Irrigation Planning Research Articles

Implementation of smart irrigation system

Current global technology significantly impacts agriculture, with automation playing a crucial role by executing procedures without assistance from any individual. This work aims to demonstrate how an individual can set up an affordable automatic irrigation system in a few hours by connecting various electronic components and materials. A sensor-based automated irrigation system has been designed and implemented, providing widespread use and significant advantages. This system aids daily activities by saving time and reducing labor. It makes use of a sensor technology in conjunction with a microcontroller, relay, DC motor, and battery. By measuring the moisture content of the soil and turning on irrigation as necessary, the device functions as an intelligent switch. The motor automatically turns on or off based on soil dryness. Sensor readings are transmitted to a computer for graph generation and analysis, allowing easy monitoring and control via a computer. Green roofs, nurseries, greenhouses, and gardens of all sizes can use this watering system.It conserves time, energy, reduces water wastage, and simplifies irrigation planning for farmers, enhancing their management of crops.

Geo-statistical assessment of spatial variability of soil water-holding capacity for optimal irrigation under semi-arid vertisols in South India

Based on a field survey conducted in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, details about the crops grown, cropping pattern, land utilization and soil samples were collected from 20 locations under seven different soil textures of semi-arid vertisols. Using the data, efficient geostatistical models have been explored to study the spatial variation of irrigation water requirement of paddy, sugarcane and banana grown in the study area. Geostatistics was applied to identify the best interpolation method to acquire the spatial map of the available water holding capacity. Ordinary kriging and Disjunctive kriging were explored with six models viz., Circular, Spherical, Exponential, Gaussian, Penta-spherical and Sine-Hole effect models and were used for spatial prediction. The cross-validation statistics indicated that the Sine-Hole effect model with disjunctive kriging of available water holding capacity was superior for interpolation with a minimum value of RMSE and moderate spatial dependency. With the available water holding capacity map of Lalgudi block, total available water, readily available water and irrigation interval of paddy, sugarcane and banana were estimated. The irrigation interval of paddy, sugarcane and banana were found to vary between 1-2, 3-5 and 2-4 days respectively within Lalgudi block. The adoption of spatial algorithms for estimating crop water requirements would greatly help irrigation planners and water policymakers create efficient regional plans for precision irrigation under semi-arid vertisols.

Impact of climatic trends on the water demand and irrigation planning for annual crops in the São Francisco River basin, Brazil

This study evaluated the spatial distribution of precipitation (P) and Peman-Monteith reference ET (ETo) in Brazil's São Francisco River basin (1961–2022). The analysis included Mann-Kendall trend tests and future IPCC projections on P and ETo to assess the need for supplemental irrigation (SI) of maize crop over twelve hypothetical growing seasons. The average annual P in the basin was 1300 mm year−1, decreasing from the Upper to the Lower section. Annual ETo averaged 1670 mm year−1, with higher depths in the Middle and Lower-middle sections. Trends for both P and ETo were significant(p < 0.05) in 58% of the basin. The overall trend magnitude in P was positive (average of about 0.5 mm year−2). Trend was decreasing in 56% of the sites (3.2 mm year−2) and increasing in 44% of them (5.3 mm year−2). For ETo, the overall trend was also positive (average of 3.0 mm year−2), with increasing trend in 79% of the sites (4.6 mm year−2) and a decreasing trend of 3.8 mm year−2 in 21% of the area. The need of SI varied both over the year with peaks in the May–August period and over the basin with higher depths in the Middle and Lower-middle sections. Large SI values (>800 mm) were associated with the dry season and areas with persistent trends in decreasing P and increasing ETo. Future IPCC projections indicate increased SI depths in most of the basin by 2081–2100, with the Middle and Lower-middle sections facing potentially unsustainable level for crop production.

Public irrigation decision support systems (IDSS) in Italy: Description, evaluation and national context overview

This survey comprehensively examines the public irrigation decision support systems (IDSS) in Italy, offering a detailed description, analysis and evaluation of their features. The study investigates the agrometeorological networks and infrastructures that support Italian IDSS, providing a clearer understanding of the national context. The evaluation criteria include relevant factors such as soil moisture monitoring, crop water requirements (CWR) estimation models, biophysical parameters along with their spatial and temporal resolutions, irrigation planning and decision support visualization. Additionally, the assessment covers accessibility, scalability and interoperability of these systems. The survey also highlights the strengths and weaknesses of various IDSS, such as IRRIFRAME, IRRISIAS and IRTO, discussing their operational methodologies, data integration and regional coverage. The aim is to provide insights that facilitate advancements in sustainable irrigation management practices and address key challenges for future developments at both regional and national levels. This comprehensive evaluation seeks to enhance the effectiveness of IDSS in promoting sustainable water management in agriculture across Italy.

