The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km². Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime example is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer’s accuracies ranging from 83.7% to 94.8% and user’s accuracies ranging from 90.2% to 97.9%. The coefficient of determination ($R^2$) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km${}^2$ in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km${}^2$. While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions.

The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields, and their varying levels of water exploitation through time, than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model, was developed to provide information on both irrigated crop-water use and groundwater abstraction rates. Results from the local-scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo inputs, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables, and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center-pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center-pivots in Al Kharj that was used for an initial evaluation, and a much larger agricultural region in Al Jawf province comprising more than 5,000 individual fields across an area exceeding 2,500 km². Total groundwater abstraction for the year 2015 in Al Jawf were estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data-model fusion approach facilitates the compilation of national-scale groundwater abstractions, while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

Object-based analysis is widely used for extracting information from satellite data using machine learning, offering reduced sensitivity to fine-scale variability, noise, and computational cost compared to pixel-based methods. However, segmentation algorithms for center-pivot fields often treat fields as single units, neglecting that a field can be subdivided into different sections caused by varied management practices such as differing planting and harvesting dates, crop types, and rotations. This variability is particularly prevalent in hot arid regions like Saudi Arabia, where precise water and crop management are crucial for sustaining agricultural productivity. However, such subfield division reduces the accuracy of objectbased agroinformatic insights and the effectiveness of large-scale analyses. A machine learning-based approach combining Kmeans clustering and cosine similarity was developed to quantify subfield divisions using temporal features derived from Sentinel2 Normalized Difference Vegetation Index (NDVI) time series. The performance of Discrete Wavelet Transformation (DWT) and Savitzky-Golay filtering was compared for processing the NDVI time series. When evaluated against a reference dataset, the approach achieved a maximum accuracy of 93.38% with DWT level 1 decomposition using the ”haar” wavelet function. These parameters were applied to map the nationwide centerpivot subfield division dynamics across Saudi Arabia from 2019 to 2023. Results revealed that approximately 20% of center-pivot fields exhibited subfield divisions, ranging from 5,740 fields (2,083 km2) in 2020 to 7,342 fields (2,770 km2) in 2023. Larger fields were more prone to subfield divisions, with a median acreage of 40 ha compared to 20 ha for undivided fields. Dominant management strategies included half-to-half and 5:3:2 divisions. This approach enhances objected-based agroinformatics products and facilitates more accurate food security assessments.

Monitoring agricultural water use is essential to ensure water security, especially in regions facing water scarcity. Satellite-acquired multi-spectral images of the Earth’s surface provide crucial data to enable frequent estimations of crop water use. In large data-scarce regions, these estimations represent a key source of information for water management. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has made it feasible, accessible, and cost-effective to automate crop water use monitoring pipelines. Here, we demonstrate the potential benefits of a cloud-based crop water use estimation and monitoring framework by estimating a decade's worth of agricultural water use over Saudi Arabia. In Saudi Arabia, large-scale agricultural activities account for the majority (>80%) of water use, water which is sourced primarily from non-renewable groundwater resources from the Arabian Shelf. Saudi Arabia’s large land area (> 2 million km2) and the long study period (+10 years) forms the basis of a case-study for our cloud-based model. Previous mapping efforts provided annual maps of individual field boundary delineations at, identifying more than 30,000 fields covering a total of more than 10,000 km2 of croplands that are distributed across several large-scale agricultural clusters within the Kingdom. As a preprocessing step, we developed an approach to generate large-scale delineations of irrigated agricultural regions over arid areas. This approach helped reduce processing efforts for field delineations, and at the same time reducing the water use estimation computations. Our GEE cloud-based model implements a two-source energy balance model (TSEB) and automatically incorporates all available Landsat collection 2 surface reflectance and surface temperature products from Landsat 7, 8, and 9, along with climate reanalysis data from the ECMWF ERA5-Land hourly product. The model can be readily applied elsewhere by defining just the date range and study geometry, while allowing the flexibility for more advanced users to control parameters within the TSEB model. Total crop water use (evapotranspiration term only; not accounting for irrigation efficiency) was estimated at between 7 and 12 BCM per year of study, with the highest use in 2016 and the lowest in 2020. Both Riyadh and al Jawf administrative regions collectively shared more than half of the total study cropland area, and a similar contribution of water use. This study represents the convergence of a number of efforts towards developing operational crop water use monitoring, while motivating further applications in other regions and providing a rich dataset for further food and water security related studies.

Driven in large part by policy initiatives designed to increase food security and realized via the construction of thousands of center-pivot irrigation fields since the 1970s, agriculture development in Saudi Arabia has undergone tremendous changes. However, little is known about the accurate number, acreage, and the changing dynamics of the fields. To bridge the knowledge gap between the political drivers and in-field response, we leveraged a hybrid machine learning framework by implementing Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering in a stepwise manner to delineate the center-pivot fields on a national scale in Saudi Arabia using historical Landsat imagery since 1990. The framework achieved producer's and user's accuracies larger than 83.7% and 90.2%, respectively, when assessed against 28,000 manually delineated fields collected from different regions and periods. We explored multi-decadal dynamics of the agricultural development in Saudi Arabia by quantifying the number, acreage, and size distribution of center-pivot fields, along with the first and last detection year of the fields since 1990. The agricultural development in Saudi Arabia experienced four stages, including an initialization stage before 1990, a contraction stage from 1990 to 2010, an expansion stage from 2010 to 2016, and an ongoing contraction stage since 2016. Most of the fields predated 1990, representing over 8,800 km2 in that year, as a result of the policy initiatives to stimulate wheat production, promoting Saudi Arabia as the sixth largest exporter of wheat in the 1980s. A decreasing trend was observed from 1990 to 2010, with an average of 8,011 km2 of fields detected during those two decades, which was a response to the policy initiative implemented to phase-out wheat after 1990. As a consequence of planting fodder crops to promote the dairy industry, the number and extent of fields increased rapidly from 2010 to 2015 and reached its peak in 2016, with 33,961 fields representing 9,400 km2. Agricultural extent has seen a continuous decline since 2016 to a level lower than 1990 values in 2020. This decline has been related to sustainable policy initiatives implemented for the Saudi Vision 2030. There is some evidence of an uptick in 2021 — also observed in an ongoing analysis for 2022 — which might be in response to global influences, such as the COVID-19 pandemic and the more recent conflict in the Ukraine, which has disrupted the international supply of agricultural products. The results provide a historical account of agricultural activity throughout the Kingdom and provide a basis for informed decision-making on sustainable irrigation and agricultural practices, helping to better protect and manage the nation's threatened groundwater resources, and providing insights into the resilience and elasticity of the Saudi Arabian food system to global perturbations.