LCIS DSS—An Irrigation Supporting System for Efficient Water Use in Precision Agriculture

The sustainable management of water resources is one of the most important topics to face future climate change and food security. Many countries facing a serious water crisis, due to both natural and artificial causes. The efficient use of water in agriculture is one of the most significant agricultural challenges that modern technologies. These last are considered powerful management instruments able to help farmers achieve the best efficiency in irrigation water use and to increase their incomes by obtaining the highest possible crop yield. In this context, within the project “An advanced low cost system for farm irrigation support—LCIS” (a joint Italian Israeli R&D project), a fully transferable Decision Support Systems (DSS) for irrigation support, based on three different methodologies representative of the state of the art in irrigation management tools (W-Tens, in situ soil sensor; IRRISAT®, remote sensing; W-Mod, simulation modelling of water balance in the soil-plant and atmosphere system), has been developed. These three LCIS-DSS tools have been evaluated, in terms of their ability to support the farmer in irrigation management, in a real applicative case study in Italy and Israel. The main challenge of a new DSS for irrigation is attributed to the uncertain factors during the growing season such as weather uncertainty, and crop monitoring platform. For encounter this challenge, we developed during two years the LCIS, a web-based real-time DSS for irrigation scheduling using low-cost imaging spectroscopy for state estimation of the agriculture system and probabilistic short- and medium-term climate forecasts. While the majority of the existing DSS models are incorporated directly into the optimization framework, we propose to integrate continuous feedback from the field (e.g., soil moisture, crop water-stress, plant stage, LAI, and biomass) estimated based on remote sensing information. These field data will be collected by the point-based spectrometer and hyperspectral imaging system. Then a low-cost camera will be designed for specific spectral/spatial parameters (bound to the required feedbacks). The main objectives were: developing real-time Decision Support System (DSS) for optimal irrigation scheduling at farm scale for crop yield improvement, reducing irrigation cost, and water saving; developing a low-cost imaging spectroscopy framework to support the irrigation scheduling DSS above and facilitates its use in countries/places where expensive imaging spectroscopy is not available; examining the developed framework in real-life application, the framework will be calibrated evaluated using high resolution devices and tested using a low-cost system in Israel and Italy farms.

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Using a Remote Sensing data based toolkit to monitor vine water use and water status for real time irrigation scheduling in California vineyards

10.5194/egusphere-egu2020-22248 ◽

2020 ◽

Author(s):

Maria Mar Alsina ◽

Kyle Knipper ◽

Martha Anderson ◽

WIlliam Kustas ◽

Nicolas Bambach ◽

...

Keyword(s):

Remote Sensing ◽

Real Time ◽

Water Use ◽

Water Status ◽

Irrigation Management ◽

Remote Sensing Data ◽

Irrigation Scheduling ◽

Area Index ◽

Pilot Experiment ◽

Sensing Data

Grapevines are one of the major drivers of agriculture in California, representing a production equivalent to $6.25 billion in 2018. Water is scarce, and increasingly intense and prolonged drought periods, like one that recently occurred in the 2012-2016 period, may happen with greater frequency. Consequently, there is a need to develop irrigation management decision tools to help growers maximize water use while maintaining productivity. Furthermore, grapevines are deficit irrigated, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete data that do not represent the often-high spatial variability of vineyards. &#160;Remote sensing techniques may represent a fast real-time decision-making tool for irrigation management, able to extensively cover multiple vineyards with low human or economic investments.&#160; While growers currently calculate the vine water demands using the reference evapotranspiration from a weather station located in the region and a crop coefficient, usually from literature, they don't have any means to measure or estimate the actual water used by the vines. Knowing the actual evapotranspiration (ET) in real-time and at a sub-field scale would provide essential information to monitor vine water status and adjust the irrigation amounts to the real water needs. The aim of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project, has been to provide growers with an irrigation toolkit that integrates the spatial distribution of vine water use and water status. The project focuses on grapevines, but it will be easily extrapolated to orchards and other crop types. We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX project into a practical tool that growers can use for irrigation management. We run this pilot experiment over 6 commercial grapevine blocks, located in Cloverdale, Sonoma, CA. During the 2019 growing season, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation and Normalized Vegetation Water Indices as well as local weather data, forecasted ET and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. We compared the irrigation prescription based on the provided data with the grower's practices. The total season irrigation ranged between 70 and 120 mm depending on the block, and our irrigation recommendations deviated between 10 and 30 mm from the growers' practices, also depending on the block. This analyzes the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks.

