasha, R., Dairon, R., Le Priol, C., Mailhol, J.C., 2016. A framework to use crop models for multi-objective constrained optimization of irrigation strategies. Environ. Model. Softw. 86, 145–157.

Cortés-Ramos, J., León-Cruz, J.F., Meza-Landero, H.A., et al., 2025. Evaluation of the weather research and forecasting model to simulate historical hazardous freeze events in the semiarid northwestern Mexico. Theor. Appl. Clim. 156, 60. https://doi.org/10.1007/s00704-024-05278-x.

Courault, D., Hadria, R., Ruget, F., Olioso, A., Duchemin, B., Hagolle, O., Dedieu, G., 2010. Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region. Hydrol. Earth Syst. Sci. 14 (9), 1731–1744.

Dalla Marta, A., Chirico, G.B., Falanga Bolognesi, S., Mancini, M., D'Urso, G., Orlandini, S., De Michele, C., Altobelli, F., 2019. Integrating sentinel-2 imagery with aquacrop for dynamic assessment of tomato water requirements in Southern Italy. Agronomy 9 (7). https://doi.org/10.3390/agronomy9070404.

Besawsheh, M.K., Beslemes, D., Kouneli, V., Tigka, E., Bilalis, D., Roussis, I., Karydogianni, S., Mavroeidis, A., Triantafyllidis, V., Kosma, C., Zotos, A., Kakabouki, I., 2022. Environmental and regional effects on fiber quality of cotton cultivated in Greece. Agronomy 12 (4), 943. https://doi.org/10.3390/agronomy12040943.

Dercas, N., 2020. Agricultural water management in Greece. In: Negm, A.M., Romanescu, G., Zelenakova, M. (Eds.), Water Resources Management in Balkan Countries. Springer Water: Springer International Publishing, Cham, Switzerland, pp. 421–453. ISBN 978-3-030-22468-4.

Dercas, N., Dalezios, N.R., Stamatiadis, S., Evangelou, E., Glampedakis, A., Mantonakis, G., Tserlikakis, N., 2022. AquaCrop simulation of winter wheat under different n management practices. Hydrology 9, 56. https://doi.org/10.3390/hydrology9040056.

Díaz, J., Quinonez, Y., De-la-Hoz-Franco, E., Butt-Aziz, S., Mercado, T., Salcedo, D., 2025. Information and communication technologies used in precision agriculture: a systematic review. AgriEngineering 7 (6), 167. https://doi.org/10.3390/agriengineering7060167.

Dimitriou, E., Efstratiadis, A., Zotou, I., Papadopoulos, A., Iliopoulou, T., Sakki, G.-K., Mazi, K., Rozos, E., Koukouvinos, A., Koussis, A.D., Mamassis, N., Koutsoyiannis, D., 2024. Post-Analysis of daniel extreme flood event in thessaly, central Greece: practical lessons and the value of state-of-the-art water-monitoring networks. Water 16 (7), 980. https://doi.org/10.3390/w16070980.

Doorenbos, J., Kassam, A.H., 1979. Yield response to water. FAO Irrig. Drain. Pap. No. 33 FAO Rome Italy.

El Hajj, M., Baghdadi, N., Cheviron, B., Belaud, G., Zribi, M., 2016. Integration of remote sensing derived parameters in crop models: application to the PILOTE model for hay production. Agric. Water Manag. 176, 67–79. https://doi.org/10.1016/j.agwat.2016.05.017.

ERDAS IMAGINE. https://hexagon.com/products/erdas-imagine (accessed on 02 November 2024).

ESA, 2020. USER MANUAL FOR SEN-ET SNAP PLUGIN. V1.1.0 March 19, 2020, 37 pages. https://www.esa-sen4et.org/ (accessed on 02 November 2024).

Falaras, T., Dosiou, A., Tounta, S., Diakakis, M., Lekkas, E., Parcharidis, I., 2025. Mapping of flood impacts caused by the September 2023 storm daniel in Thessaly's plain (Greece) with the use of remote sensing satellite data. Remote Sens. 17 (10), 1750. https://doi.org/10.3390/rs17101750.

