Water plays a crucial role in food security. Currently, agriculture irrigation withdraws about 70% of the world’s fresh water. By 2050, the world population is estimated to increase to over 9 billion (UNDP, 2022). Together with climate change that is increasingly affecting the terrestrial ecosystem such as increasing the temperature, drought and extreme flood, all of which can lead to crop failure (IPCC et al., 2007) and water scarcity, the demand for food and irrigation water is expected to rise, dramatically. Accordingly, novel technologies for innovative, real-time water management for sustainable irrigation are necessary. In this regard, the main objective of this study is to simulate and predict the soil water content at the root zone, as a main factor of defining the irrigation time and quantity. Here, we have chosen a study area of 150km² which is located in west Europe, covering parts of Netherlands, Belgium, Luxemburg and west of Germany. Therewith, we have relied on the coupled land surface-subsurface CLM-Parflow model for hydrological simulations and the soil moisture data from on-site Cosmic-ray neutron sensor (CRNS) stations, as well as the SMAP (L3_SM_E_P), and high resolution C- and L-band Synthetic Aperture Radar (SAR) are used for data assessment. It is expected that the reliability of the soil moisture magnitude and dynamic will be examined.

How to cite: Moradi, S., Mengen, D., Vereecken, H., and Montzka, C.: Soil moisture simulation at the local scale using satellite remote sensing data towards sustainable irrigation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2385, https://doi.org/10.5194/egusphere-egu23-2385, 2023.

Variable rate irrigation is usually based on prescription maps delineated according to a static approach. Irrigation rate and timing are optimised by sensor and/or modelling-based methods applied within homogenous zones whose spatial distribution is kept constant during the crop season. The objective of this study was to develop a procedure based on the combination of the crop-energy-water balance model FEST-EWB-SAFY with remote sensing data of vegetation variables and land surface temperature to generate dynamic irrigation prescription maps. The crop-energy-water balance FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB, which allows computing continuously in time and distributed in space both soil moisture and evapotranspiration fluxes, and the SAFY (simple model for yield prediction and plant development).

The model was tested in a 30-ha field cultivated with soybean in 2022 at Ceregnano, in the lower zone of the Po Valley (Italy). Irrigation was provided by 270m long lateral move irrigation machine, equipped with a precision irrigation system with a lateral resolution of 34 m. The model was pixelwise calibrated with remotely sensed land surface temperature (LST, RMSE 1.3 °C) and leaf area index (RMSE 0.45) as well as local measured soil moisture at 10cm and 50cm depth (RMSE 0.04). Four dynamic prescription maps were generated during the season, calculating the pixel-by-pixel difference between the field retention capacity and the daily average of the 50-cm soil moisture profile. Dynamic variable rate irrigation was compared with a conventional irrigation system according to an experimental block design with three replicates and evaluated in terms of crop yield, irrigation volumes and water use efficiency.

FEST-EWB-SAFY allowed the creation of dynamic maps that captured the crop water requirement variability originated by the interaction of ET, soil properties and field management. Compared with the conventional system, there was a significant increase in water use efficiency, but not in crop yield. These results confirm that the model-based dynamic prescription maps could be used to optimize variable irrigation in highly spatio-temporal dynamic cropping systems

How to cite: Morari, F., Gabrieli, D., Furlan, L., Furlanetto, J., Misturini, D., Sobrino, J., Skokovic, D., and Corbari, C.: Optimizing variable rate irrigation using model-based dynamic prescription maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2653, https://doi.org/10.5194/egusphere-egu23-2653, 2023.

Even though irrigation is the largest direct anthropogenic interference with the terrestrial water cycle, limited knowledge on the amount of water applied for irrigation exist. Quantification of irrigation via evapotranspiration (ET) or soil moisture residuals between remote sensing models and hydrological models, with the latter acting as baselines of natural conditions without the influence of irrigation, have successfully been applied in various regions. Here, we implement an novel ensemble methodology to estimate the precision of ET-based net irrigation quantification by combining different ET and precipitation products in the Indus and Ganges basins. A multi-model calibration of 15 models independently calibrated to simulate natural rainfed ET was conducted prior to the irrigation quantification. Based on the ensemble average, the 2003-2013 net irrigation amounts to 233.4 mm/year (74.4 km³/year) and 101.4 mm/year (66.7km³/year) in Indus and Ganges basin, respectively. Net irrigation in Indus basin is evenly split between dry and wet period, whereas 70% of net irrigation occurs during the dry period in Ganges basin. We found that although annual ET from remote sensing models varied by 91.5 mm/year, net irrigation precision was within 25.3 mm/season during the dry period, which emphasizes the robustness the applied multi-model calibration approach. Net irrigation variance was found to decrease as ET uncertainty decreased, which related to the climatic conditions, i.e. high uncertainty under arid conditions. A variance decomposition analysis showed that ET uncertainty accounted for 74% of the overall net irrigation variance and that the influence of precipitation uncertainty was seasonally dependent, i.e. with an increase during the monsoon season. The results underline the robustness of the framework to support large scale sustainable water resource management of irrigated land.

