research topics in gis and remote sensing

• Remote Sensing Project Topics

As the name suggests, “remote sensing” refers to capturing and monitoring information from the longest distance in wireless mode. For instance: sensing the green vegetation on the earth’s surface from a satellite . For that, it uses electromagnetic waves obtained from sensed objects in the form of diffraction, emission, and reflection . This article is envisioned to provide key Remote Sensing Project Topics along with its research areas, trends, issues, and techniques!!!

 From this, we can identify the need and importance of enhancing natural resource management and a green environment .  Further, the remote sensing systems or platforms provide below data,

• It is simple and quick to collect data
• It gives unreachable area information
• It enables to compare surveyors’ team which is an inexpensive process
• It quickly generates maps for interpretation
• It gives extremely huge-scale area information
• It acquires area images in constant time duration where the environmental changes can be analysed

            In the remote sensing research field, one should know some key terminologies that are widely used among researcher’s interested people . These terms are not only just the terminologies to know but also the principal operations involved in remote sensing . The first term represents the overall main operations involved in remote sensing and the last three terms represent the workflow of remote sensing project topics.

Our professional team of experts has sufficient skill in developing remote sensing projects based on your handhold client’s requirements . Further, we also assist you to provide you development plan to realize your project workflow .

What are the primary terms in remote sensing?

• Addresses entire operations involved in remote sensing space systems
• For instance: data acquisition, analysis, examination, interpretation, and distribution
• Collect data from remote sensors in the space objects
• Transfer collected raw data to earth through telemetry in different forms
• For instance – magnetic tape, electromagnetic signals, photographic film, etc.
• Get the above collected primary data as input
• Process collected data to extract useful information by eliminating unnecessary data
• Get above processed data as input
• Perform required techniques to interpret the processed data and other sources in detail

Now, we can see some significant research challenges involved in remote sensing. Intending to provide innovative research topics based on current scholars’ demands, we work deeply in analysing the recent research journal papers and magazines. So, we can update the recent technological development frequently.

This makes us find the up-to-date research issues that scholars are expecting to solve through advanced methodologies . Moreover, we also provide you with some interesting research ideas to crack the research handpicked research issues/challenges to craft remote sensing project topics . To know the appropriate solutions for the below-specified research issues in remote sensing , approach our team.

What are the important issues in remote sensing?

• Some sensors are lacking in measuring the events in uncertain situations
• Although the inferential techniques are utilized, the sensed data may be inaccurate
• For instance; underwater research
• Although there are several advanced remote sensing tools, lack of data distribution availability may cause a severe impact in the remote sensing field
• Even the data is commercially available, it is very expensive for economically weak researchers
• Specifically, developing countries are largely get affected by this issue
• Since it stops the remote sensing data accessibility for processing and assessment

For illustration, here we have taken the sensor as an example from the above list. Generally, there are numerous sensors are available for the remote sensing process . And, these sensors are differentiated by their characteristics, merits, and demerits. Depending on this information, one should select the appropriate sensor for their project.

Suppose if the sensor attempts to capture images then it should need to consider certain factors such as area coverage, image resolution, flight restriction , etc. For instance: the satellite has a global view, the drone has small and the aircraft has a limited view . Here, we have given you a few most widely used sensor technologies for remote sensing.

What are the 3 remote sensing tools in geography?

• Sound Navigation Ranging (Sonar)
• Sensors, Radiometers and Spectrometers
• Light Detection and Ranging (LiDAR)

From the above, Sonar and LiDAR are used to construct the topographic models. Although these techniques are used for a common purpose, it has one main difference i.e., a supportive environment. In specific, Sonar can be used underwater and LiDAR can be used underground. In addition to topographic models, we can also construct assessment models. Examples of topographic models are delineating watersheds, archaeological sites, and flood risk prediction. Overall, we support you with more number research areas of remote sensing through the above-specified tools. For your reference, here we have given you some important research areas that provide uncountable Remote Sensing Project for research / final year project .

Innovative Remote Sensing Project Topics

• Image Classification
• LiDAR-based Remote Sensing
• Various Imagining Techniques
• Green House Gases Prediction
• PAN and MS Image Merging
• Green Vegetation Land Estimation
• UAV-assisted Sensing in Emergency Situations

In addition, we have also given you some latest research directions on remote sensing . Beyond this list of trends, we also support you in other emerging research perspectives. Further, we also support your interest to develop your idea. Our experts help you to refine your desired idea based on the current research demands and technologies . And, we have framed several Remote Sensing Projects to support following trends and other important trends of remote sensing project topics . We assure you that we provide step-by-step research assistance to support you from the beginning onwards . So, approach us to avail friendly research and development support in your interested remote sensing domain.

Current Trends in Remote Sensing

• Improved Radiometric Resolution for Sensor-centric Characterization
• Encryption of Large-scale Sensed Data over Long Distance
• Environmental Factors Implication on Imaging State
• Employment of Hyperspectral and Multispectral Data for High-Dimension Monitoring

What is the classification of remote sensing data?

Now, we can see the importance of “classification’ in remote sensing. Classification is one of the main processes in digital image processing. While classifying remotely sensed images, it assigns pixel/pixels to the land cover class. If the pixel is bound up with one class, then it is referred to as hard or crisp classification. If the pixel is bound up with multiple classes, then it is referred to as soft or fuzzy classification.

Before implementing the classification process, pre-process method has to be implemented. Since it is necessary to eliminate the artifacts for processing only meaningful information . Here, we have given you a list of preprocessing methods based on artifact types.

• Haze and Low illumination (Histogram Truncation)
• Contextual noise (Difference of Offset Gaussian Filters)
• Speckle noise (Dynamic Thresholding and Image Rationing)
• Contrast Variation (Sub-histogram Equalization and Fuzzy Clustering)
• Blur (Improved S2CVA and Gram-Schmidt Spectral Sharpening)
• Brightness Distortion (Cuckoo Search-based Discrete Wavelet Transform)
• Intensity Variation (Simulated Annealing, Hybrid Ant Colony and Genetic Algorithm)
• Haze (Radiative Transfer-based Detail-Injection Method)
• Irregular Intensity (Alternating Direction Method of Multipliers with Split Bregman Iteration (ADMM-SBI) and Maximum-a-Posterior)
• Brightness and Contrast (BAT algorithm for Histogram Equalized Contrast Enhancement)

            Now, we can see the classification process in detail. The input of the classification process is preprocessed data . Generally, the classification is categorized into two types where one is pixel-based classification and the other is object-oriented classification . In pixel-based classification, the whole scene is processed pixel-by-pixel . So, it is also addressed as per-pixel classification. Object-oriented classification, the whole scene is decomposed into several segments/patches using the image segmentation technique . Further, it is used as high-spatial-resolution imagery where the characteristics are subjected to the conventional fuzzy logic .

Deep Learning (DL) is the most widely used technique for performing supervised learning processes as classification . These supervised classification algorithms are used to allocate unknown pixels in the place of m possible classes. Further, the selection of a particular classifier is based on the input data characteristics. Parametric classification assumes that observed vectors in each class and the spectral band are classified as Gaussian . Nonparametric classification will make such an assumption. For your information, here we have classified some extensively used algorithms .

