Real-time Irrigation Scheduling Research Articles

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Validation of farmers’ on-farm irrigation scheduling for optimal water utilization in Tigray Region, Ethiopia

Unwise use of irrigation water in water-scarce areas exacerbates water scarcity, diminishes crop yield, and leads to resource wastage. In the Tigray region of Ethiopia, where water is scarce and a determinant resource, no regional-level validation of on-farm irrigation scheduling had been conducted until now. The objectives of this study were to (i) validate farmers’ irrigation scheduling practices and (ii) optimize water consumption for increased irrigated area and number of farmers. Eight different irrigation schemes were purposively selected from the Tigray region. Primary data, acquired through field measurements, observations, and discussions, were supplemented with secondary data. Farmers' irrigation scheduling practices were predominantly of the "Fixed" type, characterized by constant irrigation intervals and amounts. Unfortunately, these practices were non-optimal, with 55% over-irrigation and 45% under-irrigation. Over-irrigation instances ranged from 1350 m3in a garlic plot within the Mesima scheme to a maximum of 1,327,067 m3in a maize plot within the Serenta scheme. The excess water could potentially be utilized to irrigate an additional area of 2 to 148 hectares, thereby benefiting 7 to 296 more farmers, respectively. Conversely, instances of under-irrigation in the Fre Lekatit scheme resulted in yield reductions of 10,445 kg for potatoes and 138,499 kg for maize. In the Tigray region, most of the schemes are semi-arid, emphasizing the imperative need for water conservation. Renovating regional-level irrigation scheduling by integrating performance assessment and enhancing water productivity at the field level, and establishing a remote-sensing-based 'Real-Time Irrigation Scheduling System' is deemed necessary to sustain dryland irrigated agriculture.

Smart Irrigation Systems: Overview

Countries are collaborating to make agriculture more efficient by combining new technologies to improve its procedure. Improving irrigation efficiency in agriculture is thus critical for the survival of sustainable agricultural production. Smart irrigation methods can enhance irrigation efficiency, specially with the introduction of wireless communication systems, monitoring devices, and enhanced control techniques for efficient irrigation scheduling. The study compared on a wide range of study subjects to investigate scientific approaches for smart irrigation. As a result, this project included a wide range of topics related to irrigation methods, decision-making, and technology used. Information was gathered from a variety of scientific papers. So, our research relied on several published documents, the majority of which were published during the last four years, and authors from all over the world. In the meantime, various irrigation initiatives were given special attention. Following that, the evaluation focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting.

Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation

Efficient irrigation scheduling is crucial for both improving crop production and saving irrigation water use in arid/semi-arid agricultural regions threatened by water shortage and soil salinity. However, irrigation scheduling optimization is hindered by the uncertainties of data and optimization model, and adopting the optimal irrigation scheduling is subject to farmers' acceptance. To effectively tackle these challenges, this paper presents a novel human-machine interactive framework for real-time irrigation scheduling (RIS). The developed modeling framework couples a simulation-optimization model, irrigation farmers, and a data assimilation procedure within the human-machine interactive framework for RIS. The proposed approach is capable of: 1) searching optimal irrigation scheduling through the simulation-optimization model; 2) making actual irrigation decisions based on farmers' experiences, knowledge, behaviors, or optimal solutions; and 3) updating soil water content based on the model simulations and real-time observations at each time period. The RIS is applied to a real-world case in a typical arid agricultural region of China. Based on the comparisons with historical irrigation records and a tradition simulation-optimization model, the proposed RIS can not only achieve higher economic benefit with less irrigation water allocation quotas, but also improve irrigation efficiency. This study contributes to the methodology by integrating computer model, real-time observations and farmers' experiences to optimization modeling framework for supporting sustainable irrigation water management.

Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as Irrigation Principles. Soil and Water Conservation Engineera technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5 °C was found to be more appropriate than the literature value of 1.0 °C, but consideration of crop growth stage would further improve scheduling.