Farahani, H.J., Izzi, G., Oweis, T.Y., 2009. Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agron. J. 2009 101 (3), 469–476. https://doi.org/10.2134/agronj2008.0182s.

Faraslis, I., Dalezios, N.R., Alpanakis, N., Tziatzios, G.A., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., Dercas, N., Domínguez, A., Martínez-López, J.A., López-Urrea, R., 2023. Remotely sensed agroclimatic classification and zoning in water-limited Mediterranean areas towards sustainable agriculture. Remote Sens. 15 (24). https://doi.org/10.3390/rs15245720.

Farré, I., Faci, J.M., 2006. Comparative response of maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) to deficit irrigation in a Mediterranean environment. Agric. Water Manag. 83 (1–2), 135–143. https://doi.org/10.1016/j.agwat.2005.11.001.

Feldman, L. (1994). The maize root. In The maize handbook (pp. 29-37). New York, NY: Springer New York.

Fernández, J.E., Alcon, F., Diaz-Espejo, A., Hernandez-Santana, V., Cuevas, M.V., 2020. Water use indicators and economic analysis for on-farm irrigation decision: a case study of a super high density olive tree orchard. Agric. Water Manag 237, 106074. https://doi.org/10.1016/j.agwat.2020.106074.

Foster, T., Brozovic, N., Butler, A.P., Neale, C.M.U., Raes, D., Steduto, P., Fereres, E., Hsiao, T.C., 2017. AquaCrop-OS: an open-source version of FAO's crop water productivity model. Agric. Water Manag 181, 18–22. https://doi.org/10.1016/j.agwat.2016.11.015.

Franco-Navarro, J.D., Padilla, Y.G., Álvarez, S., Calatayud, Á., Colmenero-Flores, J.M., Gómez-Bellot, M.J., Hernández, J.A., Martínez-Alcalá, I., Penella, C., Pérez-Pérez, J. G., Sánchez-Blanco, M.J., Tasa, M., Acosta-Motos, J.R., 2025. Advancements in Water-Saving Strategies and Crop Adaptation to Drought: A Comprehensive Review. Physiologia Plantarum 177, e70332. https://doi.org/10.1111/ppl.70332.

García-Vila, M., Fereres, E., Mateos, L., Orgaz, F., Steduto, P., 2009. Deficit irrigation optimization of cotton with AquaCrop. Agron. J. 101, 477–487.

Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J.A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., Steduto, P., 2009a. Simulating yield response of quinoa to water availability with AquaCrop. Agron. J. 101, 499–508.

Geerts, S., Raes, D., Garcia, M., Taboada, C., Miranda, R., Cusicanqui, J., Mhizha, T., Vacher, J., 2009b. Modeling the potential for closing quinoa yield gaps under varying water availability in the bolivian altiplano. Agric. Water Manag 96, 1652–1658.

Ghimire, O.P., Kuraparthy, V., Jones, M., Campbell, B.T., Bridges Jr, W.C., Alege, F.P., Delhom, C., Narayanan, S., 2025. Better root length distribution in the deep soil profile enhances upland cotton performance. Field Crops Res. 325. https://doi.org/10.1016/j.fcr.2025.109805.

Ghosh, K., Sharma, S., 2021. Fermat Point-Based wireless sensor networks: a default choice for measuring and reporting farm parameters in precision agriculture. In: Choudhury, A., Biswas, A., Singh, T.P., Ghosh, S.K. (Eds.), Smart Agriculture Automation Using Advanced Technologies. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-6124-28.

Giannaros, T.M., Melas, D., Ziomas, I., 2017. Performance evaluation of the weather research and forecasting (WRF) model for assessing wind resource in Greece. Renew. Energy 102, 190–198. https://doi.org/10.1016/j.renene.2016.10.033.

Global Forecast System (GFS). https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php (accessed on 27 July 2025).

Gowda, P.H., Chávez, J.L., Howell, T.A., Marek, T.H., New, L.L., 2008. Surface energy balance based evapotranspiration mapping in the texas high plains. Sensors 8, 5186–5201.