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Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

10.32469/10355/79549 ◽

2019 ◽

Author(s):

◽

Anh Thi Tuan Nguyen

Keyword(s):

Remote Sensing ◽

Real Time ◽

Regression Models ◽

Field Data ◽

Irrigation Management ◽

Irrigation Scheduling ◽

Crop Growth ◽

Variable Rate ◽

Water Application ◽

Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

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

Transactions of the ASABE ◽

10.13031/trans.12311 ◽

2018 ◽

Vol 61 (2) ◽

pp. 533-548 ◽

Cited By ~ 7

Author(s):

J. Burdette Barker ◽

Christopher M. U. Neale ◽

Derek M. Heeren ◽

Andrew E. Suyker

Keyword(s):

Remote Sensing ◽

Energy Balance ◽

Water Balance ◽

Real Time ◽

Eddy Covariance ◽

Irrigation Management ◽

Crop Coefficient ◽

Irrigation Scheduling ◽

Balance Model ◽

Crop Coefficients

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.

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SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽

10.15809/irriga.2021v1n3p585-598 ◽

2021 ◽

Vol 1 (3) ◽

pp. 585-598

Author(s):

Pedro Henrique Jandreice Magnoni ◽

Cesar De Oliveira Ferreira Silva ◽

Rodrigo Lilla Manzione

Keyword(s):

Remote Sensing ◽

Water Budget ◽

Irrigation Management ◽

Irrigation Scheduling ◽

In Situ Data ◽

Joint Application ◽

E Mail ◽

Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP* PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil, [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores. 1 RESUMO Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados. Palavras-chave: evapotranspiração, momento de irrigar, agriwater. MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP 2 ABSTRACT Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data. Keywords: evapotranspiration, irrigation scheduling, agriwater.

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Optimization of irrigation water in South Africa for sustainable and beneficial use

10.51415/10321/2467 ◽

2017 ◽

Author(s):

◽

Akinola Mayowa Ikudayisi

Keyword(s):

South Africa ◽

Water Use ◽

Crop Yield ◽

Optimization Algorithm ◽

Irrigation Water ◽

Irrigation Scheduling ◽

Crop Water ◽

Land Area ◽

Water Requirements ◽

Irrigation Water Use

Water is an essential natural resource for human existence and survival on the earth. South Africa, a water stressed country, allocates a high percentage of its available consumptive water use to irrigation. Therefore, it is necessary that we optimize water use in order to enhance food security. This study presents the development of mathematical models for irrigation scheduling of crops, optimal irrigation water release and crop yields in Vaal Harts irrigation scheme (VIS) of South Africa. For efficient irrigation water management, an accurate estimation of reference evapotranspiration (ETₒ) should be carried out. However, due to non-availability of enough historical data for the study area, mathematical models were developed to estimate ETₒ. A 20-year monthly meteorological data was collected and analysed using two data–driven modeling techniques namely principal component analysis (PCA) and adaptive neuro-fuzzy inference systems (ANFIS). Furthermore, an artificial neural network (ANN) model was developed for real time prediction of future ETₒ for the study area. The real time irrigation scheduling of potatoes was developed using a crop growth simulation model called CROPWAT. It was used to determine the crop water productivity (CWP), which is a determinant of the relationship between water applied and crop yield. Finally, a new and novel evolutionary multi-objective optimization algorithm called combined Pareto multi-objective differential evolution (CPMDE) was applied to optimize irrigation water use and crop yield on the VIS farmland. The net irrigation benefit, land area and irrigation water use of maize, potatoes and groundnut were optimized. Results obtained show that ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity have less significance on the value of ETₒ. Also, ANN models with one hidden layer showed better predictive performance compared with other considered configurations. A 5-day time step irrigation schedule data and graphs showing the crop water requirements and irrigation water requirements was generated. This would enable farmers know when, where, and how much water to apply to a given farmland. Finally, the employed CPMDE optimization algorithm produced a set of non-dominated Pareto optimal solutions. The best solution suggests that maize, groundnut and potatoes should be planted on 403543.44 m2, 181542.00 m2 and 352876.05 m2areas of land respectively. This solution generates a total net benefit of ZAR 767,961.49, total planting area of 937961.49 m2 and irrigation water volume of 391,061.52 m3. Among the three crops optimized, maize has the greatest land area, followed by potatoes and groundnut. This shows that maize is more profitable than potatoes and groundnut with respect to crop yield and water use in the study area.

