Remote Sensing, GIS, and Machine Learning in Water Resources Management for Arid Agricultural Regions: A Review

Abstract

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions.

1. Introduction

Water scarcity represents a major constraint to sustainable development in arid and semi-arid regions, where limited precipitation, high evapotranspiration rates, and increasing anthropogenic demands place extreme pressure on freshwater availability. These climatic and geographic characteristics severely limit the natural recharge of surface and groundwater resources, leading to a heavy reliance on non-renewable fossil aquifers. In this review, arid and semi-arid regions refer to areas characterized by low and irregular rainfall, high potential evapotranspiration, and chronic water scarcity—conditions that often coincide with what are broadly termed vulnerable agricultural landscapes, where climatic and hydrological stresses critically limit agricultural productivity and sustainability. In such environments, agriculture is the dominant land use and the principal consumer of water, accounting for more than 70% of freshwater withdrawals globally, and exceeding 80% in many arid countries such as those in the Middle East and North Africa (MENA)—underscoring the urgent need to improve irrigation efficiency and overall water productivity [1,2].

The traditional expansion of irrigated agriculture in these regions has often been driven by national food security strategies and government incentives, but it has also resulted in unintended environmental consequences. Over-extraction of groundwater has led to declining water tables, land subsidence, and salinization of soil and aquifers, threatening the long-term viability of agriculture itself [3,4]. For instance, recent investigations have demonstrated that excessive groundwater withdrawal for irrigation has caused significant land subsidence and sinkhole development in several endorheic basins of Turkey. For example, in the Konya Basin, Orhan et al. [5] used multi-temporal Sentinel-1 InSAR data to map subsidence rates of up to 90 mm per year, linked primarily to aquifer over-exploitation and rapid agricultural expansion. Their findings also revealed more than 660 new sinkholes formed between 2014 and 2022, confirming the accelerating anthropogenic influence on subsurface stability in semi-arid karstic terrains. These results underscore the urgent need for sustainable groundwater management policies to prevent irreversible land deformation [5]. Furthermore, it is challenging for water managers and policymakers to evaluate these impacts or implement corrective measures due to a lack of timely and spatially explicit data. Such dynamic, large-scale phenomena cannot be sufficiently monitored by the conventional reliance on point-based measurements and manual field surveys.

To address these challenges, recent years have witnessed a surge in the adoption of advanced geospatial technologies—namely, remote sensing (RS), Geographic Information Systems (GIS), and machine learning (ML). Remote sensing allows for regular, synoptic observation of land surface conditions, including vegetation health, evapotranspiration, and land use changes, making it indispensable for monitoring agricultural systems and natural resource trends at scale [2,6]. GIS, on the other hand, provides a framework for spatial data integration, modeling, and visualization that supports hydrological analysis, zoning, and decision-making [7,8]. Meanwhile, machine learning has emerged as a powerful tool to analyze complex, multi-source datasets and to detect patterns, forecast trends, and support decision support systems in water resource management [9,10].

Importantly, the synergy between these technologies amplifies their individual strengths. Remote sensing generates continuous, spatially extensive observations; GIS structures and contextualizes these observations across relevant environmental and administrative units; and ML models learn from both historical and real-time data to produce actionable insights. Previous studies have illustrated the utility of such integrated approaches. For instance, Rabie et al. [11] demonstrated that accurate flood hazard mapping could be achieved using a combination of digital elevation models (DEMs), limited field data, and optimized stream gauge configurations—highlighting the potential of geospatial tools in data-scarce regions [11].

This review synthesizes the latest applications of RS, GIS, and ML in water resource assessment and agricultural monitoring, with a particular focus on their use in arid and semi-arid regions. In addition to identifying current research gaps and institutional barriers to widespread adoption, it looks at the methodological frameworks and technological developments that make them easier to use in real-world decision-making contexts.

To structure this synthesis, the review is organized around four key objectives:

• To compare water resource usage and efficiency across different arid agricultural systems using geospatial and RS data [12];
• To evaluate methodologies for monitoring agricultural practices and land use dynamics, with emphasis on crop mapping and irrigation assessment using satellite and GIS technologies [13];
• To assess groundwater usage efficiency using integrated RS and ML models to measure actual water consumption and irrigation performance [9,14];
• To develop sustainable water management strategies by applying policy-relevant frameworks such as the Drivers–Pressures–State–Impact–Response (DPSIR) model [15,16].

Despite rapid advances in geospatial and data-driven technologies, a comprehensive synthesis of how RS, GIS, and ML jointly contribute to sustainable water management in arid agricultural regions remains limited. Existing reviews often focus on individual tools or specific hydrological processes rather than integrated, decision-support perspectives. This paper addresses that gap by consolidating recent developments, identifying methodological synergies, and highlighting barriers, particularly those linked to data scarcity, model interpretability, and institutional adoption. By doing so, it aims to provide both researchers and policymakers with a structured overview of emerging directions for water governance in dryland agriculture. This review seeks to provide insights to researchers and practitioners in the field by critically analyzing these themes in order to support more resilient and knowledgeable water governance systems in arid zones that rely on agriculture.

2. Review Methodology

This review was conducted through a systematic and comprehensive search of peer-reviewed literature published between 2010 and 2025 using major databases, including Scopus, Web of Science, and Google Scholar. The following keyword combinations and Boolean operators were used to identify relevant studies: (‘remote sensing’ OR ‘RS’) AND (‘geographic information systems’ OR ‘GIS’) AND (‘machine learning’ OR ‘artificial intelligence’) AND (‘water resources management’ OR ‘irrigation’ OR ‘groundwater’) AND (‘arid’ OR ‘semi-arid’ OR ‘drylands’ OR ‘agriculture’).

Search results were refined by excluding non-English publications, conference abstracts, and reports lacking full-text availability. Additional studies were identified through backward and forward citation tracking to ensure comprehensive coverage. The review emphasized studies on RS, GIS, and ML applications in arid and semi-arid agricultural water management, while selectively including seminal references that provide essential methodological or conceptual context.

Each selected article was evaluated for study area, methodology, data source, and key findings to identify trends, research gaps, and integration opportunities. Although this review draws on examples from diverse arid and semi-arid regions worldwide, the findings are interpreted in terms of shared environmental and hydrological characteristics rather than geographic uniformity.

The objective is to highlight transferable methodological insights while recognizing that local climatic, institutional, and data conditions may influence the applicability of specific approaches.

3. Remote Sensing Technologies for Water Resource Monitoring

Remote sensing (RS) has transformed the monitoring of hydrological and agricultural systems by providing scalable, repeatable, and cost-effective observations over large and inaccessible regions. In arid and semi-arid zones—where water scarcity, rapid land use change, and limited ground monitoring converge—RS plays a crucial role in enabling data-driven decision-making. Satellite-based remote sensing offers synoptic coverage at multiple spatial and temporal resolutions, facilitating the assessment of variables such as land cover change, vegetation dynamics, soil moisture, surface water extent, and evapotranspiration [2,12].