Evaluation of Five Equations for Short-Term Reference Evapotranspiration Forecasting Using Public Temperature Forecasts for North China Plain

Accurate short-term forecasts of daily reference evapotranspiration (ET0) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET0. However, easily accessible temperature forecasts are relatively less reported in short-term ET0 forecasting. Furthermore, the accuracy of ET0 forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET0 for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET0 models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET0. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET0 in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET0 forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET0 forecasting in NCP.

Development of a new UAV-thermal imaging based model for estimating pecan evapotranspiration

• A new modified model was proposed for calculating pecan water consumption using canopy temperature. • The proposed modified model was calibrated and validated using field measured data. • A good agreement was observed between estimated pecan water consumption and applied water based on ET a . • Utility of proposed model for estimating monthly pecan water use was confirmed. • There is potential for application of the proposed model in other locations with various crops. Pecans are a specialty crop in New Mexico’s Lower Rio Grande Valley (LRGV), a region that produces around 30% of pecans in USA. Pecans are also a major water consumer, requiring 1200–1300 mm depth for maximum yield in this region. The combination of prolonged drought and increasing competition for water among various water consumers has created an urgency for the efficient use of scarce water resources in the LRGV. More efficient water management through the real-time irrigation scheduling is one method to promote reduced water application in agriculture. This study was conducted to calibrate and validate a new modified model for estimating the pecan actual evapotranspiration (ET a ) based on canopy temperature using thermal images taken from an Unmanned Aerial Vehicle (UAV) during three growing seasons in a drip irrigated pecan orchard. A capacity to estimate the relation between ET a and canopy temperature provides an important information to guide water management choices. The Simplified Surface Energy Balance (SSEBop) model was modified and used for calibration and validation. Applied irrigation water based on ET a was used to calibrate and validate the proposed modified model. The scaling factor of K in the SSEBop model was calculated as 0.75 through the calibration process. Findings showed a good agreement between estimated pecan ET a using modified SSEBop model and applied water based on ET a during calibration (R 2 = 0.72, RMSE = 0.6 mm/d, MAE = 0.48 mm/d) and validation period (R 2 = 0.90, RMSE = 0.24 mm/d, MAE = 0.22 mm/d). Also, findings confirmed the utility of modified model for estimating monthly pecan ET a (RMSE = 8.87 mm/month, MAE = 6.55 mm/month). The proposed modified model provides pecan farmers with a simple real-time irrigation scheduling tool where they can better practice precision irrigation. Although the modified model was calibrated and validated for irrigation scheduling in the LRGV, it has potential to see application for other locations with different crops using similar calibration approach.

Real-Time Irrigation Scheduling of Maize Using Degrees Above Non-Stressed (Dans) Index in Semi-Arid Environment

Real-Time Irrigation Scheduling of Maize Using Degrees Above Non-Stressed (Dans) Index in Semi-Arid Environment

Real-time irrigation scheduling for upland crop based on soil and climate characteristics of tidal lowland area in South Sumatera

The research aims to determine the water requirement of plants based on soil and climate characteristics in the lowland area of South Sumatra. The study was conducted at the Soil Physics Laboratory, Soil Department, Faculty of Agriculture, Sriwijaya University. Soil samples were taken from lowland areas at Tidal Lowland Reclamation in Telang I Banyuasin. Field research was conducted from March to November 2019. Climatic data were differentiated into rainfall conditions below 2,500 mm/year and more than 2,500 mm/year. The BUDGET model was used for simulations to determine real-time irrigation scheduling. The indicator crop is tomatoes, with a scenario arranged at the dry season planting period. The results showed that in the condition of annual rainfall < 2,500 mm, farmers need more irrigation frequencies compared to rainfall > 2,500 mm. Irrigation is not required under normal climatic conditions. Farmers only need to maintain the groundwater table under 40 cm using gate operation in the tertiary canal. Rainfall was sufficient to fulfill crop evapotranspiration. However, the irrigation scheduling was done every week during the initial growing stage and every ten days in the vegetative up to generative stages in the dry climatic condition.