Grigorieva, E., Livenets, A., Stelmakh, E., 2023. Adaptation of agriculture to climate change: a scoping review. Climate 11 (10), 202.

Hagolle, O., Dedieu, G., Mougenot, B., Debaecker, V., Duchemin, B., Meygret, A., 2008. Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2 images. Remote Sens. Environ. 112 (4), 1689–1701.

Han, C., Zhang, B., Chen, H., Liu, Y., Wei, Z., 2020. Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data. Agric. Water Manag. 240. https://doi.org/10.1016/j.agwat.2020.106288.

He, K., Yang, Q., Shen, X., Dimitriou, E., Mentzafou, A., Papadaki, C., Stoumboudi, M., Anagnostou, E.N., 2024. Brief communication: storm daniel flood impact in Greece in 2023: mapping crop and livestock exposure from synthetic-aperture radar (Sar). Nat. Hazards Earth Syst. Sci. 24 (7). https://doi.org/10.5194/nhess-24-2375-2024.

Heng, L.K., Evett, S.R., Howell, T.A., Hsiao, T.C., 2009. Validating the FAO AquaCrop model for irrigated and water deficit field maize. Agron. J. 101, 488–498.

Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., Fereres, E., 2009. AquaCrop: The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron. J. 101 (3), 448–459. https://doi.org/10.2134/agronj2008.0218s.

HubIS (2020-2024) Open innovation Hub for Irrigation Systems in Mediterranean agriculture. https://prima-hubis.org/ (accessed on 20 July 2025).

Hussein, F., Janat, M., Yakoub, A., 2011. Simulating cotton yield response to deficit irrigation with the FAO AquaCrop model. Span. J. Agric. Res. 9, 1319–1330.

HVA, 2025. Master plan water management in Thessaly in the wake of storm Daniel how to address Thessaly's water-related agricultural challenges. Ministry of Climate Crisis and Civil Protection (https://www.opengov.gr/civilprotection/?p= 7246).

Iatrou, M., Tziouvalekas, M., Tsitouras, A., Evangelou, E., Noulas, C., Vlachostergios, D., Aschonitis, V., Arampatzis, G., Metaxi, A., Karydas, C., Tziachris, P., 2024. Analyzing the impact of storm 'Daniel' and subsequent flooding on Thessaly's soil chemistry through causal inference. Agriculture 14, 549. https://doi.org/10.3390/agriculture14040549.
Ihuoma, S.O., Madramootoo, C.A., Kalacska, M., 2021. Integration of satellite imagery and in situ soil moisture data for estimating irrigation water requirements. Int. J. Appl. Earth Obs. Geoinf. 102. https://doi.org/10.1016/j.jag.2021.102396.

IPCC, 2021. Climate change 2021: the physical science basis. In: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., Yelekçi, O., Yu, R., Zhou, B. (Eds.), Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Irina, A., Dumitrache, R.C., Lupascu, A., Stefan, S., 2016. Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory. Atm. ósfera 29 (1), 11–21. https://doi.org/10.20937/ATM.2016.29.01.02.

Janjic, Z.I., 2019. The surface layer parameterization in the NMM models. Natl. Meteorol. Cent. (U. S.) Rep. No. 497. https://doi.org/10.25923/9quej-k604inJ.

Janjic, Z.I., 1994. The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Weather Rev. 122 (5). https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>-2.0.CO;2.

Katerji, N., Campi, P., Mastrorilli, M., 2013. Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agric. Water Manag 130, 14–26.

Kisekka, I., Peddinti, S.R., Kustas, W.P., McElrone, A.J., Bambach-Ortiz, N., McKee, L., Bastiaanssen, W., 2022. Spatial-temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrig. Sci. 40 (4), 761–777.

Klein Tank, A.M., Wijngaard, J.B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A., ... & Petrovic, P. 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(12), 1441-1453. https://doi.org/10.1002/joc.773Data and metadataavailable at http://www.ecad.eu.

Kourgialas, N.N., 2021. A critical review of water resources in Greece: the key role of agricultural adaptation to climate-water effects. Sci. Total Environ. 775, 145857.