How to cite: Kragh, S. J., Fensholt, R., Stisen, S., and Koch, J.: The precision of satellite-based net irrigation quantification in the Indus and Ganges basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4863, https://doi.org/10.5194/egusphere-egu23-4863, 2023.

Integrating remote sensing technology into crop growth models is a viable approach for water resources management and agricultural sustainability assurance since it allows crop water requirements and yield within agricultural fields to be estimated. Saudi Arabia has severely limited renewable water resources and non-renewable groundwater reserves that are rapidly depleting. Unlike rain-fed agriculture, the majority of agricultural water demand in Saudi Arabia is pumped from deep aquifers (up to 1000 m) to irrigate center pivots. This situation entails continuous monitoring of agricultural water use to enhance agricultural water productivity (i.e., producing more crops per drop) and preserve the equilibrium among the water, food, and energy sectors. The main purpose of this study is to calibrate the AquaCrop-OSPy model (an open-source Python implementation of the FAO crop-water productivity model, known as AquaCrop) using field data and Sentinel-2 (S2) images for operational mapping of crop yield, water demand, and water productivity. Another objective is to spatiotemporally estimate the energy requirements and associated CO2 emissions related to groundwater pumping for irrigation. The study area is located in the north of Saudi Arabia. It is a commercial farm with an area of 30,000 hectares comprising more than 200 agricultural fields with center-pivot irrigation systems. The crops cultivated on the farm are wheat and tomato. Field data were collected over three consecutive growing seasons (2019-2020, 2020-2021, and 2021-2022) and include information on wells, pumps, irrigation technique, field management practices, soil parameters, crop parameters, daily meteorological data, actual crop yield, and water use. The AquaCrop-OSPy model was first calibrated and validated using the collected field data as well as S2 images over the three seasons. Subsequently, the fractional vegetation cover (FVC) derived from S2 images was assimilated into the AquaCrop-OSPy model by direct insertion in place of AquaCrop-OSPy's simulated canopy cover (CC). Later, the energy requirements and CO2 emissions associated with irrigation groundwater pumping were estimated using crop water demand information calculated with the calibrated AquaCrop-OSPy model along with pumps and wells data. Coupling the S2-derived FVC and the AquaCrop-OSPy model improved AquaCrop-OSPy predictions of crop water demand, yield, and water productivity as S2 images provide spatialized FVC information every 6-days. This integration further permitted a robust quantification of the energy requirements and CO2 emissions associated with groundwater pumping for irrigation. These results, when applied to larger scales and multiple crops, can help develop a comprehensive understanding of the water-energy-agriculture nexus and indicate potential improvements in AquaCrop-OSPy estimates that could be achieved once remote sensing data are integrated.

How to cite: Almalki, A. S., López Valencia, O. M., Johansen, K., El Hajj, M. M., and McCabe, M. F.: Integration of Sentinel-2 Imagery with the AquaCrop-OSPy Model for Simulating Agricultural Crop Requirements and Growth in Desert Farming Systems: A Saudi Arabian Case Study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6776, https://doi.org/10.5194/egusphere-egu23-6776, 2023.

Water shortage is one of the issues people are facing globally nowadays especially in arid and semi-arid regions. In these regions the increase in air temperature with slightly changing rates of precipitation cause frequent droughts. This determines the necessity for the usage of irrigation in agriculture that, in turn, makes it the most water consuming sector of economy.

The detection of water stress using different approaches is crucial for irrigation scheduling and precise calculation of the volume of water that covers the gap needed for plants’ normal development.