3.1. Surface and Groundwater Detection

One of the most direct applications of RS is the delineation and monitoring of surface water bodies. Optical remote sensing sensors, such as those onboard the Landsat and Sentinel satellites, exploit differences in reflectance—particularly in the near-infrared (NIR) and shortwave infrared (SWIR) bands—to distinguish between land and water. These approaches have been widely used for mapping reservoirs, wetlands, and rivers, as well as detecting seasonal and long-term changes in water availability [17]. However, optical methods are limited by cloud cover, atmospheric disturbances, and vegetation masking.

To overcome these challenges, microwave remote sensing—especially Synthetic Aperture Radar (SAR)—has become a preferred tool for surface water detection in all weather conditions and during both day and night [18,19]. SAR’s ability to penetrate vegetation and detect backscatter variations allows for consistent monitoring of inundation and moisture dynamics even in densely vegetated or cloud-prone regions.

Although direct remote sensing of groundwater is not possible, several proxy indicators can be monitored to infer groundwater dynamics. Changes in vegetation vigor, surface soil moisture, and land subsidence can signal groundwater extraction or recharge activity. The Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided regional-scale estimates of total terrestrial water storage, including groundwater, by detecting subtle changes in Earth’s gravity field. Studies such as Halipu et al. [4] have successfully combined GRACE data with land cover analysis to assess groundwater depletion trends in Egypt, offering a framework for similar applications in other arid zones [4].

Recent advances have improved resolution and field-level sensitivity in soil moisture monitoring. Zhai et al. [20] demonstrated that combining the Normalized Difference Vegetation Index (NDVI) with texture metrics derived from gray-level co-occurrence matrices (GLCM) using UAV imagery significantly enhanced the accuracy of yield and water-fertilizer use efficiency assessments in winter wheat fields [20]. Their approach revealed substantial intra-field variability and underscored the value of integrating spectral and spatial features. Similarly, Liu et al. [21] applied machine learning techniques to downscale satellite soil moisture data, achieving more localized and actionable insights—particularly in fragmented or data-scarce agricultural landscapes [21].

3.2. Evapotranspiration and Water Use Analysis

Estimating actual evapotranspiration (ET) is essential for assessing agricultural water consumption and irrigation performance. Remote sensing provides critical inputs—such as land surface temperature (LST), albedo, and vegetation indices—that feed into ET models based on surface energy balance principles. The Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC) are widely used RS-based models that have demonstrated accuracy in multiple agroecological zones [1,2].

These traditional RS-based ET models are now increasingly supported by cloud-based platforms such as Google Earth Engine (GEE), which facilitate large-scale and long-term ET analysis. Kazemi Garajeh et al. [22] utilized GEE to investigate climate-related evapotranspiration trends across Central Asia and found strong correlations between increasing ETa and water resource vulnerability under warming conditions [22]. Also, Liu et al. [10] showed that combining RS data with machine learning models improved the accuracy of ET predictions and gave reliable alternatives in places where there was not much data by modeling the complicated nonlinear interactions between vegetation, temperature, and land cover [10].

Vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are especially useful for monitoring crop growth stages and canopy development. These indices serve as proxies for plant water uptake and stress levels, and they can be used to estimate spatial variability in ET and irrigation demand. In semi-arid Morocco, Kharrou et al. [12] used RS data to compute ET at the field scale and compare it with crop water requirements, revealing large disparities in water use efficiency across farms [12]. Similarly, Emam [7] applied RS-based ET estimates to analyze the water balance in the Razan-Ghahavand Basin in Iran, demonstrating the effects of land use change on recharge and depletion dynamics [7].

3.3. Monitoring Vegetation, Land Use, and Agricultural Dynamics

Land cover and vegetation mapping through RS has enabled more precise tracking of agricultural expansion, cropping patterns, and seasonal dynamics. High-resolution sensors such as Sentinel-2 and PlanetScope provide near-daily observations that are invaluable for detecting planting and harvest dates, crop rotation, and fallow periods. These applications are essential for water accounting and for enforcing land use regulations where over-irrigation or illegal abstraction is suspected.

To overcome cloud interference and improve temporal continuity, Sigopi et al. [23] reviewed dual-sensor approaches that combine Sentinel-1 SAR and Sentinel-2 optical imagery for more accurate and frequent surface water detection. Their research in sub-Saharan Africa showed that these methods are better at keeping track of reservoir dynamics, finding illegal withdrawals, and dealing with seasonal changes [23]. In addition, Chen et al. [14] applied similar techniques in arid North African basins, reinforcing the relevance of integrated optical-radar strategies in climate-vulnerable, water-stressed environments [14]

For example, in Mediterranean regions, Hunink et al. [9] used RS time-series data to differentiate between irrigated and rainfed crops and to assess water productivity at the pixel level [9]. Similarly, Chen et al. [24] used long-term RS data to examine inland lake shrinkage and its relationship with glacial melt and precipitation variability in the arid region of Xinjiang, China [24].

3.4. Error Metrics and Model Evaluation

Reliable evaluation of model performance is fundamental to ensuring the accuracy, reproducibility, and benchmarking consistency of Remote Sensing (RS), Geographic Information System (GIS), and Machine Learning (ML) applications in water resources management. The most frequently used performance indicators include both regression- and classification-based metrics, depending on whether the modeled variable is continuous or categorical.

For continuous predictions—such as evapotranspiration, groundwater depth, or soil moisture—error statistics like the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) are widely applied. These metrics quantify the magnitude of prediction error, average deviation from observed values, and the proportion of explained variance, respectively. Their joint use allows for comprehensive assessment of both bias and dispersion in model outputs [25,26].

$$\mathrm{RMSE}=\sqrt{{\displaystyle \frac{1}{n}}\sum _{i=1}^n{\left(y_i-\widehat{y_i}\right)}^2}$$

(1)

$$\mathrm{MAE}={\displaystyle \frac{1}{n}}\sum _{i=1}^n\left|y_i-\widehat{y_i}\right|$$

(2)

$${\mathrm{R}}^2=1-{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_i-\widehat{y_i}\right)}^2}{{\sum }_{i=1}^n{\left(y_i-\overline{y}\right)}^2}}$$

(3)

In RS-based water applications, RMSE and R² are particularly important for evaluating evapotranspiration, precipitation, and soil moisture retrievals, where algorithm outputs must be benchmarked against ground-based observations or flux tower data [27]. Additional indicators such as the Kappa coefficient, Mean Bias Error (MBE), and Nash–Sutcliffe Efficiency (NSE) are also used to assess spatial congruence and predictive robustness. Incorporating these metrics ensures transparent model comparison and enhances reproducibility across RS–GIS–ML workflows in arid-region water resource studies.