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Remote Sensing Applications for Planning Irrigation Management. The Use of SEBAL Methodology for Estimating Crop Evapotranspiration in Cyprus

Scientific Journal of Riga Technical University Environmental and Climate Technologies ◽

10.2478/v10145-012-0012-z ◽

2012 ◽

Vol 9 (1) ◽

pp. 17-21

Author(s):

George Papadavid ◽

Skevi Perdikou ◽

Michalakis Hadjimitsis ◽

Diofantos Hadjimitsis

Keyword(s):

Remote Sensing ◽

Water Allocation ◽

Surface Energy Balance ◽

Irrigation Management ◽

Irrigation Scheduling ◽

Severe Drought ◽

Remotely Sensed Data ◽

Sensing Applications ◽

Remote Sensing Applications ◽

Remote Sensing Techniques

Abstract Water allocation to crops has always been of great importance in the agricultural process. In this context, and under the current conditions, where Cyprus is facing a severe drought the last five years, the purpose of this study is basically to estimate the needed crop water requirements for supporting irrigation management and monitoring irrigation on a systematic basis for Cyprus using remote sensing techniques. The use of satellite images supported by ground measurements has provided quite accurate results. Intended purpose of this paper is to estimate the Evapotranspiration (ET) of specific crops which is the basis for irrigation scheduling and establish a procedure for monitoring and managing irrigation water over Cyprus, using remotely sensed data from Landsat TM/ ETM+ and a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL).

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Middleware to Operate Smart Photovoltaic Irrigation Systems in Real Time

Water ◽

10.3390/w11071508 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1508 ◽

Cited By ~ 2

Author(s):

Rafael González Perea ◽

Aida Mérida García ◽

Irene Fernández García ◽

Emilio Camacho Poyato ◽

Pilar Montesinos ◽

...

Keyword(s):

Renewable Energy ◽

Real Time ◽

Precision Agriculture ◽

Renewable Energy Sources ◽

Irrigation Management ◽

Irrigation Scheduling ◽

Real Field ◽

Energy Sources ◽

Irrigated Agriculture ◽

Irrigation Season

Climate change, water scarcity and higher energy requirements and electric tariff compromises the continuity of the irrigated agriculture. Precision agriculture (PA) or renewable energy sources which are based on communication and information technologies and a large amount of data are key to ensuring this economic activity and guaranteeing food security at the global level. Several works which are based on the use of PA and renewable energy sources have been developed in order to optimize different variables of irrigated agriculture such as irrigation scheduling. However, the large amount of technologies and sensors that these models need to be implemented are still far from being easily accessible and usable by farmers. In this way, a middleware called Real time Smart Solar Irrigation Manager (RESSIM) has been developed in this work and implemented in MATLABTM with the aim to provide to farmers a user-friendly tool for the daily making decision process of irrigation scheduling using a smart photovoltaic irrigation management module. RESSIM middleware was successfully tested in a real field during a full irrigation season of olive trees using a real smart photovoltaic irrigation system.