It is known that the Photochemical Reflectance Index (PRI) is highly sensitive to the photosynthetic activity of plants. Especially that can be observed for forests and orchards due to the high heterogeneity of canopy structure. In previous studies it was found that PRI can be used as an indicator for monitoring water stress in plants. However, at present, no full answer is given about the limitations of PRI usability and no clear algorithm is formulated for its use in order to detect water stress of plants.

In this regard, we concentrate on studying the response of PRI to the water stress of olive trees rain-fed conditions for the case of semi-arid climate (Tunisia). We performed the analysis of data sets for 2021 and 2022, which are, respectively, a dry year and a year of normal water availability. The data sets included meteorological data, PRI measurements made every five minutes, sap flow measurements, soil moisture content values, and dendrometer measurements.

On the first stage of our study we processed PRI data sets in order to find an analytic function that best describes daily dynamics of the index. As the result, a new modeling function is constructed to describe an increase in PRI in the middle of the day when minimal PRI values are reached several hours after sunrise and before sunset. Such PRI behavior was mainly observed in sunny days of the dry year.

Further, we looked for the correlations of the characteristics of PRI daily dynamics, particularly minimal of PRI, with the values of sap flow that is a main measurable indicator in the class of transpiration and water stress models (see, e.g., https://doi.org/10.1016/j.agwat.2020.106343 ). In this context, the behavior of PRI was different for 2021 and 2022, which, in our opinion, related with weather conditions. For the dry year of 2021 we found a strong correlation between the minimum of PRI and sap flow in morning hours (R²= 0,68) during the summer season. For the normal year 2022 the same results are not obvious.

In the perspective of our study it is planned to compare the results for the rain-fed site with the measurements obtained under irrigated conditions. The final goal of the research we want to achieve is to propose a reliable approach for separation of physically meaningful part of PRI signal from the noises created by the canopy structure. This will lead us to the reliable algorithm of PRI usage for the detection of plants’ water stress condition.

How to cite: Danylenko, I., Le Dantec, V., Fanise, P., Boujnah, D., Cheheb, H., Gascoin, S., and Boulet, G.: Use of the Photochemical Reflectance Index to determine water stress in semi-arid climate conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13621, https://doi.org/10.5194/egusphere-egu23-13621, 2023.

Irrigation consumes around 70% of the world’s freshwater and has a significant impact on the continental water and energy cycles of the basins where it is present. Despite the clear benefits of irrigation, it has a strong impact on the continental water cycle, which must be evaluated to improve water resources management. Land-Surface Models (LSM) and remote sensing data can be used to analyse and quantify how irrigation affects the continental water cycle.

The Ebro basin is located in the Iberian Peninsula and is a representative Mediterranean basin. It is therefore characterised by a variety of different landscapes, as well as an uneven distribution of precipitation. This leads to the construction of a large network of dams and canals to supply water to agricultural irrigated districts. In fact, irrigated agriculture and farming represent 92% of the basin's total water consumption, according to the Ebro Hydrographic Confederation.

This work presents studies using datasets developed at the Ebro Observatory to simulate irrigation related processes over the Ebro basin with a LSM. It is provided at 1 km spatial resolution and contains meteorological and physiographical data, namely vegetation classes, actual irrigated areas, irrigation methods per area, and a new version of the SAFRAN meteorological forcing. All of the simulations used in the work presented here are carried out using the SURFEX LSM v9 version, which has an irrigation scheme implemented.

In the first place, we evaluate how the new physiographic datasets impact irrigation simulation in the area. Then, the datasets are used to perform simulations to analyse the impact of different irrigation scenarios (defined by different model parameters) on irrigation, evaporation, streamflow, and drainage. The scenarios defined are the default configuration of SURFEX’s irrigation scheme, a realistic simulation based on a survey to farmers from several irrigation districts from the Ebro basin, and further scenarios modifying the irrigation event’s frequency and amount of water. For this analysis, the simulations are carried out from 2008 to 2019. 

In the second place, a comparison of our simulation results to remote sensing irrigation estimations from the ESA funded IRRIGATION+ project is performed. For this, the irrigation estimation is added to the precipitation of the SAFRAN forcing, which is then used to force SURFEX simulations. The irrigation products span different periods ranging from 2015 to 2021 and are based on different techniques: data assimilation (Sentinel-1), SM-based DELTA algorithm (Sentinel-1), SM-based inversion algorithm (Sentinel-1, ERA5-Land, GLEAM product), and the Hydrological Similar Pixels (HSP) algorithm.