3.5. Challenges and Future Opportunities

Even though RS has several positive aspects, there are some problems that make it difficult to use in more situations. Some of these problems are that it is hard to get to high-resolution commercial datasets, some areas have a lot of cloud cover, and models need ground truth data to be checked and calibrated. Additionally, while RS provides excellent surface observations, it has a limited ability to directly measure subsurface variables such as aquifer storage or percolation.

Emerging technologies offer promising solutions. Unmanned aerial vehicles (UAVs) can provide ultra-high-resolution imagery for plot-level analysis and validation. Hyperspectral sensors enable the detection of more subtle variations in plant physiology and soil properties. CubeSats and commercial constellations such as Planet or Satellogic now offer near-daily global coverage, enhancing temporal resolution for dynamic water use monitoring. Recent advances in evapotranspiration estimation have been driven by the integration of satellite observations and open-access modeling frameworks. Early efforts by Allen et al. [28], and Bastiaanssen et al. [29], refined energy-balance-based models such as SEBAL and METRIC to improve the accuracy of water productivity and irrigation management assessments. Subsequent developments expanded operational monitoring through global and regional ET datasets, including Zwart et al. [30], who developed FAO’s open-access WaPOR platform for Africa and the Near East, Senay et al. [31], who introduced the SSEBop model for large-scale ET and water-use mapping, and Melton et al. [32], who led the OpenET initiative integrating multiple models and cloud-based processing for improved transparency and accessibility. Collectively, these contributions demonstrate the evolution of satellite-based ET mapping as a fundamental tool for quantifying water consumption and productivity in arid agricultural systems [28, 29, 30, 31, 32].

Moreover, the integration of RS with machine learning algorithms is improving classification accuracy, anomaly detection, and prediction of future water demand. As cloud platforms such as Google Earth Engine and Copernicus DIAS (Data and Information Access Services) become more accessible, the processing of large-scale RS datasets is becoming faster and more scalable [10].

Remote sensing is thus a foundational tool for modern water resource monitoring, providing the temporal continuity and spatial breadth necessary to understand water dynamics in rapidly changing agricultural landscapes. When complemented with GIS and ML—as explored in subsequent sections—it forms the analytical backbone of sustainable water governance in arid zones.

4. GIS in Hydrological and Agricultural Decision-Making

Geographic Information Systems (GIS) have become central to the analysis and management of environmental resources by enabling the integration, visualization, and modeling of diverse datasets. In arid and semi-arid regions, where water resources are limited and spatial variability in land use, topography, and hydrology is high, this technology provides critical capabilities for synthesizing data from remote sensing, field measurements, and hydrological models. It facilitates the production of maps, simulations, and decision-support tools that are essential for both operational and strategic water resource management [6,8].

4.1. Spatial Data Integration and Hydrological Modeling

One of GIS’s most powerful functions is its ability to integrate geospatial data from heterogeneous sources to produce thematic maps and spatial analyses. These include datasets on digital elevation models (DEMs), land use and land cover (LULC), soil types, drainage networks, aquifer extents, and infrastructure. The synthesis of these variables enables multi-criteria modeling of surface runoff, infiltration, recharge zones, and groundwater potential [33].

Marshall et al. [34] compared ten major hydrological models integrated with artificial intelligence (AI) and machine learning (ML) techniques, highlighting improvements in spatial calibration, runoff prediction, and streamflow simulation [34]. Their findings show that hybrid GIS-ML frameworks outperform conventional hydrological models, especially in data-scarce regions, by enabling automated feature selection, enhancing model transferability, and reducing calibration bias.

GIS-based hydrological models such as SWAT (Soil and Water Assessment Tool), MIKE SHE, and HEC-HMS rely heavily on spatial inputs derived from remote sensing and terrain data. For example, DEMs are used to delineate watershed boundaries, extract stream networks, and compute slope and flow direction, which are essential for modeling runoff and erosion. The accuracy of such outputs is highly sensitive to the spatial resolution of input datasets. Rabie et al. [11] demonstrated that stream gauge placement and DEM resolution significantly affect the reliability of flood hazard maps in the Illinois River, a finding that holds particular relevance for regions with sparse in situ monitoring [11].

Mahmood et al. [35] applied a GIS-based framework integrating the Normalized Difference Water Index (NDWI), support vector machine (SVM) classification, and remote sensing to monitor drought and surface water changes in large Iraqi lakes. This method enabled spatial tracking of declining water coverage over time and demonstrated how integrated GIS-RS tools can support early warning systems and regional water allocation strategies in arid environments [35].

In addition, GIS facilitates the integration of time-series data for the monitoring of temporal changes in hydrological conditions. Changes in groundwater depth, surface water storage, or land cover can be mapped over time to detect trends and inform adaptive responses.

4.2. GIS for Agricultural Zoning and Irrigation Planning

In agricultural applications, GIS is used extensively for crop suitability mapping, irrigation system design, land use zoning, and resource optimization. By combining satellite-derived indicators such as NDVI or SAVI with soil and climate data, GIS supports land classification based on agroecological potential and water availability [2]. This allows planners to identify zones of high water productivity and areas at risk of over-extraction or degradation.

Kharrou et al. [12] used GIS in conjunction with remote sensing and a soil water balance model to assess irrigation performance across Moroccan farms. The results revealed large spatial disparities in irrigation efficiency, allowing water managers to target interventions more precisely [12]. Similarly, Belmonte and Gonzalez [36] demonstrated how GIS-based advisory systems can deliver location-specific irrigation recommendations by combining evapotranspiration estimates with crop coefficients and weather forecasts [36]. Furthermore, Bavishi and Shekhar [37] presented a comprehensive review of GIS applications in watershed planning and agricultural zoning. Their analysis covered hydrological modeling, DEM-based terrain classification, stream network analysis, and spatial zoning for irrigation suitability. The study emphasizes the role of GIS as a foundational tool in delineating micro-watersheds and optimizing land and water management interventions [37].

GIS can also support the design and evaluation of water-saving technologies such as drip irrigation, lined canals, and groundwater recharge structures. Spatial analysis enables the identification of optimal locations for intervention based on slope, proximity to water sources, land tenure, and other criteria.

4.3. Decision Support and Policy Integration

GIS is essential for participatory water governance, stakeholder engagement, and policy design in addition to technical modeling. Stakeholders can better understand the intricate relationships between land use, water flows, and socioeconomic drivers through the visualization of spatial data. This is especially valuable in resource-constrained regions where competing demands among agriculture, domestic use, and ecosystems must be reconciled.

Hassan et al. [38] employed a GIS-based multi-criteria decision-making (MCDM) approach using the Analytical Hierarchy Process (AHP) to identify suitable rainwater harvesting sites in arid Iraq. The model supported sustainable water capture and soil conservation by enabling the spatial prioritization of catchment areas by integrating criteria like rainfall, land use, slope, and soil texture [38].

Emam [7], for example, used GIS to model the impact of land use changes on water balance components in Iran’s Razan-Ghahavand Basin, demonstrating the cascading effects of agricultural expansion on groundwater recharge [7]. GIS platforms are also commonly used in environmental impact assessments (EIAs), scenario planning, and disaster risk management—offering spatial tools for decision-making under uncertainty.