Lagarias, A., 2025. Climate change as spatial change: repercussions of the disastrous daniel flood in thessaly region on its territorial future. GeoJournal 90, 154. https://doi.org/10.1007/s10708-025-11407-4.

Li, X., Feng, Z., Ren, H., Z, Li, S., 2020. Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. Agric. Water Manag. 227. https://doi.org/10.1016/j.agwat.2019.105846.

Liu, S., 2024. Sensitivity of WRF-simulated 2m temperature and precipitation to physics options over the loess plateau. Adv. Meteorol. 2024, 6633255. https://doi.org/10.1155/2024/6633255.

Mailhol, J.C., Merot, A., 2008. SPFC: a tool to improve water management and hay production in the crau region. Irrig. Sci. 26 (4), 289–302.

Maniruzzaman, M., Talukder, M.S.U., Khan, M.H., Biswas, J.C., Nemes, A., 2015. Validation of the AquaCrop model for irrigated rice production under variedwater regimes in Bangladesh. Agric. Water Manag 159, 331–340.

Maraveas, C., Arvanitis, K.G., Bartzanas, T., Loukatos, D., 2025. Potential applications of quantum sensors in agriculture: a review. Comput. Electron. Agric. 235, 110402. https://doi.org/10.1016/j.compag.2025.110402.

Masasi, B., Taghvaeian, S., Gowda, P.H., Marek, G., Boman, R., 2020. Validation and application of AquaCrop for irrigated cotton in the Southern great plains of US. Irrig. Sci. 38, 593–607. https://doi.org/10.1007/s00271-020-00665-4.

McMichael, B. (1986). Growth of roots. In J.R. Mauney, & J.M. Stewart (Eds.), Cotton Foundation.

Mehmood, F., Wang, G., Abubakar, S.A., Zain, M., Rahman, S.U., Gao, Y., Duan, A., 2023. Optimizing irrigation management sustained grain yield, crop water productivity, and mitigated greenhouse gas emissions from the winter wheat field in north China plain. Agric. Water Manag 290, 108599. https://doi.org/10.1016/j.agwat.2023.108599.

Merot, A., Wéry, J., Isberie, C., Charron, F., 2008. Response of a plurispecific permanent grassland to border irrigation regulated by tensiometers. Eur. J. Agron. 28 (1), 8–18. Meteo. http://penteli.meteo.gr/stations/larissa/ (accessed on 20 November 2021).

Metergroup. https://www.metergroup.com/products/zl6-basic/ (accessed on 27 July 2025).

Mkhabela, M.S., Bullock, P.R., 2012. Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agric. Water Manag 110, 16–24.

Mkwenkwana, A., Matongera, T.N., Blaauw, C., Mutanga, O., 2025. A critical review on the applications of sentinel satellite datasets for soil moisture assessment in crop production. Int. J. Appl. Earth Obs. Geoinf. 141, 104647. https://doi.org/10.1016/j.jag.2025.104647.

Mourelatos, S., Charizani, E., Kalaitzopoulou, S., Tseni, X., Lazos, N., Tsioka, K., Pappa, A., Dafka, S., Rocklöv, J., Gewehr, S., 2025. Extreme flood and WNV transmission in thessaly, Greece, 2023. Sci. Rep. 15 (1), 22433. https://doi.org/10.1038/s41598-025-03884-x.

Mustafa, S.M.T., Vanuytrecht, E., Huysmans, M., 2017. Combined deficit irrigation and soil fertility management on different soil textures to improve wheat yield in drought-prone Bangladesh. Agric. Water Manag 191, 124–137.

NASA. National Aeronautics and Space Administration. https://www.nasa.gov/ (accessed on 22 February 2015).

National Centre for Environmental Prediction. https://www.emc.ncep.noaa.gov/emc/pages/ncep-numerical-forecast-systems.php#ncepda (accessed on 27 July 2025).

NOAA, 2001: National Oceanic and Atmospheric Administration Changes to the NCEP Meso Eta Analysis and Forecast System: Increase in resolution, new cloud microphysic, modified precipitation assimilation, modified 3DVAR analysis.