Neural network soil moisture model for irrigation scheduling

Real-time irrigation scheduling systems attempt to eliminate crop water stress and achieve a high yield at harvest through the control of soil moisture. Artificial intelligence algorithms are possibly to learn the soil moisture dynamics in the soil-plant-atmosphere system and then are embedded into a low-cost controller to generate appropriate irrigation schedules. In this study, a neural network (NN) model was proposed to learn from a process-based agricultural systems model, the Root Zone Water Quality Model (RZWQM2) in predicting the root zone soil moisture during the crop growing season. Climatic data, rooting depth and hesternal soil moisture are set as inputs of the NN model to predict intraday soil moisture in different layers. Conditions with and without water supply are modeled separately to achieve a higher accuracy. The NN-based irrigation scheduling method (NN method), triggers irrigation when the predicted soil moisture drops to a level defined by the product of management-allowed depletion multiplied by the depth of available water to plant. Irrigation quantity was set to replenish the root zone soil water content to field capacity. NN method was compared with the reported water stress (WS) method based on RZWQM2. The results reveal that though the constructed NN model well-predicted soil moisture changes during the main crop season with minor errors, but the error was larger at lower soil moisture thereby decreased the scheduling efficiency. An NN ensemble model was tested and shown to improve the precision and robustness of soil moisture prediction as well as the scheduling performance in view of water conservation and yield maintenance. Combined with the NN ensemble model and adjusted lowest soil moisture for triggering irrigations, the NN ensemble-based irrigation scheduling method achieved a performance not better than that of the RZWQM2-WS method but exceeded the method based on evapotranspiration and water balance by up to 20%. The constructed NN ensemble model and NN ensemble-based irrigation scheduling method could act as an alternative to predicting soil moisture and obtaining efficient irrigation scheduling.

REUTIVAR: Model for Precision Fertigation Scheduling for Olive Orchards Using Reclaimed Water

Olive orchard is the most representative and iconic crop in Andalusia (Southern Spain). It is also considered one of the major economic activities of this region. However, due to its extensive growing area, olive orchard is also the most water-demanding crop in the Guadalquivir River Basin. In addition, its fertilization is commonly imprecise, which causes over-fertilization, especially nitrogen. This leads to pollution problems in both soil and water, threating the environment and the system sustainability. This concern is further exacerbated by the use of reclaimed water to irrigate since water is already a nutrient carrier. In this work, a model which determines the real-time irrigation and fertilization scheduling for olive orchard, applying treated wastewater, has been developed. The precision fertigation model considers weather information, both historical and forecast data, soil characteristics, hydraulic characteristics of the system, water allocation, tree nutrient status, and irrigation water quality. As a result, daily information about irrigation time and fertilizer quantity, considering the most susceptible crop stage, is provided. The proposed model showed that by using treated wastewater, additional fertilization was not required, leading to significant environmental benefits but also benefits in the total farm financial costs.

Assessment of a Smartphone Application for Real-Time Irrigation Scheduling in Mediterranean Environments

The suitability of cloud-based irrigation technologies remains questionable due to limited information on their evaluation in the field. This study focussed on the on-field assessment of a smartphone irrigation scheduling tool—Bluleaf®—with respect to traditional water application practices. Bluleaf® uses weather, crop, soil, and irrigation system data to support a farmer’s decision on the timing and amounts of irrigation. The smartphone application was tested in Bekaa Valley, Lebanon, on durum wheat, a strategic Mediterranean crop, during the 2017 and 2018 growing seasons. The simulation results on soil water balance were in “acceptable to very good” agreement with the measured soil moisture values, with a root mean square error (RMSE) between 15.1 and 26.6 mm and a modelling efficiency (NSE) that ranged from 0.77 to 0.92. The appropriateness of the adopted smartphone irrigation scheduling was confirmed also by leaf water potential measurements and the Crop Water Stress Index (CWSI). A water saving of more than 1000 m3/ha (25.7%) was observed with Bluleaf® with respect to traditional irrigation scheduling. Therefore, new technologies could bring about substantial benefits to farmers and support water saving efforts in the Mediterranean region.

Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain

Irrigated agriculture is very important for securing food production for an increasing population over the next decades. Given scarcity of water resources, optimal irrigation management is needed to reduce water while realizing maximal crop productivity. The new method of integrating soil water content measurements and the Community Land Model (CLM) using sequential data assimilation (DA) is promising to improve the prediction of soil water status and efficiently design irrigation strategies. Soil water content measured by FDR (Frequency Domain Reflectometry) was assimilated into CLM by LETKF (Local Ensemble Transform Kalman Filter) to improve model predictions. Atmospheric input data from GFS (Global Forecast System) were used to force CLM in order to predict short-term soil water contents. The irrigation amount was then calculated on the basis of the difference between predicted and targeted soil water content over the root zone.During the real-time irrigation campaigns in Picassent (Spain) in 2015 and 2016, there were 6 fields irrigated according the data assimilation-optimization approach (CLM-DA), 2 further fields according the FAO (Food and Agriculture Organization) water balance method and also 2 fields traditionally according the farmers preference. The required amount of irrigation water for each citrus field was applied by SCADA (supervisory control and data acquisition system). Compared with the traditionally irrigated fields by farmers, 24% less irrigation water was needed for the CLM-DA scheduled fields averaged over both years from July to September, while the FAO fields were irrigated with 22% less water. Stem water potential data and soil moisture recordings of the CLM-DA scheduled fields did not indicate significant water stress during the irrigation period. The CLM-DA scheduled fields received less irrigation water than traditionally irrigated fields, but the orange production was not significantly suppressed.Overall, our results show that the CLM-DA method is attractive given its water saving potential and automated approach, ease of incorporation of on-line measurements and ensemble based predictions of soil moisture evolution.

An Online Tool for Estimating Evapotranspiration and Irrigation Requirements of Crops in South Carolina

In recent years, there has been an increased interest in South Carolina regarding the amount of water used by different consumers, especially agricultural producers. This interest has sparked conversations among different stakeholders, including the media, policy makers, producers, scientists, and the general public, regarding the current state and future of water resources in the state. Central to these discussions, from the agricultural sector perspective, is the question of how much water producers really need to grow crops. The objective of this study was, therefore, to develop an online tool to use local South Carolina historic weather data to estimate daily and seasonal crop evapotranspiration and irrigation requirements for different crops. The overall goal was for the new tool to assist farmers and other stakeholders to better plan irrigation water allocations and management. Therefore, an interactive online tool called ETcCalc was created to address this objective. ETcCalc, which is freely available online (http://sccropwater.com), was developed using historic weather data; therefore, it is suitable as an irrigation planning tool rather than a real-time irrigation scheduling tool.

Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

Abstract. Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrf alone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management. Keywords: Center-pivot irrigation, ET estimation methods, Evapotranspiration, Irrigation scheduling, Irrigation water balance, Model validation, Variable-rate irrigation.

Development of decision support system based on crop water demand and soil moisture deficit for real time irrigation scheduling

An automated irrigation system has a potential solution to control the site specific irrigation, is of benefits to producers can save water and improving water productivity. The study was conducted to develop a decision support system (DSS) and integrated with soil moisture sensor based on tensiometric principle, for real time irrigation scheduling either on time basis or soil moisture sensor basis. The designed system was successfully tested on potato crop under different methods of irrigation at Precision Farming Development Centre (PFDC), IARI, New Delhi, India. The reference evapotranspiration, water requirement of potato crop estimated from DSS was verified and tested with CROPWAT model, and approximately similar results were obtained. The crop evapotranspiration of potato crop was estimated 25.3 cm using DSS. The coefficient of determination (R2) range was found 0.93-0.94, 0.82-0.91 between water level depletion in modified tensiometer and soil water suction measured by tensiometer, watermark at 20 cm, 30 cm soil depth respectively. The performance evaluation of developed system helped to control water application as per crop needs with its various functionality were found satisfactorily.