• Matched Filtering
• Linear Spectral Unmixing
• Spectral Angle Mapper
• Spectral Feature Filtering
• Binary Encoding
• Parallepieped
• Neural Network
• 1Dimensional Density Slicing
• Expert System Analysis
• Nearest-neighbor
• Minimum Distance
• Maximum Likelihood

Further, other primary challenges need to be addressed such as environmental state, system trade-off, high-altitude imaging, etc. Since all these challenges may cause an effect on observation quality. Deep learning plays a major role in tackling many important research challenges . Besides, we also support you with other efficient techniques to solve any sort of complicated research issue . 

Deep Learning Methods for Remote Sensing

The measurement uncertainties like varieties, complexity, and volume create blockages in the data analysis process. To crack this main obstacle, machine learning was employed. These techniques automate the analysis process and enhance the observation quality . In this, it first removes the unwanted artifacts using deep learning which is a subset of machine learning.

The motive of deep learning techniques is to consider the important factors such as pan-sharping, denoising, fusion, single / multi-band super-resolution, restoration, etc. to enhance the Remote sensing and GIS observation. Some of the other commonly used deep learning-based methods are given as follows,

• DenseNet-201
• DenseNet-121
• Generative Adversarial Networks
• Convolutional Neural Networks

Furthermore, we have also given you the other major concepts that work on the principle of deep learning algorithms . Our developers are adept to handle different input datasets using various advanced techniques to achieve desired research objectives/project goals. Here, we have given you some key concepts with their input data and techniques. Similarly, we also support other primary research concepts of remote sensing project topics with their best-fitting research datasets and techniques .

Important Deep Learning Concepts

• Input – SAR and MS
• Concentrated observation-based CNN
• Input – HS and MS
• Concentrated observation-based 3D-CNN
• 2-stream CNNS-based Fusion
• Input – SAR and RGB
• GAN-based Models\
• CNN sub-networks Fusion

Performance Evaluation in Remote Sensing

Although remote-sensing-driven data are evolving fast, it has some in-built errors from different sources . To reduce the errors, we need the source and perform an accuracy evaluation. For that, first , develop metadata before strategy decision and analysis. And also, it needs GIS layers to support classification. For any kind of application, we use appropriate performance metrics to enhance the efficiency of the system .

• True positives
• False positives

On the whole, we are glad to support you in every stage of research from recent research area identification to code development evaluation . Further, we also provide project documentation/dissertation support. If you are a research scholar, then we extend our services in novel remote sensing project topics , phd guidance , research proposal writing, paper writing, the paper publication with an acceptance guarantee, and thesis writing. Therefore, use this opportunity to utilize all your required services in one place like us.

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Generally, remote sensing is the technology that is intended to observe the earth’s surface with the help of satellites, airplanes, and drones by deploying a camera in them. The sensors in the sensing tools capture the data related to the target then it will be processed as an image for the analytical purpose .  In other words, remote sensing is the science technology that is used to gather data of the surface which cannot be manually visualized .

“This article contains the data about the possible Project Topics on GIS and Remote Sensing in wide coverage”

Furthermore, we will see about, how to collect the remote sensing data in real-time . We would like to share the victories about our guidance and project ideas on emerging technology . As we have the benchmark remarks on our guidance in researches and projects for the scholars and the college students we render the best ideologies in the project topics in the current technical requirements.

How to collect the Remote Sensing Data?

• Seaborne – Devices SONAR
• Satellite Sensors – INSAT and Landsat satellites
• Airborne Devices – Air crafts and Drones
• Handheld Devices- Spectrometer, camera, and Transmissometer

These are some of the platforms for acquiring remotely sensed data . We are liable to make the note of our expert’s working flow . They are explained below for your understanding.

Our Experts Working Flow

• Collecting the satellite images and understanding the same
• Pointing the illustration for the ground analysis sample
• Examining and reporting using novel algorithms
• Compare the performance of the remote sensing techniques by more scenarios
• Experiments conducted for different scenarios and also parameters

This is how our experts do the working on remote sensing in general . At this time, research ideas will be resulted in tremendous ways by deploying the right working module. In addition to we are the company with clear ideas and working flows for effective results . The next step is all about an overview of the Project Topics on GIS and Remote Sensing in detail.

Overview of GIS and Remote Sensing

Remote Sensing

• Remote sensing is a tool that is collecting data about the earth’s surface with the help of sensors in satellites or aircraft.
• There are two types of sensors is existing they are active and passive sensors
• Active sensors represent the internal inducements following the earth surface produce energies (laser-beam remote sensing system)
• Passive sensors represent the external inducements that collect the energy produced by the earth surface
• For best instance remote sensing in agriculture will lead to soil management and insects issues and so on

GIS (Geographical Information System)

• The GIS is an effective tool that is integrated with remote sensing for the visualization of the geographic status of the earth
• This is the tool designed for the support of remote sensing in the fields like recording, managing, examining, and visualizing the spatial information

So far we had seen how to collect the remotely sensed images and the overview of the Geographical Information System and the Remote Sensing in detail. Now is the time to know about the working flow of the GIS and remote sensing.

Workflow for GIS and Remote Sensing

• Examining the various (multi) scenes by spectral values
• Acquire data in a geographical location with the use of the following attributes
• Time series
• Multi-image
• Climatic changes
• Same sensors for the spatial and spectral resolution
• This is done based on the multiple scenes that arise in the study
• Collects remote sensing data and gathered it to generate the datasets.
• Next, involves radiometric, atmospheric, and ortho corrections
• The surface is subject to the comparison of the various location, research phases that indulge in more than one scenery capture
• Finally, classify the object or scene for further processing

As of now, we had seen the categorical classifications of the GIS and Remote Sensing in detail. We strongly suggest you have an interaction with our experts for the best outcomes of your research and projects in networking and emerging technologies . Today, we are very happy to transfer our knowledge to you in the sense of research and project guidance. We are always there for you to help in this aspect. Without wasting time let us discuss the current trends on GIS and Remote Sensing .

Current Trends in GIS and Remote Sensing

• This represents the flow of the ocean, temperature, icebergs, and the wavelengths by the observation of the deep seas
• This will help us to maintain the oceans in an effective manner
• This observes the land uses, wildlife environments and point the swamplands
• This will lead us to take safety measures with predetermined methods to protect the environment from pollution
• This will help us to take safety measures in the seashore changes by gathering data on residue transportation through the seas and plotting the sea characteristics
• This is the assessment that identifies the erosions, storms, volcanic activity, and flooding
• The gathered data will be used to take preventive measures before and after exploitations of the hazard events

“Our knowledge hulks are always delighted to assist you in the fields of emerging technology facts and ideologies”

In the emerging technical world, one must know about the programming involved in the Geographic Information System . This is the growing technology; it will enrich one’s career growth abundantly. Every individual can understand this GIS because it doesn’t require a wide knowledge of programming project topics on GIS and remote sensing . We had written this article to the GIS subject matter experts who want to explore more in this field . In this regard, we will see about the programming languages used in remote sensing projects.

Programming languages for Remote Sensing Projects

• Remote Sensing with Python

In the following, an explanation of programming languages is described.