Norman, J.M., Kustas, W.P., Humes, K.S., 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteor. 77, 263–293. https://doi.org/10.1016/0168-1923 (95)02265-Y.

Pandey, H., Singh, D., Das, R., Pandey, D., 2021. Precision farming and its application. In: Choudhury, A., Biswas, A., Singh, T.P., Ghosh, S.K. (Eds.), Smart Agriculture Automation Using Advanced Technologies. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-6124-2_2.

Panek-Chwastny, E., Ozbilge, C.N., Dabrowska-Zielinska, K., Wróblewski, K., 2024. Assessment of grassland biomass prediction using AquaCrop model: integrating Sentinel-2 data and ground measurements in wielkopolska and podlasie regions, Poland. Agriculture 14 (6). https://doi.org/10.3390/agriculture14060837.

Pappa, A., Siouti, E., Pandis, S.N., Kioutsioukis, I., 2023. High-resolution WRF forecasts in the SmartAQ system: evaluation of the meteorological forcing used for PMCAMx predictions in an urban area. Atmos. Res. 296, 107041. https://doi.org/10.1016/j.atmosres.2023.107041.

Parida, B.R., Kumar, A., Ranjan, A.K., 2023. Crop types discrimination and yield prediction using sentinel-2 data and aquacrop model in hazaribagh district, jharkhand. KNJ. Cartogr. Geogr. Inf. 73 (1). https://doi.org/10.1007/s42489-021-00073-4.

Pelosi, A., Belfiore, O.R., D'Urso, G., Chirico, G.B., 2022. Assessing crop water requirement and yield by combining ERA5-Land reanalysis data with CM-SAF satellite-based radiation data and Sentinel-2 satellite imagery. Remote Sens. 14 (24). https://doi.org/10.3390/rs14246233.

Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop-The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 101, 438–447.

Raes, D., Steduto, P., Hsiao, T., Fereres, E., 2018. AquaCrop version 6.0-6.1, reference manual. FAO, Rome, Italy.

Rahman, H., Dedieu, G., 1994. SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Remote Sens. 15 (1), 123–143.

Raj, E.F.I., Appadurai, M., Athiappan, K., 2021. Precision farming in modern agriculture. In: Choudhury, A., Biswas, A., Singh, T.P., Ghosh, S.K. (Eds.), Smart Agriculture Automation Using Advanced Technologies. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-6124-2_4.

Rapid Radiative Transfer Model (RRTMG) for Global Circulation Models (GCMs). https://dtcenter.ucar.edu/GMTB/v4.0/sci_doc/GFS_RRTMG.html (accessed on 27 July 2025).

Roenink, G.J., Su, Z., Menenti, M., 2000. S-SEBI a simple remote sensing algorithm to estimate the surface energy balance. In: Phys. Chem. Earth Part B Hydrol. Ocean. Atmos, 25, pp. 147–157. https://doi.org/10.1016/S1464-1909(99)00128-8.

Ruescas, A.B., Peters, M. S3TBX Collocation Tutorial, V1/rev6 1.0, ESA & Brockmann Consult GMBH. 2022. https://step.esa.int/docs/tutorials/Collocation_S3TBX_Tutorial_v1_rev6.pdf.

Sallah, A.H.M., Tychon, B., Piccard, I., Gobin, A., Van Hoolst, R., Djaby, B., Wellens, J., 2019. Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived fractional vegetation cover data. Agric. Water Manag. 217, 346–355. https://doi.org/10.1016/j.agwat.2019.03.016.

Sandhu, R., Irmak, S., 2019a. Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed conditions. Agric. Water Manag. 224, 105753. https://doi.org/10.1016/j.agwat.2019.105753.

Sandhu, R., Irmak, S., 2019b. Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agric. Water Manag 223, 105687. https://doi.org/10.1016/j.agwat.2019.105687.

Shirazi, S.Z., Mei, X., Liu, B., Liu, Y., 2022. Estimating potential yield and change in water budget for wheat and maize across Huang-Huai hai plain in the future. Agric. Water Manag 260, 107282.

