Irrigation Demand Research Articles

Remote Sensing of Residential Landscape Irrigation in Weber County, Utah: Implications for Water Conservation, Image Analysis, and Drone Applications

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning.

A new optimized procedure for circular WasteWater sustainability: Coastal cities supporting agricultural rural communities

This proposal introduces a novel methodology that addresses the increasing irrigation demands driven by climate change and urban growth. Traditionally water-scarce areas are now facing severe water deficits, while wastewater volumes from treatment plants, often discharged into the sea, contribute to pollution. The proposed hybrid system strategy innovatively reallocates 33 hm3 of water annually to agricultural communities, employing a zero-discharge approach to prevent marine pollution. Evaluated from energetic, environmental, and social perspectives, this methodology shows a remarkable cost-benefit ratio exceeding 12, showing its feasibility. It features technical indicators for optimizing water distribution and regulatory components, applied effectively to 28,424 ha of farmland. This strategy meets 24.1 % of the irrigation needs in these regions while safeguarding coastal areas from degradation. Crucially, it integrates 11.3 GWh of renewable energy annually, underscoring its sustainability and enhancing its replicability for other water-deficient regions.

Water saving based on moisture observations: scheduling drip irrigation regimes for tomatoes under greenhouse conditions in Tajikistan

This study aimed to examine the efficiency of different drip irrigation regimes in spring film greenhouses for early tomato harvesting. The field (small-plot) experiment included designing and testing the watering technology suitable for rural Tajikistan. Irrigation, soil moisture, and tomato growth were followed for several seasons under four irrigation pilots. The experiments showed that the most effective drip irrigation regime for Elpida hybrid tomato was carrying it out while regulating soil moisture within 75-85%, with the irrigation demand of 4,978 m3/ha and irrigation norm of 99.0 m3/ha, also contributing to better water efficiency. The volume of irrigation water for the production of one unit of tomato crop in the second experiment was 5.73 m3/ha, i.e. 14.18% less than in the control plot. The study showed that the tomato evapotranspiration coefficient tends to increase as the threshold of soil mois-ture before and after irrigation grows. The maximum net yield amounted to 1,342 thou. Somoni/ha – 1.9 times or 52.7% higher compared to the control plot. The research findings can guide individual farmers and production facilities, as well as the overall development of agrarian economies like Tajikistan.

Crop Coefficients and Irrigation Demand in Response to Climate-Change-Induced Alterations in Phenology and Growing Season of Vegetable Crops

This study investigates the effects of climate change on the irrigation demand of vegetable crops caused by alteration of climate parameters affecting evapotranspiration (ET), plant development, and growing periods in Central Europe. Utilizing a model framework comprising two varying climate scenarios (RCP 2.6 and RCP 8.5) and two regional climate models (COSMO C-CLM and WETTREG 2013), we calculate the daily crop water balance (CWBc) as a measure for irrigation demand based on reference ET and the temperature-driven duration of crop coefficients until 2100. Our findings for onion show that rising temperatures may shorten cultivation periods by 5 to 17 days; however, the irrigation demand may increase by 5 to 71 mm due to higher ET. By reaching the base temperatures for onion growth earlier in the year, cultivation start can be advanced by up to 30 days. Greater utilization of winter soil moisture reduces the irrigation demand by up to 21 mm, though earlier cultivation is restricted by frost risks. The cultivation of thermophilic crops, however, cannot be advanced to the same extent, as shown for bush beans, and plants will transpire more strongly due to longer dry periods simulated for summer. The results underscore the need for adaptive crop and water management strategies to counteract the simulated changes in phenology and irrigation demand of vegetable crops. Therefore, special consideration must be given to the regional-specific and model- and scenario-dependent simulation results.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Analysis of diversion ratio and study of sediment transport in the first canal station of an irrigation area based on Mike21 hydrodynamic model

Abstract Based on MIKE21 hydrodynamic model, the change of diversion ratio and sediment transport at the first canal station in an irrigation area are analyzed. Two-dimensional numerical simulation is carried out by using MIKE21 FM hydrodynamic module, and the split ratio is studied. In the aspect of sediment transport, the transport path and deposition distribution of different sediment particle sizes are simulated by simulating sediment transport. The results show that under the lowest daily average water level, the diversion canal flow can meet the irrigation demand; The particle size of sediment has a great influence on the deposition distribution and thickness of the approach channel at the first station of the canal, and then affects the water diversion capacity of the pumping station.