• JavaScript is widely used in every web customization because it is the core language used in it
• Location mapping apps make use of the javascript for the effective results
• The acronym stands for Structured Query Language
• This facilitates to make an influence in the geospatial database
• We can make an influence in the python libraries as they are open-source data that will sink accurately
• It is habitually utilized in the Geographical Information System as its programming language
• C++ and the .NET programming languages are capable of the window-based operating systems instead of Linux and Unix operating systems
• This is a free language which is intended for the examination of the geospatial, visualizations, and arithmetical based computation
• This is the well-developed programming language used in the application creation
• This is the substitute for the above-mentioned languages

This is especially meant for the map servers for instance it is the pillar language used in the GEO map server   far, we have discussed the collection of the Remote sensing data, an overview of GIS and remote sensing, workflow for GIS and remote sensing, current trends on GIS and remote sensing, and programming languages for remote sensing projects in detail.

We hope that these ideas and outlines will be very short and crisp for your better perspectives . In addition to every technology needs research for advancement hence the researchers in the industry continuously researching the emerging technology. Usually, it needs an expert’s guidance as we are having the experts who are filtered out. For any queries feel free to approach us for innovative project topics on GIS and Remote Sensing.

“We are a delight to serve you in the fields of research projects that are emerging in recent technology”

Now we will discuss the tools that are available for the GIS and remote sensing projects wide. Without wasting the time we will move to the next phase. They are mentioned by our expert’s experiments in real-time.

Tools for GIS and Remote Sensing projects

• RTool (open-source tool)
• MATLAB (commercial tool)
• Python (open-source tool)

These can be used following their features. Matlab is a commercial remote sensing tool that is specially designed for effective graphical visualizations . R is the open-source and free remote sensing tool used for investigation purposes and python is widely utilized in the GIS allied errands . Every technology owes some systematic processes. The process will effectively enrich the technology . In this regard, let us see the Major process involved in the GIS and remote sensing in detail.

Major 3 Processes in GIS and Remote Sensing

Image Pre-processing  

• Radiometric, atmospheric and geometric corrections will showcase the effective perspective
• This also leads the interaction with each field by enrichment of the intensity of the images, and effects
• Comparison of the fields leads to elimination of the misrepresentations

Image Enhancement

• Enriching the interpretability of the pictures produced by the sensors
• Spatial element filtering and enriching the boundaries
• Influence of the multi-images by rationing the bands, aspects, canonical aspects, and vegetation features

Image Classification

• This helps to investigate the earth conditions in the form of human-understandable mode
• Temporal pattern recognition helps to identify the changes aroused in the pixel over the era for the modulation of the features
• Spectral pattern recognition filters the radiance in each pixel for the better understanding

These are the classification of the images that are captured by the sensors . We hope that you will understand the aspects and concepts behind them. Following the ease of your understanding, our experts made this article in different real-time perspectives. Without wasting the time we will move to the theme that how to analyze the GIS and remote sensing data .

Investigation of the technology eminently plays a vital role in the effective execution of the technology!!

How to Analysis the GIS and Remote Sensing Data?

• Discovery of the resources (what are all the resources available and where?)
• Immediacy Analysis(discovering the relevant area that is apt for the choice-making)
• Evaluating the geographical areas, detachments, and route extensions
• Examining the location(for fixing the industry in the right place, warehouse implantation)
• Investigation of the network(location apps for ensuring the earlier arrival)
• Spatio-Temporal Investigation(analysing the changes made to the land in the past 20 years)
• Topography examination(analysing the most vulnerable area which is subject to the natural ruins or disasters)

This is how the developers of the GIS and remote sensing subject matter experts will analyze the research topics on GIS and Remote Sensing in general as we do. Our experts of the concern have mentioned to you the trendy project ideas in GIS and remote sensing technology.

Top 10+ Research Project Topics on GIS and Remote Sensing

• Amalgamated urban studies based on Ontologies
• Consideration of the urban transportations
• Innovative cities and semantic technologies
• Route and operation analysis
• Integration of the multimodal in urban areas
• Location discovery services in urban areas
• Urban development data by the sensors
• Reclamation of the geographic data
• Query handling and optimizing of spatial
• Spatial reasoning and designing
• Structural design of the geographical and middleware
• Spatial and temporal handling of geodata

So far we gave you the overall view of the remote sensing technology and the Geographical Information System in wide coverage. Without a doubt, we hope that this article will ensure the best perspectives in real-time. Reach us if you are interested in implementing research project topics on GIS and Remote Sensing . Thanks for reading.

Happy researching and learning with our effective research and project assistance. We are there for you to transfer our knowledge!!

• Project Topics on GIS and Remote Sensing

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Remote sensing and geospatial approaches for studying the environment affected by human activities.

1. Introduction

2. overview of the published contributions, 3. conclusions, conflicts of interest.

• Yue, F.; Li, X.; Huang, Q.; Li, D. A Framework for the Construction of a Heritage Corridor System: A Case Study of the Shu Road in China. Remote Sens. 2023 , 15 , 4650. [ Google Scholar ] [ CrossRef ]
• Wang, W.; Tang, J.; Zhang, N.; Wang, Y.; Xu, X.; Zhang, A. Spatiotemporal Pattern of Invasive Pedicularis in the Bayinbuluke Land, China, during 2019–2021: An Analysis Based on PlanetScope and Sentinel-2 Data. Remote Sens. 2023 , 15 , 4383. [ Google Scholar ] [ CrossRef ]
• Li, X.; Zhou, J.; Huang, Y.; Wang, R.; Lu, T. Quantifying Water Impoundment-Driven Air Temperature Changes in the Dammed Jinsha River, Southwest China. Remote Sens. 2023 , 15 , 4280. [ Google Scholar ] [ CrossRef ]
• Jiang, Y.; Liao, L.; Luo, H.; Zhu, X.; Lu, Z. Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China. Remote Sens. 2023 , 15 , 3995. [ Google Scholar ] [ CrossRef ]
• Chen, Q.; Zhang, H.; Xu, B.; Liu, Z.; Mao, W. Accessing the Time-Series Two-Dimensional Displacements around a Reservoir Using Multi-Orbit SAR Datasets: A Case Study of Xiluodu Hydropower Station. Remote Sens. 2022 , 15 , 168. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; He, Y.; Li, J.; Jiang, Y. Evolution simulation and risk analysis of land use functions and structures in ecologically fragile watersheds. Remote Sens. 2022 , 14 , 5521. [ Google Scholar ] [ CrossRef ]
• Yang, Z.; Zou, L.; Xia, J.; Qiao, Y.; Cai, D. Inner dynamic detection and prediction of water quality based on CEEMDAN and GA-SVM models. Remote Sens. 2022 , 14 , 1714. [ Google Scholar ] [ CrossRef ]
• Li, J.; Qin, T.; Zhang, C.; Zheng, H.; Guo, J.; Xie, H.; Zhang, C.; Zhang, Y. A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN. Remote Sens. 2022 , 14 , 1579. [ Google Scholar ] [ CrossRef ]
• Fu, J.; Zhang, Q.; Wang, P.; Zhang, L.; Tian, Y.; Li, X. Spatio-temporal changes in ecosystem service value and its coordinated development with economy: A case study in Hainan Province, China. Remote Sens. 2022 , 14 , 970. [ Google Scholar ] [ CrossRef ]
• Chen, X.; Zhao, W.; Chen, J.; Qu, Y.; Wu, D.; Chen, X. Mapping large-scale forest disturbance types with multi-temporal CNN framework. Remote Sens. 2021 , 13 , 5177. [ Google Scholar ] [ CrossRef ]
• Li, Q.; Guo, J.; Wang, F.; Song, Z. Monitoring the characteristics of ecological cumulative effect due to mining disturbance utilizing remote sensing. Remote Sens. 2021 , 13 , 5034. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Chen, H.; Liang, J.; Huang, W.; Xu, W.; Li, B.; Wang, J. Extraction of water body information from remote sensing imagery while considering greenness and wetness based on Tasseled Cap transformation. Remote Sens. 2022 , 14 , 3001. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Zhang, C.; Li, J.; Shen, X.; Wu, Q. Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities. Remote Sens. 2024 , 16 , 3364. https://doi.org/10.3390/rs16183364