Shrestha, N., Raes, D., Vanuytrecht, E., Sah, S.K., 2013. Cereal yield stabilization in terai (Nepal) by water and soil fertility management modeling. Agric. Water Manag 122, 53–62.

Sicard, P., Crippa, P., De Marco, A., Castruccio, S., Giani, P., Cuesta, J., Paoletti, E., Feng, A., Anav, A., 2021. High spatial resolution WRF-Chem model over Asia: physics and chemistry evaluation. Atmos. Environ. 244, 118004. https://doi.org/10.1016/j.atmosenv.2020.118004.

Singh, A., 2021. Soil salinization management for sustainable development: a review. J. Environ. Manag. 277, 111383. https://doi.org/10.1016/j.jenvman.2020.111383.

SIRRIMED project. Sustainable use of irrigation water in the Mediterranean Region (2010 - 2014) http://www.sirrimed.org/index.php (accessed on 22 February 2015).

Skamarock, W.C.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Liu, Z.; Berner, J.; Wang, W.; Powers, J.G.; Duda, M.G.; Barker, D.M.; (2019). A Description of the Advanced Research WRF Version 4. In NCAR Tech. Note NCAR-TN-556+STR, 1st ed.; NCAR: Boulder, Colorado, USA, 2019; p. 145. [CrossRef].

Sofiati, I., Nurlatifah, A., Yulihastin, E., Ihsan, C.N., Wikacsana, S.K.J., Syahdiza, R., Izzaturahim, M.H., Hatmaja, R.B., Avia, L.Q., Budiman, A.S., Satyawardhana, H., 2025. Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent. Kuwait Journal of Science 52 (1), 100293. https://doi.org/10.1016/j.kjs.2024.100293.

Solbakken, K., Birkelund, Y., 2018. Evaluation of the weather research and forecasting (WRF) model with respect to wind in complex terrain. J. Phys. Conf. Ser. 1102, 012011. https://doi.org/10.1088/1742-6596/1102/1/012011.

Spiliotopoulos, M., Alpanakis, N., Tziatzios, G.A., Faraslis, I., Sidiropoulos, P., Sakellariou, S., Karoutsos, G., Dalezios, N.R., Dercas, N., 2023. Estimation of
remotely sensed actual evapotranspiration in water-limited Mediterranean agroecosystems for monitoring crop (cotton) water requirements. *Environ. Sci. Proc.* 25, 9. https://doi.org/10.3390/ECWS-7-14200.

Srivastava, P.K., Islam, T., Gupta, M., Petropoulos, G., Qiang Dai, Q., 2015. WRF dynamical downscaling and bias correction schemes for NCEP estimated Hydro-Meteorological variables. Water Resour. Manag. 29, 2267–2284. https://doi.org/10.1007/s11269-015-0940-z.

Steduto, P., Hsiao, T., Fereres, E., 2007. On the conservative behavior of biomass water productivity. Irrig. Sci. 25, 189–207.

Steduto, P., Hsiao, T., Fereres, E., Raes, D., 2012. Crop yield response to water. FAO Paper No. 66, FAO, Rome, Italy.

Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101, 426–437.

Steduto, P., Raes, D., Hsiao, T., Fereres, E., Heng, L., Izzi, G., Hoogeveen, J. AquaCrop: A new model for crop prediction under water deficit conditions. In Drought Management: Scientific and Technological Innovations, López-Francos, A., Ed., CIHEAM: Zaragoza, Spain, 2008.

Stricevic, R., Cosic, M., Djurovic, N., Pejic, B., Maksimovic, L., 2011. Assessment of the FAO AquaCrop model in simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agric. Water Manag 98, 1615–1621.

Su, Z., 2022. The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci. 6, 85–100. https://doi.org/10.5194/hess-6-85-2002.

Tarkalson, D.D., King, B.A., Bjorneberg, D.L., 2022. Maize grain yield and crop water productivity functions in the arid northwest U.S. Agric. Water Manag 264, 107513. https://doi.org/10.1016/j.agwat.2022.107513.