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Usage of soil moisture models in agronomic research

Canadian Journal of Soil Science ◽

10.4141/cjss96-035 ◽

1996 ◽

Vol 76 (3) ◽

pp. 285-295 ◽

Cited By ~ 5

Author(s):

O. O. Akinremi ◽

S. M. McGinn

Keyword(s):

Soil Moisture ◽

Grain Yield ◽

Water Use ◽

Crop Yield ◽

Drought Index ◽

Regional Scale ◽

Irrigation Scheduling ◽

Water Model ◽

Yield Reduction ◽

Efficiency Coefficient

Soil moisture controls many important processes in the soil-plant system and the extent of these processes cannot be quantified without knowing moisture status of the root zone. Of agronomic importance these include, seedling emergence, evapotranspiration, mineralization of the soil organic fraction, surface runoff, leaching and crop yield. Many models have been developed to simulate these processes based on algorithms of varying degrees of complexity that describe the dynamic nature of soil moisture at different temporal and spatial scales. This paper reviews the direct applications of soil moisture models in agronomy from the field to regional scale and for daily to seasonal time steps. At every level of detail, the lack of model validation beyond the region where it was developed is the main limitation to the application of soil moisture models in agronomy. At the field scale, models have been used for irrigation scheduling to ensure efficient utilization of irrigation water and maximize crop yields. Models are also used to estimate crop yield based on the growing season water use. The water use of crops is converted to biomass accumulation and grain yield using a water-use efficiency coefficient and a harvest index. Other empirical equations are available that relate cumulative crop water use directly to grain yield. On a regional scale, in a study of drought climatology on the Canadian prairie, we coupled a soil water model, the Versatile Soil Moisture Budget, with the Palmer Drought Index model to improve the modelling of soil moisture. This was found to improve the relationship of the Palmer drought index to wheat yield reduction resulting from drought. Key words: Soil moisture, modelling, water-use, evapotranspiration, aridity index, Canadian prairies

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Combining the FAO56 agrohydrological model and remote sensing data to assess water demand in a Sicilian irrigation district

10.5194/egusphere-egu2020-319 ◽

2020 ◽

Author(s):

Matteo Ippolito ◽

Mario Minacapilli ◽

Giuseppe Provenzano

Keyword(s):

Remote Sensing ◽

Water Balance ◽

Water Use ◽

Irrigation Water ◽

Remote Sensing Data ◽

Irrigation Scheduling ◽

Irrigation District ◽

Sensing Data ◽

Spatiotemporal Distributions ◽

Crop Parameters

Agricultural water use in irrigated areas plays a key role in the Mediterranean regions characterized by semi-arid climate and water shortage. In the face of optimizing irrigation water use, farmers must revise their irrigation practices to increase the drought resilience of agricultural systems and to avoid severe damages in agro-ecosystems. In this direction, during the last decades, the research has been focused on mathematical models to simulate the process of driving mass transport and energy exchanges in the Soil-Plant-Atmosphere system.The objective of the paper was to test the suitability of the combination of FAO56 agro-hydrological model with remote sensing data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform, to assess the spatiotemporal distributions of crop water requirement and to schedule irrigation in an irrigation district of the south-west of Sicily, Italy.The proposed approach allowed obtaining the spatiotemporal distributions of soil and crop parameters used in the FAO56 model implemented in a GIS environment to simulate the water balance, as well as to assess the actual irrigation strategy. The GIS database was organized to include soil and crop parameters, as well as the irrigation volumes actually delivered to each farmer; the latter data can be used not only as input for water balance to evaluate the efficiency of the actual irrigation strategies but also to identify different irrigation scheduling scenario obtained by the FAO56 procedure.The first application was carried out for the period 2014-2017, to identify a combination of irrigation scheduling parameters to be implemented in the model aimed at reproducing the ordinary strategy adopted by the farmers, based on the spatiotemporal variability of soil and climate forcings. When the model outputs were aggregated for single crop types, a fairly good agreement was found between simulated and actual seasonal irrigation volumes delivered either at the level of district and secondary units. Alternative scenarios of irrigation water distribution were then identified and analyzed, to provide irrigation technicians and policymakers a decision support tool to improve the efficiency of irrigation systems and to optimize the distribution based on the availability of water resources.

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