This work is a contribution to the LIAISE campaign, through the IDEWA project (PCI2020-112043), as well as to the IRRIGATION+ (4000129870/20/I-NB) project.

How to cite: Barella-Ortiz, A., Quintana-Seguí, P., Clavera-Gispert, R., Munier, S., Merlin, O., Olivera-Guerra, L.-E., Altés, V., Villar, J.-M., Brocca, L., Dari, J., Modanesi, S., Zapa, L., and Brombacher, J.: Analysis of the impact of different irrigation scenarios on the water balance of the Ebro River Basin by means of a LSM and remote sensing irrigation estimations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14091, https://doi.org/10.5194/egusphere-egu23-14091, 2023.

The Rhine catchment's water resources are likely to continue to experience considerable pressure from growing irrigated land. Concerns regarding the current and future status of water availability to balance conflicting water allocations for maintaining ecosystem services, shipping, and biodiversity are highlighted as freshwater resources become more limited. Contrary to other sectors, agricultural water consumption primarily consists of actual evapotranspiration, resulting in only a small amount of flow returning to receiving water bodies. As a feasible alternative, actual evapotranspiration is therefore increasingly used to quantify agricultural water use.  In hydrological models, agricultural water demand is typically assessed by the volume of water required to fully restore soil moisture to predefined thresholds for sustaining optimal crop growth, under the assumption that there will be enough water available during the growing season to fulfill the demand. However, the assumption of ideal crop growing circumstances (close to potential evapotranspiration) is not necessarily true in dry season, when limited water supply influences irrigation decision-making and will lead to inaccurate estimation of actual water use. This PhD research, as part of the HorizonEurope project Stars4Water, aims to produce historical spatiotemporal estimates of agricultural water use over the Rhine catchment by using satellite observations. To isolate the actual evapotranspiration due from irrigated land, the actual evapotranspiration from the hydrological model wflow_sbm without irrigation scheme will be compared against the actual evapotranspiration derived from satellite retrievals. Land surface temperature observations will be assimilated to constrain the actual evapotranspiration estimates to consider the relationship between the water and energy balance.

How to cite: Purnamasari, D., ter Maat, J., J. Teuling, A., and Weerts, A.: Modelling current and future water resources availability of the river Rhine, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14518, https://doi.org/10.5194/egusphere-egu23-14518, 2023.

Irrigation is widely recognized as the human activity that alters the natural circulation of water on the Earth’s surface the most. It greatly contributes to making the canonical conceptualization of the hydrological cycle incomplete. Nevertheless, irrigation dynamics are still generally unmonitored worldwide, but satellite capabilities have recently proved their suitability for such a purpose.

In this contribution, the first regional-scale and high-resolution data sets of irrigation water use retrieved from satellite data are presented. The products, obtained through the SM-based (Soil-Moisture-based) inversion approach, are an outcome of the Irrigation+ project (https://esairrigationplus.org/) funded by the European Space Agency (ESA). The data have been produced over the Ebro basin (Spain), the Po valley (Italy), and the Murray-Darling basin (Australia) and they are available at: https://zenodo.org/record/7341284#.Y7WHsHbMKUm. The irrigation estimates referring to the Spanish and the Italian pilot areas rely on Sentinel-1 soil moisture obtained through the RT1 (first-order Radiative Transfer) model and are characterized by a spatial resolution of 1 km. A 6 km spatial sampling has been adopted for the Murray-Darling basin; in this case, irrigation water amounts have been retrieved from CYGNSS (CYclone Global Navigation Satellite System) soil moisture. The data sets referring to the European sites cover a time span ranging from January 2016 to July 2020, while irrigation amounts over the Murray-Darling basin are available for the period April 2017 – July 2020. The reliability of the retrieved irrigation estimates has been assessed through comparison against benchmark amounts. Satisfactory performances have been found over the Ebro and the Murray-Darling basins. More in detail, a median value of RMSE, Pearson correlation, r, and BIAS equal to 12.4 mm/14-day, 0.66, and -4.62 mm/14-day, respectively, is found across pilot districts located within the Ebro basin. The analogous results obtained over the Murray-Darling basin are equal to10.54 mm/month, 0.77, and -3.07 mm/month. The evaluation over the Po valley is affected by the limited availability of in-situ reference data for irrigation. This study sheds light on the perspective of building operational systems aimed at monitoring agricultural water use relying on satellite data.