Todorovic, M., Albrizio, R., Zivotic, L., Abi-Saab, M.T., Stockle, C., Steduto, T.C., 2009. Assessment of AquaCrop, CropSyst, and WOFOST, models in the simulation of sunflower growth under different water regimes. Agron. J. 101, 509–521.

Tsakmakis, I.D., Zoidou, M., Gikas, G.D., Sylaios, G.K., 2018. Impact of irrigation technologies and strategies on cotton water footprint using AquaCrop and CROPWAT models. Environ. Process 5 (1), 181–199. https://doi.org/10.1007/s40710-018-0289-4.

Tsegay, A., Raes, D., Geerts, S., Vanuytrecht, E., Abraha, B., Deckers, J., Bauer, H., Gebrehiwot, K., 2012. Unravelling crop water productivity of tef (Eragrostis tef (Zucc.) Trotter) through AquaCrop in Northern Ethiopia. Exp. Agric. 48, 222–237.

Tsegay, A., Vanuytrecht, E., Abraha, B., Deckers, J., Gebrehiwot, K., Raes, D., 2015. Sowing and irrigation strategies for improving rainfed tef (Eragrostis tef (Zucc.) Trotter) production in the water scarce tigray region, Ethiopia. Agric. Water Manag 150, 81–91.

Unified Noah Land Surface Model. https://ral.ucar.edu/model/unified-noah-lsm (accessed on 27 July 2025).

Van Gaelen, H., Tsegay, A., Delbecque, N., Shrestha, N., Garcia, M., Fajardo, H., Miranda, R., Vanuytrecht, E., Abraha, B., Diels, J., et al., 2014. A semi-quantitative approach for modelling crop response to soil fertility: evaluation of the AquaCrop procedure. J. Agric. Sci. 153, 1218–1233.

Vanuytrecht, E., Raes, D., Willems, P., 2014. Global sensitivity analysis of yield output from the water productivity model. Environ. Model. Softw. 51, 323–332.

Vanuytrecht, E., Raes, D., Willems, P., 2016. Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium. Reg. Environ. Chang. 16, 659–672.

Varga, Á.J., Breuer, H., 2024a. Evaluation of multiple surface-, satellite-, reanalysis-, and WRF model-based gridded precipitation datasets over south-east central Europe. Atmos. Res. 298, 107138. https://doi.org/10.1016/j.atmosres.2023.107138.

Varga, A.J., Breuer, H., 2024b. Sensitivity analysis of snow depth and surface air temperature to various WRF/Noah-MP model configurations in central Europe. Atmos. Res. 311, 107659. https://doi.org/10.1016/j.atmosres.2024.107659.

Wang, Y., An, J., Shao, M., Wu, J., Zhou, D., Yao, X., Zhang, X., Cao, W., Jiang, C., Zhu, Y., 2025. A comprehensive review of proximal spectral sensing devices and diagnostic equipment for field crop growth monitoring. Precis. Agric. 26 (3), 54.

Wei, Q., Xu, J., Liu, Y., Wang, D., Chen, S., Qian, W., He, M., Chen, P., Zhou, X., Qi, Z., 2024. Nitrogen losses from soil as affected by water and fertilizer management under drip irrigation: development, hotspots and future perspectives. Agric. Water Manag. 296, 108791. https://doi.org/10.1016/j.agwat.2024.108791.

Yang, L., Lu, B., Schmidt, M., Natesan, S., McCaffrey, D., 2025. Applications of remote sensing for crop residue cover mapping. Smart Agric. Technol., 100880 https://doi.org/10.1016/j.atech.2025.100880.

Yu, E., Bai, R., Chen, X., Shao, L., 2022. Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of north China. Geosci. Model Dev. 15 (21), 8111–8134. https://doi.org/10.5194/gmd-15-8111-2022.

Zhang, H., Pu, Z., Zhang, X., 2013. Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Am. Meteorol. Soc. 28 (3), 893–914. https://doi.org/10.1175/WAF-D-12-00109.1.

Zwart, S.J., Bastiaanssen, W.G.M., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manag 69 (2), 115–133. https://doi.org/10.1016/j.agwat.2004.04.007.