Zhang C, Li J, Shen X, Wu Q. Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities. Remote Sensing . 2024; 16(18):3364. https://doi.org/10.3390/rs16183364

Zhang, Chengye, Jun Li, Xinyi Shen, and Qiusheng Wu. 2024. "Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities" Remote Sensing 16, no. 18: 3364. https://doi.org/10.3390/rs16183364

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Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies

• Cite this article
• https://doi.org/10.1080/23311916.2022.2100573

1. Introduction

2. review methodology, 3. review findings and discussions, 4. opportunities and challenges of gis and rs technologies, 5. future prospects and emerging technologies for precision irrigation management, 6. conclusion, acknowledgements, disclosure statement, additional information.

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Irrigation is the artificial application of water to crops to supply moisture. With rising drought indices and rapid population expansion, the need for irrigation water for food production is growing. Nonetheless, irrigated agriculture is battling several issues that have resulted in poor performance, inefficient water usage, and low crop water production. Improving water use efficiency in irrigated agriculture necessitates using technology that decreases water losses, matches available supplies to demand, and tracks performance. Geographical Information Systems (GIS) and Remote Sensing (RS) allow for effectively managing water and land resources for irrigation. Current GIS and RS uses in irrigation systems are covered in this study, covering land suitability for irrigation, crop water needs, irrigation scheduling, performance evaluation, and other related applications. The future potential of GIS and RS applications for sustainable irrigation water management are highlighted. This paper offers relevant information for researchers, irrigators, and policymakers on using GIS and RS in irrigation water management and how technological improvements will change irrigation water management to enhance water usage efficiency.

• data-driven
• precision irrigation
• machine learning
• water use efficiency

PUBLIC INTEREST STATEMENT

This research discusses the uses of geographic information systems and remote sensing in irrigation systems. We describe that these tools have been used to discover good agricultural locations, build maps for irrigated regions, determine crop water needs, evaluate irrigation system performance, and make judgments on the map. In addition, we show additional developing technologies, such as IoT, UAV, Big Data, and Machine Learning, which are being coupled with GIS and RS to increase precision irrigation management. With detailed research, we demonstrate that water use efficiency may be enhanced once these technologies are integrated into irrigation systems.

Climate change, particularly protracted drought, has resulted in poor agricultural production, particularly in arid and semi-arid countries. As a result, the use of irrigation systems to adapt to the impacts of climate change has increased. Irrigation contributes about 40 % of the world’s food production from 25 % of the worlds’ land area (FAO, Citation 2020 ). Irrigation ensures the continuous production of food, especially in arid and semi-arid lands of the world. Nevertheless, irrigation is a significant water user sector, as it accounts for 70 % of all freshwater withdraws, of which 60 % is lost in evaporation, seepage and deep percolation (Ungureanu et al., Citation 2020 ). Many countries are experiencing water scarcity, making it difficult to practice irrigation. Increasing drought indices in most parts of the world coupled with rapid population growth threatens the future availability of food and water resources (FAO, Citation 2018a ).

Irrigated agriculture faces tremendous challenges ranging from poor management, deteriorating infrastructure, lack of participation in decision-making, sedimentation of reservoirs (Iradukunda & Bwambale, Citation 2021 ) and poor irrigation scheduling (Kywe & Aye, Citation 2019 ). This has led to the poor performance of most surface irrigation schemes in Africa. On the other hand, pressurized irrigation systems like drip and sprinkler systems are associated with pipe leakages, component failures, high labour costs and variability of soil characteristics leading to spatial variation in irrigation (Belaud et al., Citation 2020 ). Therefore, sustainable management of irrigation systems using robust and holistic tools and software is necessary to improve water use efficiency and productivity (Bwambale et al., Citation 2019a ).

Geographical Information Systems (GIS) have evolved to address various spatial and temporal issues in irrigation and water resource management. GIS is capable of capturing, storing, manipulating, managing, and analyzing geographically referenced datasets and producing output. As a result, it is an essential tool in resource allocation, modelling, optimization, and decision-making processes. Gebeyehu ( Citation 2019 ) recognized GIS and RS as effective and powerful tools for detecting land cover and land use change. In combination with other tools, GIS has been used to understand plant health status, pest infestation, potential yield, soil conditions, crop identification, crop area estimation and agricultural water management (Gebeyehu, Citation 2019 ).

1.1. An overview of geographical information systems and remote sensing

Figure 1. GIS capabilities.

Furthermore, geographic information systems are utilized for the collecting, storage, and analysis of objects and phenomena when the geographical position is an essential property or vital to the research. GIS is a better information system due to its spatial searching and overlay capabilities. GIS is a sophisticated and effective planning tool for resource managers and decision-makers in calculating and analyzing large datasets in a short amount of time. Because of these characteristics, GIS has been used in a wide range of industries, including urban and rural planning, natural resource management, military, business, and agriculture.

Remote sensing is the art, science and technology of obtaining information about objects especially using aircraft or satellites (NOAA, Citation 2021 ). Remote sensors collected data by detecting energy from the earth. The energy can be light or any other form of electromagnetic radiation, force fields, or acoustic energy (Tempfli et al., Citation 2009 ). A remote sensor can be a conventional camera. Light reflected from an object passes through the lens and the light-sensitive film detects it, and a latent image is recorded. A photograph is then developed and generated in the photo lab for interpretation (Tempfli et al., Citation 2009 ). RS defined as above, is applied in many fields, including architecture ( Citation 2021 ), archaeology (Villa, Citation 2011 ), medicine (Suzuki et al., Citation 2012 ), industrial quality control (Barsi et al., Citation 2019 ), robotics (Guo & Wang, Citation 2012 ), extra-terrestrial mapping (Nina et al., Citation 2021 ), etc.

In agriculture, RS and GIS have enabled the spatio-temporal visualization of field information, allowing for monitoring crop and animal real-time conditions. GIS has been used in irrigation for a variety of purposes, including agricultural watershed management (Rudra et al., Citation 1993 ), hydrology (Y. Chen et al., Citation 2004 ), water productivity estimation (Tantawy, Citation 2019 ), irrigation delivery monitoring (Rowshon et al., Citation 2018 ), groundwater assessment (Megahed, Citation 2020 ; Bhunia et al., Citation 2018 ), calculating water demand (Santos et al., Citation 2008 ), spatiotemporal variations of water requirements (Kyagulanyi et al., Citation 2016 ).

(Rudra et al., Citation 1993 ), hydrology (Y. Chen et al., Citation 2004 ), water productivity estimation (Tantawy, Citation 2019 ), Related Research

One of the motivations for this systematic literature review is to develop a rich and comprehensive understanding of the existing application domains of GIS and RS in irrigation systems. Specifically, concerning the application of GIS and RS in irrigation, to the best of our knowledge, there are very few Systematic Literature Reviews (SLR) available.