Noor et al. [39] conducted a GIS-overlay analysis to select optimal dam sites in Sudan, incorporating hydrological, environmental, and socio-economic criteria. Their study demonstrated the utility of GIS in supporting infrastructure planning in data-poor regions and highlighted the importance of integrating local topographic and watershed characteristics into water management policy [39].

Halder et al. [40] introduced an integrated GIS–SWAT–VIKOR framework to prioritize agricultural lands in India based on water availability, slope, soil suitability, and proximity to infrastructure. The hybrid approach combined hydrological simulation with MCDM analysis, offering a replicable method for sustainable land use planning in regions under agricultural and water stress [40].

Importantly, GIS supports the implementation of regulatory frameworks by enabling zoning of groundwater abstraction areas, delineation of environmentally sensitive zones, and enforcement of land use policies. When combined with RS and ML, GIS can help build early warning systems, optimize monitoring networks, and facilitate compliance with water laws.

Critical Insight: Although Remote Sensing (RS) has revolutionized large-scale water monitoring through improved spatial and temporal coverage, its practical use in arid and semi-arid agriculture remains limited by calibration constraints, cloud interference, and the scarcity of ground-truth data. Most studies rely on moderate-resolution sensors (e.g., MODIS (Moderate Resolution Imaging Spectroradiometer; NASA, Washington, DC, USA), and Landsat (United States Geological Survey (USGS), Reston, VA, USA)), which may not capture field-level irrigation variability. Furthermore, algorithm transferability between regions is often assumed rather than validated, leading to inconsistencies in evapotranspiration (ET) or vegetation index estimates. To enhance operational reliability, future studies should emphasize high-resolution fusion (Sentinel-2/PlanetScope) and integrate physically based models with RS-derived products for improved hydrological realism.

5. Machine Learning in Water Resources Management

Machine learning (ML) has emerged as a transformative tool in the field of water resources and agricultural monitoring, particularly in data-scarce and climate-stressed regions. ML algorithms excel at discovering patterns and relationships within large, complex datasets, often outperforming traditional statistical models in predictive accuracy and classification efficiency [10]. In arid and semi-arid areas—where water management must contend with high variability, limited monitoring infrastructure, and urgent needs for optimization—ML enables smarter, faster, and more adaptable solutions.

5.1. Applications in Hydrology and Agriculture

Machine learning techniques are increasingly applied in hydrological modeling tasks such as predicting groundwater levels, classifying irrigated vs. non-irrigated lands, forecasting drought events, and estimating evapotranspiration [2,9]. Commonly used algorithms include Artificial Neural Networks (ANNs), Random Forests (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBMs). These models can handle nonlinear relationships and high-dimensional input data, such as combinations of satellite imagery, meteorological records, topographic features, and soil characteristics. Furthermore, Willard et al. [41] conducted a comprehensive review of ML and deep learning models for hydrological prediction in ungauged basins, emphasizing the performance of recurrent neural networks (RNNs), long short-term memory (LSTM), and attention-based models in forecasting streamflow and runoff. These models demonstrated improved accuracy over traditional physics-based simulations, particularly in data-scarce catchments with limited or intermittent observations [41].

In agricultural settings, ML is frequently used to estimate crop water productivity, classify cropping patterns, and detect anomalies in irrigation performance. For example, in Morocco, Kharrou et al. [12] applied machine learning to remote sensing-derived ET data to identify spatial inefficiencies in irrigation systems [12]. Similarly, Liu et al. [10] developed ML models that integrate climatic, spectral, and agronomic data to forecast irrigation requirements, thereby improving water allocation at the regional scale [10]. Additionally, Kumar et al. [42] benchmarked multiple ML algorithms—including CatBoost, XGBoost, Random Forest, and SVR—for rainfall forecasting across diverse agro-climatic zones in India. Their results indicated that ensemble-based models such as XGBoost and CatBoost consistently outperformed single-algorithm approaches for both daily and weekly forecasting horizons. These tools offer considerable potential for optimizing irrigation planning and managing weather-related risk in arid and semi-arid regions [42].

Supervised classification techniques, such as RF and SVM, have been particularly successful in generating high-accuracy land use and crop maps from multispectral and hyperspectral imagery. These maps are critical inputs for water budgeting and yield prediction models. ML models can also be trained to assess water stress conditions based on vegetation indices (e.g., NDVI, EVI), canopy temperature, and soil moisture content, enabling near-real-time monitoring of crop health and irrigation needs.

5.2. Groundwater Use Estimation and Efficiency Analysis

One of the most promising applications of ML in arid zone water management is the estimation of groundwater use and irrigation efficiency. Groundwater abstraction is often unmonitored or underreported, especially in regions with widespread informal or private well use. ML algorithms can infer groundwater consumption indirectly by analyzing remotely sensed data such as crop type, biomass growth, and soil moisture—alongside climate variables like temperature and precipitation [14].

For instance, Chen et al. [14] demonstrated how ET-based models, calibrated using satellite-derived vegetation and soil parameters, can estimate field-scale water consumption and irrigation return flow. When paired with actual crop yield data, these models allow the calculation of water productivity, a critical metric for evaluating irrigation performance [14]. ML can also help detect trends over time and flag zones where groundwater is being used inefficiently or unsustainably. Liu et al. [21] presented a detailed review of ML applications for estimating groundwater levels, water quality parameters, evapotranspiration, and surface runoff. Their synthesis identified Random Forest, Support Vector Regression, and LSTM as dominant models in groundwater prediction tasks. The integration of these models with RS and GIS inputs significantly improved forecasting reliability, especially in regions with limited historical hydrologic records [21].

Furthermore, time-series machine learning models can detect changes in groundwater recharge and withdrawal patterns, making them useful for early warning systems and aquifer management. By integrating socio-economic data such as well density, pumping cost, and crop market value, ML-based models can also support policy development related to pricing, subsidies, or pumping restrictions [43].

5.3. Opportunities and Challenges in Operational Deployment

The operational use of machine learning in water management organizations is still quite small, despite the expanding corpus of research. The interpretability of ML models is a significant obstacle. Complex models like deep neural networks often function as “black boxes”, providing little insight into how predictions are derived. This can hinder trust among stakeholders and limit use in regulatory contexts where transparency is essential [10].

To overcome this, researchers are increasingly adopting explainable AI (XAI) techniques. These include feature importance ranking, partial dependence plots, and surrogate models that approximate the behavior of complex ML algorithms using interpretable logic. Hybrid models that combine ML with physics-based or process-based models are also gaining popularity for their ability to balance accuracy with transparency [43]. Dagher et al. [44] applied self-organizing maps (SOMs) to classify water quality and predict membrane fouling in ultrafiltration systems. This unsupervised machine learning approach provided a dimensionality reduction strategy that enhanced system optimization and maintenance scheduling by spotting significant patterns in the water’s composition. Their research indicates that machine learning (ML) can be applied to the management of water infrastructure in both rural and urban areas [44].