Development and integration of soil moisture sensor with drip system for precise irrigation scheduling through mobile phone

Soil moisture sensor is an instrument for quick measurements of soil moisture content in the crop root zone on real time basis. The main objective of this research was development and evaluation of an indigenous sensor for precise irrigation scheduling. The various parts of sensor developed were ceramic cup, acrylic pipe, level sensor, tee, reducer, gland, cork, and end cap. The designed system was successfully tested on okra crop and calibrated with frequency domain reflectometry (FDR) by three methods of irrigation, i.e. check basin, furrow and drip, respectively. The average depth of water depletion in modified tensiometer by these methods was 27 to 35 cm at 50% management allowable depletion (MAD) of field capacity. This depth was useful for the level sensor to be installed inside modified tensiometer for real time irrigation scheduling. The correlation coefficient (R2) between soil moisture content obtained from the developed sensor and FDR was 0.963. Sensor network was integrated with global system for mobile communication (GSM), short message service (SMS) and drip head work to develop an automated irrigation system. This would enable farmers to effectively monitor and control water application in the field by sending command through SMS and receiving pumping status through the mobile phone.

Performance evaluation of gypsum block wireless sensor network system for real time irrigation scheduling

Majority of farmers in India are irrigating their crops without using any irrigation scheduling criteria. Consequently, the application of excess irrigation water causes water logging, wastage of p...

Sensor network based irrigation system for real time irrigation scheduling in vegetable crops under different methods of irrigation

Soil moisture sensor based irrigation scheduling is relatively a new concept in India. A soil moisture sensor based wireless irrigation system was designed, developed and tested during the years 2013-15 in vegetable crops, i. e. okra, tomato, brinjal, broccoli, potato, and knol-khol on the research farm of the Precision Farming Development Centre, Water Technology Centre, Indian Agricultural Research Institute, New Delhi, India. A network of sensors was integrated with a wireless system having modified tensiometer, level sensor, controller, GSM receiver, transmitter, solenoid valve, water meter, and pump for automated irrigation scheduling. The sensor network, global system for mobile communication (GSM) and short message service (SMS) carried the data from the sensors to the user through a mobile set. This system allows the user to effectively monitor and control water application in the field via sensors and/or through a mobile phone set by sending a command in the form of message and obtaining the moisture status in the field.

Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity

Summary Neutron intensity measured by the aboveground cosmic-ray neutron intensity probe (CRP) allows estimating soil moisture content at the field scale. In this work, synthetic neutron intensities were used to remove the bias of simulated soil moisture content or update soil hydraulic properties (together with soil moisture) in the Community Land Model (CLM) using the Local Ensemble Transform Kalman Filter. The cosmic-ray forward model COSMIC was used as the non-linear measurement operator which maps between neutron intensity and soil moisture. The novel aspect of this work is that synthetically measured neutron intensity was used for real time updating of soil states and soil properties (or soil moisture bias) and posterior use for the real time scheduling of irrigation (data assimilation based real-time control approach). Uncertainty of model forcing and soil properties (sand fraction, clay fraction and organic matter density) were considered in the ensemble predictions of the soil moisture profiles. Horizontal and vertical weighting of soil moisture was introduced in the data assimilation in order to handle the scale mismatch between the cosmic-ray footprint and the CLM grid cell. The approach was illustrated in a synthetic study with the real-time irrigation scheduling of fields of citrus trees. After adjusting soil moisture content by assimilating neutron intensity, the irrigation requirements were calculated based on the water deficit method. Model bias was introduced by using coarser soil texture in the data assimilation experiments than in reality. A series of experiments was done with different combinations of state, parameter and bias estimation in combination with irrigation scheduling. Assimilation of CRP neutron intensity improved soil moisture characterization. Irrigation requirement was overestimated if biased soil properties were used. The soil moisture bias was reduced by 35% after data assimilation. The scenario of joint state-parameter estimation resulted in the best soil moisture characterization (50% decrease in root mean square error compared to open loop simulations), and the best estimate of needed irrigation amount (86% decrease in Hausdorff distance compared to open loop). The coarse scale synthetic CRP observation was proven to be useful for the fine scale soil moisture and soil properties estimation for the objective of irrigation scheduling.