The most recent review papers in the realm of geospatial technologies in irrigation are by Maina et al. ( Citation 2014 ). The authors review the literature on web GIS-based support services for irrigation management in the study. In our view, the study presents valuable information for researchers on the different tools and future developments of web GIS in irrigation water management. Similarly, Saroj et al. ( Citation 2014 ) reviewed the use of geographical information systems in irrigation water management. The authors suggested that future research should concentrate on building GIS tools that deal with the unpredictability of spatial and temporal data. However, information technology is fast evolving, and various irrigation tools have subsequently been created. A few reviews on remote sensing, particularly remote sensing applications in agriculture, have been published. (Karthikeyan et al., Citation 2020 ), application of remote sensing in precision agriculture (Sishodia et al., Citation 2020 ), and remote sensing for agricultural crops (Alvino & Marino, Citation 2017 ). Besides this study, very few reviews have been published regarding GIS and RS applications in irrigation systems. In the study, we discuss the different application domains of GIS and RS in irrigation and give future prospects of this field.

Our SLR is unique as it discusses the potential integration of other technologies to improve irrigation water management. Emerging technologies like cloud technology, real-time data, Internet of Things (IoT), 3D GIS, Mobile GIS and Unmanned Aerial Vehicles (UAVs), Big data and Machine Learning that are improving the capabilities and horizons of GIS are discussed in relation to agricultural water management.

The Systematic Literature Review (SLR) guidelines described by Xiao and Watson ( Citation 2019 ) were followed for this study. Keywords were chosen to obtain target results to address the study questions. The following search phrase was used for seeking the most relevant literature to our research:

(``GIS” OR `` Geographical Information System” OR `` Remote Sensing” OR ``Geomatics”)

(“Irrigation” OR “Agricultural water management”).

Table 1. Number of publications obtained from databases

Figure 2. Selection criteria.

Figure 3. Publications for the SLR: a) Before duplicates are removed b) After duplicates are removed, c) Publications according to application domain d) Number of publications per year.

3.1. GIS-based irrigation suitability analysis

Suitability analysis is a technique of selecting the optimal place to locate goods or regions to conserve. The site to be selected relies on the site-specific characteristics. Various GIS applications contain suitability tools/Modelers used for performing out suitability studies. A suitability Modeler is an interactive, exploratory platform for generating and assessing a suitability model (ESRI, Citation 2021 ). The Modeler delivers analytical input at each level of the model generation process and enables for smooth back and forth movement between stages. (ESRI, Citation 2021 ).

Figure 4. Flowchart for land suitability for irrigation. adapted from (AL-Taani et al., Citation 2021 ).

Table 2. Suitability analysis studies using GIS and RS

3.2. mapping in irrigation, table 3. mapping in irrigated agriculture.

Several researchers have used this procedure to map irrigated agricultural systems. Gumma et al. ( Citation 2011 ) mapped irrigated areas in Ghana using MODIS data at 30 m and 250 resolution, Ketchum et al. ( Citation 2020 ) used Irrimapper, using machine learning approaches to determine the trends and extent of irrigation coverage in the United States and noted a 15% increase of irrigated agriculture over 33 years. Vogels et al. ( Citation 2019 ) presented an object-based approach to map irrigated agriculture in the Central Rift Valley in Ethiopia using SPOT6 imagery. The authors came to the conclusion that SPOT6 imagery and GIS mapping are beneficial for assessing spatiotemporal dynamics of irrigated (smallholder) agriculture in complicated terrain and provide a foundation for agricultural land and water management. Mapping irrigated areas and rainfed areas under the same cropping conditions was done by Jin et al. ( Citation 2016 ) in China using Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B.

The process of measuring soil salinity entails locating hotspots in an irrigated area. The following GIS tools are necessary for soil salinity mapping: Excel, ArcMap with the spatial analyst extension, Arc Toolbox, and Arc Catalog. To generate continuous surface maps from discrete sample points, the Spatial Analyst extension is necessary. Additionally, this expansion offers a wide range of analytic and spatial modelling capabilities, such as converting features (points, lines, and polygons) to rasters, carrying out zone and neighborhood studies, and modelling and analysing both raster and vector data. ArcPad and Geostatistical Analyst are further optional programs and extensions. Using a hand-held device equipped with a GPS, ArcPad is an application used to specify and georeference the boundaries of the surveyed area The Geostatistical Analyst extension can be used to define a spatial model that will be integrated into the kriging interpolation. New trends in mapping irrigated areas involve the use of advanced technologies, object-based image classification and non-parametric machine learning algorithms, and artificial intelligence evolved in a GIS environment (Basukala et al., Citation 2017 ;; Ketchum et al., Citation 2020 ).

3.3. Determination of crop water requirements

The amount of water a crop needs to grow in wide fields under unrestrictive soil conditions, such as soil water and fertility, and to reach its maximum output potential in the given growing environment is known as the crop’s water requirements (Pereira & Alves, Citation 2013 ). Crop evapotranspiration is a product of reference evapotranspiration and the crop coefficient. Detailed information on crop water requirements can be found in R. G. Allen et al. ( Citation 1998 ).

In a GIS environment, crop water requirements are estimated spatially using an inbuilt FAO Penman-Monteith Model that estimates the reference crop evapotranspiration. Monthly crop coefficients values are associated with each crop class and a reference evapotranspiration map derived from meteorological data (Casa et al., Citation 2009 ). Several researchers have used this approach to determine crop evapotranspiration (Elnashar et al., Citation 2021 ; Fares et al., Citation 2016 ; Ramírez-Cuesta et al., Citation 2018 ; Tessema Roba et al., Citation 2021 ; Garrido-Rubio et al., Citation 2018 ). Reyes-González et al. ( Citation 2018 ) estimated crop water use using a satellite-based vegetation index in northern Mexico for four growing seasons. The authors developed crop evapotranspiration maps which were used for irrigation scheduling. Similarly, Ramírez-Cuesta et al. ( Citation 2018 ) developed a novel ArcGIS toolbox which has been useful in estimating the amount of water required by a crop by integrating a dual crop coefficient method with multi-satellite imagery. The tool is appropriate for irrigation scheduling and other applications that account for water balance in agroecosystems.

For mapping and tracking ETa (actual evapotranspiration) at the field size, remote sensing methods using satellite and aircraft photography have become more popular in recent years. For example, potential differences in spatial and temporal resolutions have proved helpful for calculating ETa based on thermal infrared (TIR) data. Additionally, a number of sensors have been extensively used for mapping ETa in irrigated agricultural areas, including Landsat-TM/OLI (60–120 m), Moderate-resolution Imaging Spectrometer (MODIS) (1 km), ASTER (90 m), and GOES (4 km). The capabilities were recently improved with the start of the ESA’s Sentinel-2 and Sentinel-3 missions. The lack of fine spatial resolution in satellite TIR pictures is a key drawback despite extensive study on ETa estimates with satellites. Furthermore, ground validation is still insufficient.

Figure 5. Remote sensing determination of evapotranspiration.

3.4. Irrigation scheduling

Table 4. gis-enabled irrigation scheduling, 3.5. irrigation systems management.