Another limitation is the need for high-quality training data. ML models are only as good as the datasets they are built on, and in many arid regions, consistent and accurate ground truth data are lacking. Transfer learning, data augmentation, and model ensembles are being explored to reduce sensitivity to data limitations and improve generalizability across regions and cropping systems [45].

5.4. Future Prospects for ML in Arid Zone Agriculture

The potential of ML to revolutionize water governance in arid agriculture is significant. Real-time irrigation scheduling systems that use ML to dynamically recommend water volumes based on weather forecasts, crop type, and field conditions are already being piloted in countries like India, Tunisia, and Chile. In order to support proactive planning and allocation, forecasting models are being developed to predict water scarcity events months in advance.

Furthermore, the integration of machine learning (ML) with cloud platforms like Microsoft Planetary Computer, Amazon SageMaker, and Google Earth Engine is lowering computational barriers and increasing accessibility to large-scale analysis. ML is also playing a growing role in climate change adaptation by simulating the impact of different water policies, land use transitions, and temperature scenarios on future water availability and crop yield [15,45].

As more high-resolution satellite data become freely available, and as open-source ML tools continue to improve, the practical application of these models in operational water management is expected to expand. With appropriate investments in capacity building, infrastructure, and policy integration, ML can serve as a key enabler of water resilience and agricultural sustainability in arid regions. Ghobadi and Kang [46] conducted a systematic review categorizing ML applications in water resources into supervised, unsupervised, deep learning, and reinforcement learning domains. They emphasized the growing need for explainable AI (XAI) approaches, particularly for increasing stakeholder trust in automated predictions. Their work also identified key research gaps in adaptive modeling, real-time analytics, and the integration of socio-environmental variables into ML-driven decision systems [46].

Critical Insight: Geographic Information Systems (GIS) and Machine Learning (ML) have both proven indispensable for water resource management, yet each faces notable limitations when applied in isolation. GIS excels at spatial visualization and overlay analysis but often depends on subjective weighting within multi-criteria decision-making (MCDM) frameworks and static datasets that rarely capture the temporal variability of water processes. Machine Learning, on the other hand, offers strong predictive capability for groundwater, drought, and evapotranspiration modeling; however, its adoption is constrained by data requirements, limited interpretability, and poor transferability across climatic zones. A key challenge lies in bridging these two domains, linking the spatial reasoning power of GIS with the adaptive analytical strength of ML to produce transparent, reproducible, and operational models. Future progress depends on integrating these tools within open, cloud-based environments that automate data ingestion from Remote Sensing (RS) platforms and provide interpretable outputs for decision-makers.

6. Integrated Approaches: Remote Sensing, GIS, and Machine Learning

Machine learning (ML), geographic information systems (GIS), and remote sensing (RS) have important applications in monitoring and managing water resources in arid agricultural systems. However, when combined, they provide a more robust, all-encompassing, and practical framework. Integrated approaches leverage the observational power of RS, the spatial analytical strength of GIS, and the predictive and classification capabilities of ML to enable precise, dynamic, and data-driven decision-making in water-stressed regions [2]. To illustrate the diversity of integrated approaches,

Table 1. Representative studies integrating Remote Sensing (RS), Geographic Information Systems (GIS), and Machine Learning (ML) for water resource management in arid and semi-arid regions.

Author(s), Year	Study Area	Main Objective	Tools / Techniques Used	Key Findings
Saidu et al. [47]	Northern Nigeria	Assess groundwater potential using multi-criteria GIS modeling	GIS (AHP, MCDM), RS (SRTM DEM, Landsat 8)	Integrated RS–GIS approach successfully delineated recharge-favored zones and classified groundwater potential into five distinct classes.
Talpur et al. [48]	Rohri Canal Command Area, Sindh Pakistan	Evaluate irrigation efficiency and crop-water productivity	RS (Sentinel-2 NDVI, ET indices), GIS (spatial overlay), ML (SVR for ET prediction)	RS–GIS integration improved ET and CWP estimates, supporting efficient irrigation management under data-scarce conditions
Singh et al. [49]	Semi-arid India	Map evapotranspiration and crop-water productivity	RS (SETMI model, MODIS & Landsat data), Ground validation (LAS measurements)	Satellite-based SETMI model closely matched in-situ LAS measurements, demonstrating robust ET and CWP mapping accuracy.
Abbasi et al. [50]	Colorado, USA	Large-scale drought and land-degradation monitoring	RS (SMODIS NDVI series, temperature anomalies), GIS (spatial trend analysis)	Detected regional hotspots of agricultural stress and quantified temporal patterns of drought-induced land degradation.
Atalla et al. [51]	Kuwait	Evaluate irrigation sustainability under climate stress	RS (Landsat 8, MODIS), GIS (AHP for groundwater potential), MCDA	GIS–RS integration identified highly vulnerable zones to climate stress and supported adaptive irrigation strategies for water-scarce areas.

summarizes representative studies that combine Remote Sensing (RS), Geographic Information Systems (GIS), and Machine Learning (ML) for water resource management in arid and semi-arid regions.

6.1. Synergies and Complementary Roles

Remote sensing provides spatially and temporally consistent data on key environmental variables—such as land use, vegetation condition, surface temperature, and evapotranspiration—across vast and often inaccessible areas [1]. By combining these observations with auxiliary spatial layers like topography, hydrological boundaries, land tenure, and soil classification, GIS puts these observations in context and enables spatial modeling and visualization [8,33]. ML further enhances this framework by detecting patterns, classifying land use, estimating unobservable parameters (e.g., groundwater use), and forecasting changes based on trends and model training [10,43]. Krishnamoorthy et al. [52] demonstrated the power of hybrid deep learning models by integrating GIS and Transformer neural networks to predict oceanographic hazards. Their findings showed that Transformer-based models outperformed traditional CNN and LSTM architectures in capturing both spatial and temporal variability in environmental datasets. This reinforces the value of multi-domain integration when applying geospatial tools for dynamic environmental systems [52].

This synergistic triad enables systems that can dynamically track water use, flag inefficiencies, predict stress conditions, and propose spatially targeted interventions. For instance, remote sensing can provide NDVI and ET estimates; GIS can relate these to field boundaries and irrigation infrastructure; and ML can identify which fields are underperforming based on historical water productivity benchmarks. In several studies, these technologies were applied both independently (e.g., RS-based ET estimation) and in integrated form (RS–GIS–ML frameworks). This distinction is important, as some researchers focus on single-tool optimization while others emphasize cross-tool synergy.

6.2. Practical Applications in Arid Agriculture

Several case studies illustrate the power of integrated RS-GIS-ML systems. In Morocco, Kharrou et al. [12] used high-resolution satellite imagery to monitor field-level evapotranspiration, integrated it into a GIS-based water balance model, and applied machine learning to assess irrigation performance across heterogeneous landscapes [12]. The results provided actionable insights for water allocation and policy targeting.