How to cite: Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Flammini, A., and Volden, E.: First regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6916, https://doi.org/10.5194/egusphere-egu23-6916, 2023.

Sustainable water use in agriculture, while ensuring high yield returns, is key to tackling challenges imposed by climate change and population growth. Knowing the crop water status within the field allows for optimized water consumption by matching management practices to the actual crop water demand. Science and applications communities have made clear the needs and requirements for daily, field-scale (< 100 m) evapotranspiration (ET) data for agricultural applications. Current and planned space missions with thermal infrared (TIR) measurements either have high-spatial or high- temporal resolution, but not both, making it hard to capture the field-scale variability required for irrigation and crop growth modeling.

Hydrosat has innovated through the technical barriers to achieving field-scale, global TIR and VNIR measurements for ET every day, multiple times per day. With an upcoming launch en route to a 16+ smallsat constellation, Hydrosat data will be a game-changer and will significantly advance our ability to monitor and manage agricultural systems. An Early Adopter daily 20-m surface temperature product is already available now and can be used to accurately track crop water supply and demand within a specific field.

Here we show the potential of a new method that combines the spatio-temporal advantage of Hydrosat Early Adopter (along with freely available satellite data) with the predictive ability of crop model simulations to overcome the limitations of existing methods of irrigation management at the field and sub-field levels. The method was validated over multiple corn fields in the US Corn Belt, exploring a wide range of environmental conditions and management practices and across multiple growing seasons (2019-2021). ET, soil moisture, and yield data collected during the season were used for validation.

First, high spatio-temporal resolution thermal and multispectral satellite data were used to derive ET and leaf area index (LAI) during the crop growing season. Using these products, phenological development and soil-water components of the APSIM crop model were calibrated to accurately determine (and improve upon) farm-level predictions, both in terms of soil moisture content and end-of-season yield. Our method successfully estimated soil moisture with high accuracy (RMSE of 1.43 mm/mm and rRMSE of 7.47 %) and predicted yield reliably up to 14 weeks before harvest, with a strong correlation to independently collected measurements (RMSE of 1162 kg/ha and rRMSE of 7 %). The proposed approach has the potential for driving irrigation management decisions while quantifying end-of-season yield without the need for in situ data.

How to cite: Ziliani, M. G., Werner, F., and Abelló, A.: Toward high resolution and daily thermal infrared measurements for agricultural water management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16403, https://doi.org/10.5194/egusphere-egu23-16403, 2023.

Leaf Area Index (LAI) is an indicator of crop and plant growth in agricultural and ecological research. LAI can be used to monitor nitrogen status or estimate crop yield and evapotranspiration (ET). The aim of this study was to evaluate use of a remotely sensed visible vegetation index to characterize the spatial variability of LAI within irrigated wheat fields. Variation of LAI was measured with a ceptometer on random nested grids at two sites with pre-determined management zones in 2019 and 2020. Coincident digital imagery was collected using a consumer-grade unmanned aerial vehicle (UAV). A visible atmospherically resistant index (VARI) LAI estimation model was applied to red, green, blue (RGB) UAV imagery using a ladder resampling approach from 0.06 m to 3 m spatial resolution. There was significant within-field spatial and temporal variation of mean LAI. For example, in May at one of the sites, measured LAI ranged from 0.21 to 2.58 and in June from 1.68 to 4.15. The relationship of measured and estimated LAI among management zones was strong (R²=0.84), validating the remote sensing approach to characterize LAI differences among management zones. There were statistically significant differences in estimated LAI among zones for all sampling dates (P=0.05).  We assumed a minimum difference of 15% between zone LAI and the field mean for justifying variable rate irrigation among zones, a threshold that corresponds with approximately a 10% difference in evapotranspiration rate. Three of the five sampling dates had LAI differences that exceeded the threshold for at least one zone, with all three having mean LAI of less than 2.5. The VARI model for estimating LAI remotely is more effective at identifying LAI differences among management zones at lower LAI.

How to cite: Hopkins, A., Hansen, N., Jensen, R., and Flint, E.: Spatial Variability of Leaf Area Index from Drone Imaging of Two Irrigated Wheat Fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10988, https://doi.org/10.5194/egusphere-egu23-10988, 2023.