The right allocation of water resources to distribute irrigation water with variable irrigation requirements is necessary to improve irrigation scheme management. Crop water requirements may vary in space and time and using the spatial data management and utilization tools of GIS, the Spatio-temporal variation in water allocation can be harmonized (Rowshon et al., Citation 2009 ) Citation 2009 . GIS provides an effective information system of the scheme that is interactive and representative of the hierarchy of irrigation system operation. Rowshon et al. ( Citation 2009 ) developed a Rice Irrigation Management System (RIMIS) in a GIS environment. The developed system was able to provide information on the uniformity of water distribution, with maps as output, irrigation managers were able to make informed decisions in regard to the operation and management of the scheme. Likewise, Garrido-Rubio et al. ( Citation 2020 ) Citation 2020 ; Maina et al. ( Citation 2014 ); Mohd et al. ( Citation 2014 ); ; Valencia et al. ( Citation 2020 ) Citation 2020 ; and Yang et al. ( Citation 2016 ) have applied GIS and RS techniques in designing irrigation canals, visualization of water management and demand, groundwater assessment, and evaluating irrigation approaches under various environmental conditions.

3.6. Decision support systems

Table 5. gis-based decision support systems for irrigation management, 3.7. irrigation performance assessments.

The systematic observation, documenting, and interpretation of activities connected to irrigated agriculture with the goal of continual improvement may be characterized as irrigation system performance assessment (Bwambale et al., Citation 2019b ). Evaluation of irrigation scheme performance is possible when water balance and information is combined with remotely sensed evapotranspiration. Recent developments in GIS and RS make it possible to assess irrigation performance on a pixel-by-pixel basis (Taghvaeian et al., Citation 2018 ). Performance Indicators like adequacy, equity, dependability, sustainability ratio, drainage ratio and drainage uniformity were estimated by Taghvaeian et al. ( Citation 2018 ) using GIS. Nikam et al. ( Citation 2020 ) used indicators of agricultural performance, water delivery performance and physical performance using remotely sensed data and GIS to evaluate the performance of a large scale irrigation project at a disaggregated level. The authors reported that the project positively impacted agriculture and economic growth of the region, despite excessive water logging in some parts of the command area and inadequacy at the tail end. Zafar et al. ( Citation 2021 ) demonstrated that using remote sensing-derived information to complement classic canal flow data with spatially discrete field-level water use information is essential to improve equity in canal water delivery in Punjab, Pakistan. Various organizations have developed models for monitoring land and water productivity. They make use of satellite data that is input and analyzed in a GIS environment to determine land and water productivity using inbuilt algorithms (Bastiaanssen & Steduto, Citation 2017 ). FAO’s WaPOR database helps monitor agricultural water productivity using satellite data analyzed in a GIS environment (FAO, Citation 2018a , Citation 2018b ). Researchers (Blatchford et al., Citation 2018 , Citation 2020 ; Sawadogo et al., Citation 2020 ; Tantawy, Citation 2019 ) have already used this database to monitor agricultural water productivity. The authors report that the GIS-enabled database is very accurate and important in estimating water productivity in production areas where field data is unavailable. This makes GIS a very important tool in estimating the productivity of land in both rainfed and irrigated agricultural systems.

GIS and remote sensing have shown to be excellent methods for monitoring irrigated areas in various locations throughout the globe under various environmental circumstances. They offer synoptic coverage of irrigated fields in numerous spectral areas and with temporal frequencies suitable to estimate agricultural water usage at various crop development stages. Furthermore, archival data that spans several years allows for picture comparison, demonstrating evolution. Because satellite data is digital, it is extremely simple to incorporate into a GIS for synthesis or comparison with other data sources. Remote sensing reduces the cost and time required to acquire datasets compared to conventional statistical surveys, which may entail aerial photography over huge regions. As a result, GIS and remote sensing technologies have become vital in underdeveloped nations where financing for data collecting for irrigation research is limited. Moreso, remote sensing offers meaningful geographical information on the specific locations of irrigated areas rather than just totals inside arbitrary administrative subdivisions. This is vital for optimizing water distribution, measuring irrigation performance, giving irrigation schedules, enumerating environmental effect, objectively assessing irrigation water consumption and understanding irrigation dynamics (Ozdogan et al., Citation 2010 ). Finally, remote sensing may give timing data, both in terms of the number of irrigation-related vegetation peaks and the amount of time irrigation is used over the course of a year.

Difficulty distinguishing between irrigated and non-irrigated lands. In humid places, separating irrigated fields from non-irrigated plots is challenging due to significant overlap in their spectral signatures. For example, during some development phases, the fingerprints of flooded irrigated fields may overlap with those of natural wetlands, reducing mapping accuracy. Researchers have addressed these constraints by combining time information on crop planting, maturity, and harvest with spectral data. Unfortunately, the gathering of remotely sensed data is limited by the orbit and return interval of a specific satellite, thus observations are not always caught at optimal periods. The availability of optical data is also a challenge in locations with regular cloud cover, such as humid tropical and subtropical settings.

The other difficulty faced with geospatial technology is real-time irrigation scheduling. Precision irrigation management attempts to provide the plant with the optimal moisture conditions in both time and space. These geospatial technologies, particularly satellite images, are unable to provide real-time data collecting to aid with irrigation scheduling choices. This complicates matching crop water demand with supply across wide regions in time and space.

Finally, detecting agricultural fields via remote sensing is problematic because irrigated landscapes are a subtype of croplands, which have historically proven difficult to map. Agrarian fields (particularly irrigated fields) are very dynamic since each field is at a distinct stage of development and hence may be mistaken with natural land cover classes. The categorical detail of land-cover maps is often negatively proportional to their accuracy. Because agriculture is difficult to identify and map in and of itself, recognizing irrigated regions as a subclass of farming becomes much more challenging. Perhaps here is where temporal data profiles will come in handy for distinguishing irrigation from rainfed crops.

GIS and RS technologies do not work in isolation; they must be integrated with other tools to acquire, store, visualize, analyze, and output geographical data effectively. The combination of geospatial technology with advances in agricultural Internet of Things, Artificial Intelligence, 3D GIS, Mobile GIS, Big data, and Machine Learning makes collecting and processing spatiotemporal agricultural data simpler.

Figure 6. Technological advancements that will shape future irrigation systems.

5.1. Cloud technology and GIS in irrigation systems

In precision irrigation technologies, cloud computing combined with geographical information systems will be critical to ensure variable rate application. In the cloud, revolutionary algorithms and software have the potential of thoroughly analyzing field conditions to estimate spatially distributed crop water requirements (Debauche et al., Citation 2018 ). The data can then be communicated to an irrigation control to perform for actuation. The farmer then can view soil moisture status on his smartphone or a web interface and with just a click, the farmer can control the system where he/she is. This has the potential of saving irrigation water and eliminating high operational costs.