In Chile’s semi-arid Petorca Basin, Duran-Llacer et al. [53] combined remote sensing data on vegetation changes and surface water with GIS-based hydrological modeling to analyze the impacts of agribusiness on local water availability. ML algorithms were used to identify spatial correlations between land use change and water stress, highlighting specific areas where regulation or conservation efforts were most urgently needed [53].

Similarly, in Egypt, Halipu et al. [4] merged GRACE satellite gravity data with land use and climate records to assess groundwater depletion. GIS was used to visualize regional trends, and ML models helped differentiate between areas driven by climatic vs. anthropogenic pressures, thus informing both adaptation and mitigation strategies [4].

Furthermore, Kemarau et al. [54] analyzed how Malaysia has implemented integrated RS, GIS, and ML tools for climate-resilient agricultural and water policy planning. Their work documented the use of remote sensing to map flood risk, monitor rainfall variability, and assess land degradation trends, demonstrating how integrated platforms can guide public investment in resource-constrained environments [54]. Sheikh Khozani et al. [55] developed a long short-term memory (LSTM) network-based model to predict the Water Quality Index (WQI) using remote sensing data. Their integrated approach achieved a prediction accuracy of 98.6%, illustrating how machine learning models can significantly improve water quality assessment while reducing monitoring costs in regions with limited infrastructure [55].

These examples show how integrated approaches help close the gap between technical diagnostics and policy action while also improving analytical precision. Climate resilience planning, crop zoning, aquifer regulation, and irrigation scheduling can all benefit from integrated tools that combine quantitative monitoring, spatial intelligence, and predictive analytics.

6.3. System Architecture and Workflow Integration

Operationalizing integrated systems requires effective data flow across platforms and tools. Typically, RS imagery is pre-processed and analyzed for key variables (e.g., vegetation indices, LST, ET), which are then imported into GIS for spatial analysis and overlay with other relevant datasets such as soil maps or administrative boundaries. ML models are developed using training datasets derived from these inputs, along with target variables like groundwater abstraction rates, irrigation demand, or yield performance.

This workflow increasingly relies on cloud-based infrastructures. Platforms like Google Earth Engine allow users to access and process RS datasets at scale. These outputs can be exported to GIS platforms (e.g., QGIS, ArcGIS) for mapping and analysis. ML models are built in environments such as Python (scikit-learn, TensorFlow) or R, and often fed back into GIS platforms for spatial display and decision support [10]. Islam et al. [56] conducted a global review of flood susceptibility and urban resilience frameworks that integrate remote sensing, GIS, and machine learning. They found that hybrid RS-GIS-ML models—especially those using ensemble classification methods like Random Forest and XGBoost—achieve superior accuracy in urban flood prediction, enabling city planners to build more responsive early warning systems and adaptation policies [56].

Interoperability and automation are critical for large-scale, real-time applications. Modular architectures using APIs, common data formats (GeoTIFF, GeoJSON), and open standards facilitate seamless transitions between stages of the analysis chain. These systems are increasingly being embedded into dashboard-style applications for use by planners, farmers, and policymakers.

6.4. Challenges and Opportunities

Integrated systems face several obstacles despite their potential. Regional differences in data availability persist, and smallholder contexts may not be able to afford high-resolution RS data. Complex workflows require technical expertise to manage and interpret. Furthermore, institutional silos and lack of coordination can limit the operational use of integrated tools, especially where water and agricultural sectors are managed by separate agencies [15,16].

Gyang et al. [57] evaluated the effectiveness of real-time flood forecasting systems in the United States that use ML-enhanced remote sensing platforms. Their study highlighted the operational benefits of integrating automated classification tools with near-real-time imagery, while also identifying key challenges such as computational load, model generalization, and the need for greater stakeholder transparency in algorithm-based decision tools [57].

However, these obstacles are being lowered by increasing investments in collaborative platforms, capacity building, and open data. Integrated tools are becoming more widely available thanks to initiatives like OpenET, FAO’s WaPOR, and World Bank-sponsored water accounting programs. With the right enabling environment—including policy support, training, and infrastructure—RS-GIS-ML integration can become a foundation for smart, adaptive water governance.

Critical Insight: The integration of RS, GIS, and ML tools offers unprecedented potential for holistic water-management assessments; nevertheless, such integration is still in its early stages. Many studies combine two tools rather than fully integrating all three, which limits cross-scale synergy and real-time application. A key bottleneck is the absence of standardized data formats and cloud-based workflows capable of handling large multi-source datasets. Moreover, the computational cost of coupling ML algorithms with high-resolution RS data can be prohibitive for local agencies. Future research should prioritize interoperable platforms (e.g., Google Earth Engine, WaPOR, OpenET) and promote modular frameworks that link spatial analytics with machine learning under unified geospatial dashboards.

While integrated RS–GIS–ML frameworks enhance data interoperability and modeling accuracy, their full potential is realized only when analytical outcomes are translated into actionable management and policy strategies. To bridge this gap between technological capability and decision-making, holistic frameworks that connect environmental indicators with socio-economic responses are required.

Among these, the Driving Force–Pressure–State–Impact–Response (DPSIR) model provides a widely recognized structure for linking geospatial analysis to governance and sustainability assessment, serving as an essential policy-integration layer within water resource management systems.

6.5. The DPSIR Framework as a Policy-Integration Model

Sustainable water management in arid and semi-arid regions requires not only advanced technological tools but also conceptual frameworks capable of organizing complex socio-environmental systems. One such framework is the Driving Force–Pressure–State–Impact–Response (DPSIR) model, originally developed by the European Environment Agency [16]. DPSIR helps interpret the causal links between human activities and environmental outcomes [3], making it easier to identify intervention points and evaluate management performance in the context of water resources. Within this review, DPSIR serves as a policy-integration layer connecting geospatial analytics with governance frameworks and facilitating communication among scientists, policymakers, and postgraduate students.

Drivers such as population growth, agricultural expansion, and national food-security policies generate pressures like groundwater abstraction, land-cover change, and salinization [3]. Remote sensing (RS) and GIS are instrumental in mapping these pressures by tracking irrigated-area expansion, evapotranspiration (ET) anomalies, and surface-water decline [2]. The state of water and land resources—such as aquifer levels, vegetation vigor, or soil salinity—can be assessed using RS-derived indicators and GIS overlays [4,7]. Machine-learning (ML) models increasingly support the evaluation of impacts, forecasting spatial patterns of groundwater depletion and agricultural vulnerability [43]. Responses, including irrigation-efficiency programs, land use zoning, or farmer-training initiatives, can then be monitored through geospatial assessment of adoption rates and productivity recovery [6].