Projecting irrigation demand under IPCC climate change scenarios using WEAP modeling in the Rechna Doab, Pakistan

Pakistan is currently facing significant water scarcity issues, intensified by climate change. The main water source, the transboundary Indus River system, faces challenges such as water management, limited data availability, and inadequate management, leading to a gap between water demand and supply across various sectors. Agriculture, which consumes over 95 % of the country’s water resources, contributes nearly 25 % to the GDP, but is heavily dependent on irrigation due to limited rainfall. With rainfall meeting only 15 % of crop water requirements, groundwater plays a critical role, covering 40–60 % of irrigation needs. This study focuses on the Rechna Doab in the Indus Basin Irrigation System (IBIS) using the WEAP (Water Evaluation and Planning) model to assess the supply-demand gap under IPCC's climate change scenarios from Assessment Report Six (AR6). The main findings indicate: (1) Under SSP 8.5, unmet demand in the Upper Chenab Canal and other regions will increase by 33–47 % by mid-century; (2) demand site coverage will decline significantly, especially in Lower Gugera and Jhang branches; (3) groundwater dependency will increase substantially in response to the growing supply-demand gap. This work contributes to improving water management in Rechna Doab by providing a clear framework for adapting water resources to climate change using WEAP projections under various IPCC scenarios.

Pilot study on liquid desiccant systems in greenhouse for water saving and climatecontrol

In regions with hot and arid climates, meeting the growing demands for irrigation and food production is crucial. Greenhouse farming offers a promising solution, significantly enhancing agricultural water efficiency by producing high-value crops with reduced freshwater consumption compared to traditional methods. However, the reliance on freshwater-dependent evaporative cooling systems in greenhouses poses a significant challenge, consuming up to 80 % of total freshwater usage. This challenge underscores the urgent need for innovative solutions to reduce freshwater use while maintaining optimal greenhouse conditions.This study proposes integrating a desiccant system with conventional evaporative cooling to improve humidity control and reduce freshwater consumption. This innovative approach enables greenhouse establishment in humid coastal areas previously deemed unsuitable, offering a sustainable alternative to traditional cooling methods. The proposed system includes liquid desiccant, desiccant pads, a chiller, and a fan, aiming to reduce or eliminate water use during nighttime cooling cycles. Additionally, the system has the potential to harvest water from ambient air, further reducing overall water consumption. Through this case study, we address the critical issue of freshwater overuse in greenhouse agriculture, presenting a solution that could revolutionize water management practices in arid and semi-arid regions.

Future Agricultural Water Availability in Mediterranean Countries under Climate Change: A Systematic Review

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices.

Enhancing Efficiency of the Minipe Left Bank Canal Irrigation System: A Strategic Approach

Minipe left bank canal faces a head-tail water imbalance due to insufficient water availability and inefficiency in water distribution. To address this issue, Minipe anicut is raised by 3.5m. Though ample water could be released, irrigation water would not be dispersed adequately along the canal due to mis-usage and mismanagement. The study aims to establish an operational pattern for the canal to deliver adequate water to the entire command area during both the Yala and Maha seasons. Simulation models were used to model the canal's operational pattern, including inflows and irrigation demands. Catchment areas, which bring water to the Minipe Left Bank canal, were delineated using ArcGIS software, and inflows were estimated using HEC-HMS software. Crop water requirements were estimated using CROPWAT program. A system simulation model developed usingWEAP software was used to simulate irrigation system operation. Nine management scenarios were evaluated, including introducing new cropping patterns, shifting sowing dates, increasing Minipe anicut discharges, and reducing the command area. The study found that introducing a 70% paddy crop pattern, advancing the sowing date by two weeks, and raising the Minipe anicut discharge by 15% significantly reduce unmet demands. Implementing these improvements could result in a 75% reduction in unmet demand.