Complex computations from ET algorithms can easily be handled and data sent to a GIS-enabled smartphone in the form of irrigation water to be applied. Forcén-Muñoz et al. ( Citation 2022 ) described the Irriman Platform, a system based on cloud computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists optimize irrigation procedures through a web-based tool that allows them to elaborate irrigation plans and evaluate their effectiveness over crops. Similarly, Younes and Salman ( Citation 2021 ) developed a cloud-based smart irrigation system leveraging satellite data. The system provides communication interfaces to trigger irrigation scheduling in the field. The application enables building a decision-support system that allows an inexperienced farmer to practice productive farming in a semi-automated manner. Other applications include, among others, the determination of actual evapotranspiration (Laipelt et al., Citation 2021 ), assessing irrigated area expansion (Naboureh et al., Citation 2021 ), and automated mapping of cropped areas (Xiong et al., Citation 2017 )

5.2. Internet of things for irrigation management

The advent of the Internet of Things (IoT) has resulted in a paradigm shift in agricultural water management. Traditionally, farmers use heuristic approaches to schedule irrigation which oftentimes results into over or under irrigation (Kashyap et al., Citation 2021 ). Closed-loop control strategies ensure that the amount of water to be applied is determined by the current soil moisture status and prevailing crop environment. IoT provides a platform for precision irrigation, wirelessly connecting several soil sensors and other different hardware on the farm. The coupling of Geographical Information Systems with the Internet of Things is essential in the automation of irrigation systems. The combination of Geographical Information systems and IoT will bring a dynamic change in the irrigation industry and other sectors as well. IoT technology is one of those techniques in which, by using the normal internet protocol, the desired technology may wirelessly transmit the data gained by digitizing the data of the object or domain of the adapter that it is adapting to the server. With a system architecture comprising of soil moisture sensors, weather sensors and solenoids placed on sprinklers, Lora modulation and 4 G connection, real-time irrigation requirements can be met (Rehman et al., Citation 2022 ).

Recently, Abioye, Abidin, et al. ( Citation 2021 ) implemented an IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment. The authors used plant sensors to monitor water stress and weather sensors to monitor the plant environment conditions and the data was sent over a wireless sensor network. The system was able to schedule irrigation in real-time. Other applications of IoT Integrated with GIS/RS include Real-time detection of irrigation water pollution (Lin et al., Citation 2020 ), estimation of leaching requirement (Bashir et al., Citation 2020 ), irrigation scheduling for water management (Roy et al., Citation 2021 ) and irrigation management (Liu, Citation 2021 ).

5.3. Augmented reality and virtual reality

Augmented reality is a field of research that combines the real environment and computer-generated data to increase the human perception of a scene in real-time, enhancing visible details and displaying invisible or inexistent objects (Mirauda et al., Citation 2017 ). This virtual-real merge is achieved via the appropriate Graphical User Interfaces (GUIs) on the user’s hand-held portable devices (smartphones, tablets, etc.). Future outdoor activities will be transformed by augmented reality. It gives mobile field personnel a handy tool for visualizing their surroundings, providing related information, and reducing the need to read maps and documentation while monitoring and managing operations. This will assist in bridging the gap between GIS data on a PC that is not helpful to field employees in real-time. An irrigator may use augmented reality to aim a phone camera at the ground and observe the position and direction of water pipelines and power wires buried under the earth. The AR software uses the GIS system and the irrigator’s position to visually represent the infrastructure underneath. This assists in detecting leaks in subsurface drip irrigation systems and subterranean conveyance pipelines.

3D GIS adds another dimension(z) to objects for greater details and visibility 3D GIS adds an elevation component to objects that 2D maps are unable to provide. 3D technologies in GIS maps gives explanatory illustrations to represent the scale of real-world objects (Jacob, Citation 2019 ). The models in three dimensions result in a great appearance, aiding surveying in different domains. Integrating 3D technologies can help irrigation engineers envision, evaluate and analyze how certain changes in irrigation layouts will look like, and how sustainable the changes are. A typical 3D model would consist of irrigation infrastructure information, satellite imagery and data to help irrigation designers decide on the most economical routes for irrigation conveyance systems. In ArcGIS, it is possible to integrate 2D GIS and 3D GIS for greater visualization and simulations of irrigation systems (Yang et al., Citation 2016 ). With this integration, it is possible to assess the impact of flooding on irrigated agriculture in 3D using GIS 3D spatial analysis tools.

By using 3D geographic information system (3D GIS) and remote sensing (RS) technologies, Cheng et al. ( Citation 2015 ) offered a simple, practical, and cost-effective technique. 3D terrain and ground feature models were built interactively using multi-source data such as Google Earth (GE) high-resolution remote sensing images, ASTER G-DEM, hydrological facility maps, etc. After that, both models were produced with texture data and linked into the ArcGIS platform. A vibrant, realistic 3D virtual picture of an irrigation field was created with a nice visual impact and basic GIS tools for data retrieval and analysis.

5.5 Mobile GIS

Mobile GIS makes it easier to conduct feasibility studies for irrigation development by allowing field personnel to gather, save, update, edit, analyze, and display geospatial data and information. This is made possible by integrating mobile devices, GIS software, GPS, and wireless connections that provide internet-based GIS access. With mobile GIS, it is possible to make changes and updates in real-time while collecting data, accelerating processing and increasing accuracy. Several mobile GIS applications have been developed for numerous purposes, and because to significant developments in mobile technology, these applications now go well beyond basic mapping (Mostafa, Citation 2019 ). Some of these applications were developed by private sector companies for commercial purposes. Weather forecasting, acquiring market prices, and detecting plant diseases are a few examples of mobile GIS applications purposefully designed for irrigated agricultural systems.

5.6 Unmanned aerial vehicles

An unmanned aerial vehicle (UAV) is an aircraft that carries no human pilot or passengers. UAVs, sometimes called “drones” can be fully or partially autonomous but are more often controlled remotely by a human pilot (RAND, Citation 2020 ). Unmanned aerial vehicles (UAVs) are evolving toward autonomy, choosing where to fly based on deep learning, a variety of navigational signals, and sensor data. In order to create accurate 3D models and orthophotos of a region of interest, on-demand detailed picture gathering with UAVs is a quick and affordable alternative (Giacomo & David, Citation 2018 ). UAV‐based remote sensing and GIS mapping of processed data can be used for irrigation scheduling, plant water status assessment and nutrient assessment applications. Recent studies have applied UAVs in energy balance of an irrigated field (Ortega-Farias et al., Citation 2021 ), surface moisture mapping (Zeyliger et al., Citation 2022 ) and determination of actual evapotranspiration (Mokhtari et al., Citation 2021 ). Remote sensing using unmanned aerial vehicles can provide precise information of irrigated crop areas through monitoring the phenological development of crops through multi-temporal images.

5.7 Big data and machine learning

To deliver sustainable irrigation, irrigation managers need to employ cutting edge technologies like big data, machine learning and blockchain. Data-driven- precision irrigation with these technologies is the most promising approach to solve existing and future problems (Alfred et al., Citation 2021 ; Bhat & Huang, Citation 2021 ; Kashyap et al., Citation 2021 ). Once vast data is obtained from an irrigated field, it can be used to drive irrigation and other farming decisions. Data-driven agriculture has been proven to improve crop yield, reduce cost, and ensure sustainability. The integration of GIS, RS with Big data and machine learning encourages the automation of farm data collection, analysis and decision making. GIS-based data management systems have been developed to help farmers with specific irrigation-related tasks like weather prediction, mapping, irrigation scheduling, automated field management, Big data, and Machine Learning tools and the increase in computational power has made it possible to develop algorithms from data collected from the field either by UAVs, satellites and sensors to make decisions.

Data-driven machine learning models for irrigation have gained attention (Abioye, Abidin, Aman et al., Citation 2021 ; Bwambale et al., Citation 2022 , Citation 2023 ; Lozoya et al., Citation 2014 ; McCarthy et al., Citation 2014 )) over physical/mechanistic models to predict soil moisture content because data-driven models require less data for calibration as compared to mechanistic model calibration. In addition, ML models process spatial and temporal data easily in less time and show good predictive performance over physical models. The ML model-based irrigation systems have a closed-loop controller that learns from calibration, in which the system itself employs feedback from the comparison between pre-processed data and measured real-time data (Abioye et al., Citation 2023 ).