The value of DPSIR increases when integrated with analytical modeling and geospatial technologies. RS provides continuous, synoptic observations of both pressure and state indicators [1], GIS enables multi-layer analysis with demographic and infrastructure data [2,7], and ML identifies hidden correlations across variables [10]. For instance, Guo et al. [58] implemented a multi-indicator DPSIR system in China’s Xin’an River Basin, combining entropy weighting, TOPSIS, and grey correlation analysis to assess ecological performance across ten counties [53]. Likewise, Lin et al. [15] coupled DPSIR with a system-dynamics model to evaluate long-term water-sustainability scenarios [15], and Deng et al. [59] applied entropy-weighted fuzzy evaluation to measure ecological-pressure disparities in the Pearl River Delta [54]. These studies illustrate how DPSIR can link hydrological, agricultural, and policy variables into a replicable framework for decision support in arid-zone water governance.

DPSIR can also be combined with emerging tools such as explainable AI to improve transparency in data-driven decision-making. Liu et al. [10] used ML models to simulate crop-water requirements and detect early warning signals of water stress [10], while Elagib et al. [43] highlighted how interpretable AI models improve stakeholder trust in water-policy contexts [43]. Participatory GIS (PGIS) further enhances DPSIR’s communicative role by engaging local communities in mapping and interpreting environmental indicators, thereby aligning responses with local realities [3].

Challenges remain, particularly the lack of standardized indicators, uneven institutional capacity, and weak coordination between environmental monitoring and enforcement [15,16]. Nonetheless, as integrated, open-access RS–GIS–ML platforms become more common [55,56,57,58,59], the operationalization of DPSIR is increasingly feasible. When effectively implemented, DPSIR provides a structured, adaptive, and participatory pathway for managing agricultural water systems under conditions of rapid environmental and socio-economic change.

7. Identified Gaps and Future Directions

Despite the growing body of research demonstrating the value of remote sensing (RS), Geographic Information Systems (GIS), and machine learning (ML) for water management in arid and semi-arid agricultural regions, several key limitations hinder their broader adoption and long-term impact. These gaps span technical, operational, institutional, and socio-political dimensions. Addressing them is essential to translate promising research into sustainable, scalable solutions. To highlight the main research challenges and directions identified in this review,

Table 2. Summary of research gaps and recommended future directions in RS–GIS–ML applications for arid-region water management.

Identified Challenge	Description	Recommended Future Direction	Key References
Limited field validation	Many RS–ML studies lack in situ verification due to data scarcity and logistics.	Develop hybrid calibration datasets using citizen science, IoT sensors, and regional partnerships.	[1,28,41]
Fragmented tool integration	RS, GIS, and ML often applied separately, reducing system-level insight.	Promote unified cloud platforms (GEE, OpenET, WaPOR) enabling real-time integrated analytics.	[46,52,60]
Low interpretability of ML models	Black-box models hinder decision-maker trust.	Incorporate Explainable AI (XAI) methods (e.g., SHAP, LIME) in hydrological ML workflows.	[43,46,56]
Regional bias and data imbalance	Research concentrated in Asia & North Africa; minimal work in Arabian Peninsula and sub-Saharan Africa.	Encourage region-specific datasets and collaboration networks in under-studied arid basins.	[33,41,59]
Temporal inconsistency of RS datasets	Inconsistent time-series reduce trend comparability.	Employ multi-sensor fusion (Sentinel, MODIS, Landsat) with ML temporal harmonization.	[29,31,33]
Lack of policy translation	Technical outputs rarely inform local policy or governance.	Link geospatial ML results to DPSIR-based socio-economic indicators for policy relevance.	[22,43,59]

presents a concise summary of key gaps and recommended future priorities for advancing RS–GIS–ML applications in arid-region water management.

7.1. Data Scarcity and Ground-Based Validation

A foundational challenge in applying geospatial technologies in arid regions is the persistent lack of reliable, high-quality, and long-term ground-based data. While RS offers wide spatial and temporal coverage, it often requires in situ validation to ensure the accuracy of derived variables such as evapotranspiration (ET), soil moisture, or crop classification [2,12]. Ground truth data such as well depths, pumping rates, and crop yields are typically limited in these regions due to the high costs of monitoring infrastructure, fragmented data governance, and low institutional capacity [16]. Moreover, satellite sensors may not effectively capture local heterogeneity, especially in smallholder or mixed cropping systems common across much of North Africa and the Middle East. Future efforts should prioritize the co-development of low-cost, community-based monitoring systems and improved integration between local knowledge and remote sensing technologies [4]. Halipu et al. [4] emphasized that despite advances in RS-based modeling, a major limitation remains the lack of reliable in situ validation datasets, especially in arid and transboundary regions. Their work identified significant discrepancies between modeled evapotranspiration and field-observed values, particularly in heterogeneous soil and land use conditions. This underscores the need for ongoing investment in ground monitoring infrastructure to support remote sensing calibration and modeling accuracy [4].

7.2. Model Interpretability and Decision-Making Constraints

Machine learning models have demonstrated strong predictive performance in many hydrological and agricultural tasks, but their real-world deployment is frequently constrained by issues of interpretability and user trust [43]. Decision-makers—especially within government agencies—require transparency and traceability in model behavior, particularly when models are used to inform high-stakes policies such as groundwater extraction limits or irrigation subsidies. Most advanced ML models, especially deep learning approaches, lack clear explanatory mechanisms for their predictions, reducing confidence among non-technical users [10]. To address this, there is a growing emphasis on explainable AI (XAI) approaches that incorporate feature attribution, model simplification, and visual analytics to help users understand how inputs relate to outputs. In parallel, hybrid models that combine physically based hydrological simulations with ML techniques are emerging as a promising way to balance accuracy with interpretability [15]. Ghobadi and Kang [46] highlighted the growing role of explainable AI (XAI) in improving model transparency and stakeholder trust in ML-driven water management systems. Their systematic review revealed that most ML applications in the water sector still rely on black-box algorithms, which hinders adoption in decision-making settings. The authors advocate for increased use of feature importance ranking, sensitivity analysis, and model simplification tools to enhance interpretability and cross-sectoral communication [46].

7.3. Integration and Interoperability Challenges

RS, GIS, and ML systems often operate within separate platforms and disciplines, creating interoperability barriers that limit their full integration into decision-support workflows. For instance, remote sensing data may be processed using Earth observation platforms (e.g., Google Earth Engine), while GIS analysis occurs in spatial software (e.g., ArcGIS, QGIS), and ML models are developed in coding environments like Python or R. This siloed ecosystem requires high technical capacity and careful data management to ensure compatibility across formats and workflows [6]. Integration is also made more difficult by problems with data standardization, such as disparate spatial resolutions, projections, and naming conventions. Cloud-based platforms and open, modular architectures are required to enable end-to-end analysis pipelines and smooth data exchange. According to Islam et al. [56], the two main technical obstacles to completely integrating remote sensing, GIS, and ML platforms are data harmonization and system interoperability. Inconsistent temporal scales, mismatched spatial resolutions, and closed-source software architectures frequently impede smooth data exchange between modules, according to their analysis of urban flood management systems. The study calls for open standards and flexible platform architectures to improve cross-compatibility in integrated water management workflows [56]. Projects like OpenET and FAO’s WaPOR are pioneering such frameworks, but further investment is needed to scale them across different regions and institutions [10].