Climate Change Impacts for Multipurpose Reservoir Planning: Basnagoda Reservoir, Sri Lanka

Climate change via rainfall and temperature changes poses threats to water resources. Traditionally, these variations are rarely considered in irrigation infrastructure planning. Integrating climate impacts into early irrigation planning ensures resilience and sustainability.This study aims to assess the climate change impacts on Basnagoda reservoir using a reservoir operation study, system designing and sizing, water use strategies and making recommendations as mitigation and adaptation strategies.This study employed reservoir operation model recommended in the Irrigation guideline, Sri Lanka to assess four climatic scenarios derived from predicted temperature and rainfall changes. Sensitivity analysis included reservoir sizing for the most critical scenario. Mitigation and adaptation methodologies such as 1) Reservoir capacity enhancement 2) Seasonal shifting 3) Conveyance enhancement were analyzed. Reservoir operations currently meet 100% of domestic demand, irrigating total 950 ha annually (380 ha Maha, 570 ha Yala) with environmental flow. Design capacity fulfils 78% of domestic demand, with environmental flow for projected period. The maximum cultivable area at the design stage while providing 25% of design domestic demand is 544 ha in Maha and 680 ha in Yala season. With a 20% rainfall reduction posing the worst scenario, domestic water fulfilment reduced to 20% (Maha) and 19% (Yala). Optimal reservoir capacity enhancement is 15%, expanding cultivable area to 390 ha, while providing 25% of design domestic demand. Conveyance enhancement increases cultivable area from reservoir water by 15%. This study is unique for integrating climate change impacts into reservoir infrastructure planning and design stages.

Rain and evapotranspiration using the Penman-Monteih model for the periods 1963-1982. 1983-2001 and 2002-2019 for the amparo de São Francisco – Sergipe, Brazil

Evaluate the performance of the empirical method of estimating evapotranspiration, with the standard Penman-Monteih method, suggested by FAO-56, for Amparo de São Francisco – Sergipe in the periods 1963-1982; 1983-2001 and 2002-2019 generating their respective graphs and information for government decision makers. The Penman-Monteih method makes it possible to estimate (ETo(PM)) in mm/month and, after computing it, it is multiplied by the total number of days in each month of the year, thus obtaining the Penman-Monteith model estimated in mm/month. The standard Penman-Monteih method can help with irrigation planning and management, especially for small irrigators who do not have access to meteorological data. A better adaptation of the Penman-Monteith and Thornthwaite model was observed in the estimation of ETo, where its results showed adaptation for the location studied. It is advisable to evaluate other models so that they can provide more appropriate results. It is stated that the Penman-Monteith and Thornthwaite models are the best alternatives for the use of evapotranspiration in Amparo de São Francisco - Sergipe.

Modelling Soil Moisture Content with Hydrus 2D in a Continental Climate for Effective Maize Irrigation Planning

In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily evapotranspiration and water needs for maize using soil and climate data from 2021 to 2023. The HYDRUS model predicted volumetric soil moisture content, validated against observed data. A 2D hydrodynamic model within HYDRUS simulated temporal and spatial variations in soil water distribution for maize at a non-irrigated site in Hungary. The model used soil physical properties and crop evapotranspiration rates as inputs, covering crop development stages from planting to harvest. The model showed good performance, with R² values of 0.65 (10 cm) and 0.81 (60 cm) in 2021, 0.51 (10 cm) and 0.50 (60 cm) in 2022, and 0.38 (10 cm) and 0.72 (60 cm) in 2023. RMSE and NRMSE values indicated reliability. The model revealed water deficits and proposed optimal irrigation schedules to maintain soil moisture between 32.2 and 17.51 V/V%. This integrated approach offers a reliable tool for monitoring soil moisture and developing efficient irrigation systems, aiding maize production’s adaptation to climate change.

Leveraging atmospheric moisture recycling in Saudi Arabia and neighboring countries for irrigation and afforestation planning

Effective irrigation planning is crucial for sustainable agricultural development and ecosystem restoration projects in arid regions. With respect to ambitious greening initiatives, Saudi Arabia is establishing a national strategy toward a more sustainable and eco-friendly future not only for itself but also for the broader Middle East region. Thus, comprehensively understanding the water cycle in the region is essential to identify the most suitable target locations for afforestation and reforestation while considering the potential role of irrigation. Herein, in addition to traditional pedoclimatic factors, we introduce a complementary consideration—“irrigation recycling.” Building on the well-established concept of atmospheric moisture recycling and taking advantage from an atmospheric trajectory dataset, we track the path of evaporated water from current or potential irrigated sites to the location where the evaporated water eventually falls as precipitation. Our analysis offers two key benefits. First, it helps pinpoint the regions in which and the periods during which water recycling is maximum within the country, aiding more precise calculations of the investment return value for irrigation infrastructures. Second, it helps identify the land-use change patterns that contribute to international efforts such as drought mitigation in East Africa as an example. We found that one-third of the actual precipitation in the current Saudi irrigated sites originated from evapotranspiration over land, mainly from Saudi Arabia and surrounding countries. Interestingly, most of the evapotranspiration from these irrigated sites will eventually fall somewhere over land (primarily in Iran). Controlling the seasonality and spatial distribution of the future irrigation expansion will allow controlling the atmospheric moisture recirculation in Saudi Arabia and nearby drought-prone regions such as Eastern Africa. The outcomes of this study will be the subject of future integrated assessments to account for the climatic feedbacks of the land-use change scenarios. At present, they provide crucial insights to support the decision-making process surrounding the Saudi and Middle East Green Initiatives. Further, the presented methodology offers a pragmatic framework that can be applied to similar greening projects for other regions, making it a viable and valuable approach for global sustainability programs.