Analysis and forecast of crop water demand in irrigation districts across the eastern part of the Ebro river basin (Catalonia, Spain): estimation of evapotranspiration through copernicus-based inputs

AbstractThe agricultural sector is currently facing the uncertainty that accompanies climate change in terms of the availability of water resources, as well as the need to balance the water demand for agricultural irrigation with other uses in river basins. In Spain, irrigation districts (IDs) play a very important role in the management of water resources. The efficiency of ID water management involves finding an equilibrium between supply and demand. It is in relation to the latter where the uncertainty is greatest, because until now no tools have been available to characterize water demands with sufficient precision throughout irrigation campaigns. ID managers need precise information and the development of tools to support decision making in planning and water management. Therefore, this study aims to identify, compare and analyse the differences between the demands, allocations and consumptions of water for irrigation in different IDs of the eastern part of the Ebro basin during six consecutive growing seasons. In addition, projections of water demands up to 2100 are conducted using a dataset of six global climate models under different climate scenarios. Novel advances in remote sensing for evapotranspiration approaches using Copernicus-based inputs were used in this study. Large variabilities in water demands among IDs and in the adjustments between demands and allocations were observed, suggesting there is still much room for the improvement of water management. All climate projections have a very clear pattern indicating an upward trend in water demands until the end of the century.

Long-term sustainability of the water-agriculture-energy nexus in Brazil's MATOPIBA region: A case study using system dynamics.

The global demand for agricultural commodities has driven extensive land conversion to agriculture in Brazil, especially in the MATOPIBA region. This area encompasses the Rio Grande Basin, a major tributary of the São Francisco Basin that is known for expanding intensive irrigated agriculture and hydropower generation. However, recent data reveal declining precipitation and aquifer recharge, potentially exacerbating ongoing water and land conflicts. This study investigates the long-term sustainability of agricultural expansion amid the worsening water scarcity using a system dynamics model. Findings suggest that rising costs and decreasing profits due to irrigation water shortages may hinder the expansion of irrigated land. By 2040, the irrigation demand may remain partly unmet, while downstream flow and baseflow could decrease. Additionally, agricultural expansion will significantly raise energy demand, posing a developmental challenge. We suggest that ensuring the sustainability of the Rio Grande Basin depends on improved water management and exploring alternative energy sources to address existing constraints.

Summer Monsoon Drying Accelerates India's Groundwater Depletion Under Climate Change

AbstractGroundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P‐value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE‐FO) and hydrological model‐based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km3 of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near‐far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production.

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

Estimation of Groundwater Recharge in a Volcanic Aquifer System Using Soil Moisture Balance and Baseflow Separation Methods: The Case of Gilgel Gibe Catchment, Ethiopia

Understanding the recharge–discharge system of a catchment is key to the efficient use and effective management of groundwater resources. The present study focused on the estimation of groundwater recharge using Soil Moisture Balance (SMB) and Baseflow Separation (BFS) methods in the Gilgel Gibe catchment where water demand for irrigation, domestic, and industrial purposes is dramatically increasing. The demand for groundwater and the existing ambitious plans to respond to this demand will put a strain on the groundwater resource in the catchment unless prompt intervention is undertaken to ensure its sustainability. Ground-based hydrometeorological 36-years data (1985 to 2020) from 17 stations and satellite products from CHIRPS and NASA/POWER were used for the SMB method. Six BFS methods were applied through the Web-based Hydrograph Analysis Tool (WHAT), SepHydro, BFLOW, and Automated Computer Programming (PART) to sub-catchments and the main catchment to estimate the groundwater recharge. The streamflow data (discharge) obtained from the Ministry of Water and Energy were the main input data for the BFS methods. The average annual recharge of groundwater was estimated to be 313 mm using SMB for the years 1985 to 2020 and 314 mm using BFS for the years 1986 to 2003. The results from the SMB method revealed geographical heterogeneity in annual groundwater recharge, varying from 209 to 442 mm. Significant spatial variation is also observed in the estimated annual groundwater recharge using the BFS methods, which varies from 181 to 411 mm for sub-catchments. Hydrogeological conditions of the catchment were observed, and the yielding capacity of existing wells was assessed to evaluate the validity of the results. The recharge values estimated using SMB and BFS methods are comparable and hydrologically reasonable. The findings remarkably provide insightful information for decision-makers to develop effective groundwater management strategies and to prioritize the sub-catchments for immediate intervention to ensure the sustainability of groundwater.

Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

A reliable pan evaporation (Epan) estimation over a daily scale is vital for sustainable water and agriculture management, especially for designing water use allocations, irrigation system management, and demand and utilization assessments. However, designing a reliable computational methodology is a challenging task for Epan estimation due to its stochastic and random characteristics. In this study, the potential of five machine learning (ML) models namely Hammerstein-Weiner (HW), Random Forest (RF), Boosted Regression Tree (BRT), Long Short-Term Memory (LSTM), and Stepwise Linear Regression (SWLR) models were evaluated for Epan modeling in Lake Hawassa catchment, Ethiopia. Afterward, hybrid SWLR-LSTM, SWLR-RF, SWLR-BRT, and SWLR-HW models were developed to benefit from the strength of the non-linear and linear characteristics of the models. The estimation results were evaluated using determination coefficient (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Root Mean Square Error (RMSE), Index of Agreement (IA) and Kling-Gupta efficiency (KGE). The performance analysis result of individual models showed that the RF model led to more accurate estimation with NSE = 0.938, IA = 0.956, R2 = 0.94, KGE = 0.852, RMSE = 0.483 mm/day and Pbias = -1.193 mm/day during the validation phase. Among the hybrid models, SWLR-RF provides the best predictive performance improving SWLR, HW, LSTM, BRT and RF by 26.563 %, 21.348 %, 15.577 %, 9.091 % and 3.514 %, respectively based on the validation phase NSE value. Generally, the results of the current study demonstrated the capability of deep learning, ensemble tree and hybrid linear-nonlinear models for Epan estimation in the data-scarce catchment.

Effects of Muddy Water Infiltration on the Hydraulic Conductivity of Soils

Despite the high sand content of Yellow River water in arid Northwest China, locals in the region opt to use muddy water to meet the demand for agricultural irrigation. Muddy water irrigation is a complex process and is still poorly understood. In this study, six sets of saturated soil column infiltration tests were designed, considering soil texture (silt loam, sandy loam, and sand) and muddy water sand content (3%, 6%, 9%, and 12%) as the influencing factors, with two sets of validation tests. Change in hydraulic conductivity (Kh), the average change rate of hydraulic conductivity (ΔK), and cumulative infiltration volume (I) were experimentally studied in the context of muddy water infiltration to respectively establish the separate functional models and developed to fit their relationship with time. The study results indicated that the hydraulic conductivity (Kh) decreased with increasing muddy water infiltration time. For silt loam and sandy loam, Kh stabilized at 0.0030 and 0.0109 cm/min, respectively, after 70 min of infiltration. In contrast, Kh in the saturated sandy soil column significantly declined throughout the muddy water infiltration, showing a 90.84% reduction after 90 min compared to the saturated hydraulic conductivity of the sandy soil. As the sand content of the muddy water increased from 3% to 12%, Kh decreased by 83.99%, 90.90%, 91.92%, and 92.21% for 3%, 6%, 9%, and 12% sand content, respectively, in the saturated sandy soil columns at the end of the infiltration period. The I values were 21.20, 9.29, 7.90, and 6.25 cm for 3%, 6%, 9%, and 12% sand content, respectively. The ΔK values were 0.0037, 0.0041, 0.0043, and 0.0044 cm/min2 for the respective sand contents, at an infiltration time of 80 min. The validation test demonstrated that the segmented function model accurately emulated the changes in hydraulic conductivity of sandy soil textures throughout the infiltration period. Results from this study provide a significant basis for understanding the mechanisms to hinder muddy water infiltration and to efficiently utilize muddy water for irrigation.

Water Governance in an Era of Climate Change: A Model to Assess the Shifting Irrigation Demand and Its Effect on Water Management in the Western United States

Water Governance in an Era of Climate Change: A Model to Assess the Shifting Irrigation Demand and Its Effect on Water Management in the Western United States

Geo-Spatial Investigation of Climate Change Impacts on Irrigation Demand of Dry Season Boro Rice in Northwest Bangladesh

The present study is an attempt to assess the irrigation demand of the dry season Boro rice in the northwest regions of Bangladesh using climatic, soil and crop data. The FAO-56 Penman–Monteith method was applied to calculate reference evapotranspiration and irrigation demand of Boro rice has been estimated using CROPWAT model. Both the observed and downscaled Representative Concentration Pathway (RCP) climatic data were used in the study. The result reveals that there is no significant change in irrigation demand of Boro rice from 1975 to present. However, the demand will increase significantly in future. The study projects that future irrigation demand of Boro rice in climate change situation for RCP 8.5 will be increased by 180-220 mm in 2075. It is expected that the findings will contribute in devising appropriate agricultural and irrigation plans for the region. The Chittagong Univ. J. B. Sci.,Vol. 10(1 &2):217-235, 2020

Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.