7.4. Socio-Institutional and Equity Gaps

While technological advances have expanded the capacity to monitor and model water use, many existing tools pay insufficient attention to the social and institutional dynamics that shape water outcomes. In arid regions, water governance is often fragmented across sectors and administrative scales, and power asymmetries may prevent equitable access to water-saving technologies or information [3]. Examples of marginalized groups that might not have the technical literacy or steady land tenure required to take part in geospatial monitoring systems or reap the benefits of precision agriculture include women, indigenous communities, and smallholders. Furthermore, many water reforms are top-down, which can undermine the legitimacy and participation of local communities and ultimately compromise the efficacy of the policy. Integrating participatory approaches—such as community-based mapping, scenario co-design, and localized decision dashboards—into geospatial systems can increase their relevance and acceptance [16]. Institutional reforms should also support cross-sector coordination, data sharing mandates, and adaptive regulatory frameworks. Deng et al. [59] demonstrated that even highly urbanized and economically advanced regions such as the Pearl River Delta face challenges related to institutional coordination and equitable policy responses. Using a DPSIR-fuzzy evaluation model, the study found that ecological pressures are often unevenly distributed, and resource allocation mechanisms do not always match vulnerability levels. These observations emphasize how crucial it is to incorporate socio-institutional analysis into technical frameworks, especially when water governance involves several different jurisdictions [59].

7.5. Future Research Directions

Moving forward, several priority areas can advance the use of geospatial technologies for sustainable water management in arid agriculture:

• Real-time irrigation advisory systems: These systems can integrate RS-based vegetation indices, soil moisture data, and weather forecasts to deliver plot-level irrigation recommendations using ML algorithms. Early efforts in regions like Morocco and India show promise in reducing water waste and increasing yields [12,45];
• Explainable and portable ML models: Future research should focus on developing transparent ML models that can generalize across regions with different environmental and agricultural conditions. Techniques such as transfer learning and spatial cross-validation can improve model transferability [10];
• Coupled socio-hydrological models: Integrating behavioral, institutional, and ecological dimensions into water models can better capture feedback loops and policy scenarios. This includes incorporating factors such as water pricing, regulatory enforcement, and farmer decision-making into predictive frameworks [15];
• Equity-focused and participatory innovation: New systems should explicitly address who benefits from geospatial technologies. Tools should be co-developed with local users, include interfaces in local languages, and be sensitive to cultural and gender dimensions;
• Cloud-based platforms and open data infrastructure: Expanding access to open-source geospatial platforms (e.g., OpenET, Google Earth Engine) and promoting FAIR (Findable, Accessible, Interoperable, Reusable) data principles can improve scalability and replication of successful applications.

To achieve these goals, interdisciplinary collaboration is essential. Collaborations among earth scientists, engineers, social scientists, and local communities can guarantee that technological advancements are in line with policy objectives and realities on the ground. The combined application of RS, GIS, and ML can significantly contribute to sustainable water and agricultural futures in arid regions with the right kind of support and inclusive governance. Kazemi Garajeh et al. [22] measured evapotranspiration trends in Central Asia under climate variability using long-term satellite records. Their results indicated the need for more study on climate-adaptive water modeling techniques since they demonstrated a significant correlation between increases in ETa and decreases in surface water availability. Their approach supports the development of integrated predictive tools that incorporate RS, GEE, and ML analytics to forecast water stress and inform resilience planning under future scenarios [22].

Critical Insight: The reviewed literature collectively underscores the rapid methodological progress in RS–GIS–ML integration yet reveals persistent barriers to adoption in real-world water governance. Technical sophistication has outpaced policy translation, resulting in a disconnect between analytical innovation and field implementation. Furthermore, regional inequality in data availability leaves large portions of arid and semi-arid territories under-represented. Addressing these challenges requires coordinated policy frameworks, capacity building in developing regions, and open-access data initiatives that democratize advanced geospatial tools for sustainable water management.

8. Discussion and Conclusions

Water resource management in arid and semi-arid agricultural regions faces growing challenges due to increasing demand, climate variability, and environmental degradation. Traditional management approaches, which often rely on fragmented datasets and static planning frameworks, are proving inadequate to address the dynamic changes in land use, groundwater extraction, and irrigation practices. In this context, the integration of remote sensing (RS), Geographic Information Systems (GIS), and machine learning (ML) offers a powerful framework for achieving more responsive, spatially targeted, and data-driven water governance.

This review demonstrated how these three technologies complement one another. Remote sensing enables continuous monitoring of biophysical variables such as evapotranspiration, vegetation condition, land cover change, and surface water extent; key indicators of agricultural water use and ecosystem stress. GIS adds spatial intelligence, allowing for the combination and analysis of environmental and socio-economic datasets, spatial modeling of water balance components, and visualization for decision-making. Machine learning contributes predictive power, enabling the estimation of unmonitored groundwater use, optimization of irrigation schedules, and detection of emerging patterns in water productivity. Together, these technologies form a cohesive system capable of supporting proactive and evidence-based management of water resources.

The review was organized around four main objectives, each representing a frontier in the application of geospatial and AI tools to arid-zone water management:

• Evaluating water productivity and irrigation efficiency: Analysis of water use patterns revealed substantial variation across arid agricultural systems, emphasizing the need for context-specific interventions guided by high-resolution geospatial data and modeling.
• Monitoring agricultural practices and land use change: Multi-temporal satellite imagery and geospatial analytics were shown to effectively track crop dynamics, expansion trends, and land suitability shifts—offering early warning for unsustainable resource use.
• Assessing groundwater usage efficiency: The integration of ML and RS methods demonstrated strong potential for quantifying unmonitored groundwater consumption and identifying low-productivity areas, providing valuable insights for both local and basin-scale management.
• Developing sustainable water governance strategies: The DPSIR (Drivers–Pressures–State–Impact–Response) framework emerged as an effective approach for linking environmental monitoring with policy design, translating technical outputs into actionable management strategies.

Despite notable progress, several critical gaps persist. Data scarcity and validation limitations remain major obstacles, especially in resource-limited regions. Ensuring model transparency, interpretability, and institutional coordination is crucial for building trust and supporting adoption in policy contexts. Socioeconomic and equity dimensions are often underrepresented, highlighting the need for participatory approaches and interdisciplinary collaboration.

Ultimately, achieving water sustainability in arid agricultural systems will require the integration of complementary technologies, robust data infrastructure, institutional alignment, and active stakeholder participation. Geospatial and AI-based methods must be embedded within governance frameworks that are adaptive, inclusive, and informed by both science and local realities. With continued investment in open data, technical capacity, and policy innovation, the synergy of RS, GIS, and ML can help create more efficient, equitable, and climate-resilient water management systems across arid and semi-arid regions.