Big data and machine learning techniques have been applied in paddy rice to ensure the optimum water level is maintained in the field. Climate data is obtained using satellites, drones ae used in monitoring water stress while a wireless sensor network is used to build a smart irrigation system for paddy. Significant increases in yields have been reported using this approach of maintaining a certain amount of water in the paddy field using a data-driven approach.

This study examines the most current uses of Geographical Information Systems in irrigation. Furthermore, developing technologies in irrigation management are highlighted. Geographical Information Systems have been used in land suitability analysis mapping, crop water consumption estimation, irrigation scheduling, productivity monitoring, and irrigation performance evaluations. Combining GIS with existing irrigation management systems is more powerful and effective when dealing with significant amounts of geographical and temporal data. It is used to organize, retrieve, and show geographical and geographic data for irrigation at the field or basin size. Despite the fact that a few tools and systems have been developed to simulate irrigation models, there is still a need for new tools to examine spatial irrigation water needs, which might be produced by altering current GIS software. Technological and application advancements in GIS will determine the future usage of these geospatial tools in irrigation systems. Integration of cloud computing, Internet of Things Big Data and Machine Learning, 3D GIS, Mobile GIS, Augmented and Virtual Reality, and Unmanned Aerial Vehicles with GIS and Remote Sensing technologies will transform agriculture and continue to play an important role in agriculture 5.0. In site-specific precision irrigation, smart irrigation systems like as drip and sprinkler irrigation are widely employed. With water supplies becoming scarce, sophisticated water management and irrigation techniques are required to conserve this valuable resource. To address under- or over-irrigation issues, as well as soil leaching or drying, requirement-based irrigation may be carried out utilizing a mix of geospatial technologies, information systems, and decision support systems.

The authors are grateful to the West African Centre for Water, Irrigation and Sustainable Agriculture (WACWISA), and the University for Development Studies.

No potential conflict of interest was reported by the author(s).

Notes on contributors

Erion Bwambale

Erion Bwambale is a PhD Candidate at the West African Center for Water, Irrigation and Sustainable Agriculture (WACWISA) hosted at the University for Development Studies in Ghana. Erion is an early-career researcher passionate about irrigation systems modelling for improved water use efficiency. He is also the Irrigation Engineer and co-founder at Irrisol Engineering Ltd, a company that provides farmers with custom-designed water and irrigation solutions in Uganda. He holds a masters degree in Civil Engineering with a specialization in Irrigation and Drainage from Jomo Kenyatta University of Agriculture and Technology, Kenya and a B.Sc in Agricultural Mechanization and Irrigation Engineering, Busitema University, Uganda. He is a Member of ASABE, ASCE, PASAE and ICID.

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Msc geograp.information systems & remote sensing.

• Study at Maynooth /

Qualification : MASTER OF SCIENCE DEGREE

Award Type and NFQ level : TAUGHT MASTERS (9)

CAO/PAC code : MHN58

CAO Points :

Closing Date : 31 July 2025

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• Entry Requirements
• Department People
• Course Structure
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• How to Apply

Given the wider development of Citizen GIS and an increased public awareness and knowledge of the power and value of spatial data, vastly increased amounts of such data from different sources are now available to researchers. However, in order to turn these data into useful information, they must be efficiently managed, processed and analysed before being displayed in a comprehensible format. Geographical Information Systems and the associated field of Remote Sensing greatly aid us in such tasks. The course is equally split between both parts - GIS and Remote Sensing - with four core modules introducing the theory and practice of both subject at an introductory and advanced level. Geographical Information Systems or GIS as they are better known, are widely used in a wide variety of subject fields across the physical and social sciences and even in the humanities, with applicability in everything from archaeology and astronomy to geomorphology and globalisation to soil science and social planning. Remote Sensing – the analysis and interpretation of aerial and satellite imagery – has transformed the manner in which we view the Earth. The synoptic view of the Earth that it has given us has greatly improved our understanding of atmospheric and oceanic processes, sustained environmental management and the interaction of humans with the natural world. It is now a standard research tool in many fields such as geology, geography, pollution control, agriculture and climatology. Additional optional modules in Programming, Spatial Databases and Marine Remote Sensing are also available to students who want to develop the technical side more fully, though the course has a strong applied flavour throughout. In addition, all students complete a work placement in the summer months which allows them to gain valuable practical experience to test and develop the skills learnt across the course.

The MSc in GIS and Remote Sensing is accredited by both the Society of Chartered Surveyors Ireland (SCSI) and the Royal Institution of Chartered Surveyors (RICS) in the UK. Graduates of this MSc are eligible to proceed along the pathway to become Chartered Geomatics Surveyors.

Aims of the Course

- To provide highly qualified, motivated graduates who have been trained in Geographical Information Systems, Remote Sensing and Digital Image Processing and who can apply the information technology skills they obtain.

- To produce marketable graduates who will make significant contributions to GIS and RS application areas including; industry, government, academia, the community and voluntary sector and other public and private bodies.

- To provide an understanding of Geographical Information Systems and Remote Sensing, the technology involved and its applications for specific investigations.

Commences September (*optional courses may start ahead of the formal term date).

The basic entry requirement is a degree with a minimum of Second Class Honours (2:1) or equivalent in any of the following subjects: Geography, Planning, Physics, Computer Science, Environmental Science, Geology, Mathematics, Engineering, Geophysics, Public Administration, Public Health or a cognate discipline. Applicants must have a recognised primary degree which is considered equivalent to Irish university primary degree level.

Minimum English language requirements:

Applicants for whom English is not their first language are required to demonstrate their proficiency in English in order to benefit fully from their course of study. For information about English language tests accepted and required scores, please see here . The requirements specified are applicable for both EU and International applicants.

Maynooth University's TOEFL code is 8850

Dr Stephen McCarron

Head of Department

Department of Geography

Department of ICARUS

Read Full Biography

Prof Mark Boyle

Department of Social Sciences Institute (MUSSI)

Dr Patrick Bresnihan

Prof Chris Brunsdon

Department of National Centre for Geocomputation (NCG)

Department of Hamilton Institute

Dr Conor Cahalane

Dr Ro Charlton

Dr kevin credit.

Laure De Tymowski

Dr William Durkan

Prof rowan fealy.

Dr Ronan Foley

Department of ALL Institute

Dr Alistair Fraser

Prof mary gilmartin.

Dr Adrian Kavanagh

Prof Gerry Kearns

Dr Sinéad Kelly

Prof Rob Kitchin

Dr Carla Mateus

Dr gerard mccarthy.

Prof Conor Murphy

Dr Lisa Orme

Dr Martina Roche

Dr carla sa ferreira.

Dr Nick Scroxton

Dr Helen Shaw

Department of Faculty of Social Sciences

Prof Peter Thorne

Prof Karen Till

Dr Chris Van Egeraat

The course consists of six modules, five of which are compulsory. Two of these cover the theoretical concepts underpinning GIS and Remote Sensing. Two other modules involve gaining the theoretical and technical skills necessary to become proficient in the management and analysis of spatial data. A fifth module involves an assessed work placement during the summer months.

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