Concerns about managing water resources and ensuring food security in arid and semi-arid regions have grown due to increasing demands on water resources and food production. The Water Accounting Plus (WA+) approach is employed in this context to determine water consumption pathways occurring on various land uses, crop production (Land Productivity: LP), and Water Productivity (WP) in the Mahi Basin, India. The WA+ framework is appropriate for the data-scarce region since it allows the use of satellite-driven datasets for analyzing hydrological processes. The Budyko curve concept is used to differentiate between irrigation- and rain-fed agriculture by identifying the green and blue water consumption (ET). The WA+ framework uses remote sensing-based datasets from various sources for this purpose, which were used in this study for the period of 2003-2020. The average ETgreen and ETblue in the Mahi basin are found to be 15.8 km³/year and 12.32 km³/year, respectively. The average LP and WP for both the irrigated and rainfed cereals in the basin are found as 2287.71 kg/ha & 1713.62 kg/ha and 0.721 kg/m³ & 0.483 kg/m³, respectively, from 2003 to 2020. The results also indicate that the basin is highly reliant on irrigation for agricultural activities, which are neither efficient nor productive. There is significant potential for improvement in water production and beneficial water usage by using proper water management techniques. This study emphasizes the significance of water accounting and information for decision-makers, researchers, and farmer communities to create realistic goals and increase crop production in water-scarce locations.

How to cite: Patle, P., Sharma, A., Singh, P. K., Ahmad, I., Matsuno, Y., Leh, M., and Ghosh, S.: Assessment of Water Consumption Pattern & Agricultural Production using Water accounting Plus (WA+) Framework: A case study of Mahi River basin , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2510, https://doi.org/10.5194/egusphere-egu23-2510, 2023.

The irrigated area cultivated with almond trees (Prunus dulcis) has significantly increased in recent years worldwide. In Spain, the extension covered  by irrigated almond orchards has doubled in the past 5 years, currently accounting for about 14% of the harvested almond area. The high water productivity of this crop jointly with the good market perspectives for the almonds have boosted this expansion in many areas, providing a viable alternative to traditional crops, such as cereals and other woody crops. However, the expansion of irrigated almond orchards in water-scarce areas could compromise the water resources and sustainability of agriculture. In this way, precise irrigation management tools are required to adjust the water supply to the actual crop requirements with suitable temporal and spatial resolutions. Accurate estimates of the net irrigation water requirements allow to improve the water use efficiency and therefore, achieve a more profitable and sustainable management. The soil water balance model (SWB) based on the FAO-56 dual crop coefficient approach (Allen et al., 1998) is a well-recognized procedure for the estimation of daily crop water requirements. This approach considers a single-layer soil water balance estimated at the root zone, jointly with soil evaporation in the surface layer.

This work introduces a Remote Sensing (RS) assisted approach to monitor the soil water balance in almond trees. Our experiment aims at comparing this RS-based technique to the traditional FAO-56 methodology. This RS-based approach integrates a basal crop coefficient (K_cb) derived from time series of satellite images into the daily soil water balance. Although well-documented in the literature for other crops, limited information is available for the application of this RS-based methodology to almond orchards.

This study was carried out in two drip irrigated almond commercial fields, located in the semi-arid province of Albacete (Southeastern Spain). Measurements of soil water content, and stem water potential, were available during the campaigns 2020-2022 in the analyzed fields. A continuous sampling of the canopy structure was registered. Thermal infrared radiometers were deployed to model the surface energy balance, and an eddy-covariance tower was placed to monitor the actual evapotranspiration. This instrumentation provides a unique opportunity to assess the performance of the SWB models. The results show an overall similar performance for both approaches in reproducing the temporal evolution of K_cb during the campaigns analyzed. The assessment of the results indicates the potential of both approaches to accurately estimate the temporal evolution of soil water content in irrigated almond fields under different water management, either comfort or water stress.

The operational application derived from the present study will provide the farmer with accurate information on the actual crop water demand and adjust and distribute water supply to the crop’s spatially and temporally varying water requirements. The information obtained is useful for making management decisions aimed at improving irrigation scheduling, developing controlled deficit irrigation (CDI) strategies, and promoting the optimization of water resources and the development of sustainable agriculture.

How to cite: Sánchez, J. M., Campoy, J., Montoya, F., López-Urrea, R., Pérez, Y., González-Piqueras, J., Jiménez, V., Rodríguez, A., Galve, J. M., and Calera, A.: Assessment of remote sensing-based soil water balance and FAO56 dual crop coefficient approach on almond orchards, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14257, https://doi.org/10.5194/egusphere-egu23-14257, 2023.