Assessment of the reliability to use NASAPOWER gridded weather applied to irrigation planning and management in Brazil

Assessment of the reliability to use NASAPOWER gridded weather applied to irrigation planning and management in Brazil

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

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 &amp;2):217-235, 2020

Remote Sensing Assessment of Water Resources, Vegetation, and Land Surface Temperature in Eastern Saudi Arabia: Identification, Variability, and Trends

Saudi Arabia has one of the biggest water shortages and the least vegetation in the world, which is presumed to provoke this problem further due to climate change. Therefore, the present study investigates the water, vegetation, and temperature over Al-Asfar Lake region, Al Ahsa, Eastern province of Saudi Arabia using the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST: °C) from Landsat-8 based operational land imager (OLI) measurements for the period 2013 to 2023. This study presented annual and seasonal (dry months: June–September and wet months: December–April) spatiotemporal distribution and variations, calculated their absolute change and trends, and examined their relationship. Results showed positive NDWI values over Al-Asfar Lake, indicating waterbodies; while positive NDVI values on the lake's bank, signifying vegetation. Notably, there were significant temporal variations in water and vegetation observed on annual, seasonal, and monthly scales. The study also found an overall decrease in vegetation areas of 5.36 km2 in 2023 compared to 2013, while waterbodies increased by 8.83 km2. The trend analysis using area-averaged data demonstrated that NDWI increased on annual (0.0075/year) and seasonal (dry: 0.0083/year and wet: 0.0049/year) scales, while NDVI decreased (annual: 0.0066/year, dry: 0.0083/year, and wet: 0.0009/year). Moreover, LST was recorded least amount over waterbodies (28.23 °C) and vegetation (32.45 °C) covered areas compared to the entire lake region (38.43 °C), respectively. Remark, LST displayed decreasing trends over waterbodies (−0.05/year), followed by vegetation (−0.17/year), and the entire lake region (−0.0001/year), signifying that water and vegetation are vital components to controlling land surface temperature in this region. Finally, the LST showed a positive correlation with NDVI and negative correlation with NDWI. There may be a direct and indirect impact of climate change upon NDVI, LST, and NDWI as shown by the decreases in NDVI and LST and an increase in NDWI. This study can be considered as a base document to monitor waterbodies, vegetation cover, and temperature changes using remote sensing measurements of NDWI, NDVI, and LST, which will assist policymakers in developing water resource management, irrigation planning, and environmental monitoring strategies.

Improved remote sensing reference evapotranspiration estimation using simple satellite data and machine learning

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (i.e., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies

Quantifying the impacts of varying groundwater table depths on cotton evapotranspiration, yield, water use efficiency, and root zone salinity using lysimeters

Determining the evapotranspiration (ET) of cotton as a water-intensive crop is crucial for effective irrigation planning and water management, especially in regions like Sindh province, Pakistan, where shallow groundwater table depths (WTDs) are prevalent. Despite the importance of cotton, a major cash crop in Sindh, previous studies on ET were conducted decades ago and may no longer be reliable due to ongoing climate change and the introduction of new crop varieties. Thus, we quantified cotton ET across two cropping seasons and at various WTDs (0.45, 0.60, 0.75, 1.50, 2.25, and 2.75 m). The experimental study was based on the data procured from 12 mini lysimeters and 12 large lysimeters for two years (2018 and 2019) and at two soil series. The findings revealed that cotton ET ranged from 1332 to 1437, 1114–1202, 988–1075, 781–821, 690–733, and 637–683 mm at WTDs of 0.45, 0.60, 0.75, 1.50, 2.25, and 2.75 m, respectively. WTDs from 0.45 to 0.75 m fulfilled 94–96 % of cotton ET through groundwater (GW) contribution in Sultanpur soil (silt loam) and 93–97 % in Miani soil (silty clay loam). At 1.50–2.75 m WTDs, irrigation water requirements (excluding rainfall and leaching) were 63–88 % in Sultanpur soil and 67–89 % in Miani soil. The highest yield was observed at a 1.50 m WTD, while the highest water use efficiency was identified at a 2.25 m WTD. However, soil salinity increased by 60–80 %, resulting in a 40–60 % lower cotton yield at 0.45–0.75 m WTD. Therefore, periodic flushing of salts is necessary to utilize shallow WTDs effectively. Considering GW contribution to ET when allocating water for irrigation channels and devising irrigation schedules is crucial. This approach can lead to water savings, prevent land from becoming waterlogged and saline, manage the groundwater table, and reduce the need for drainage channels and labor force for their preparation.

Development of temperature and mass transfer-based empirical models for estimating reference evapotranspiration in Nigeria

ABSTRACT The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims to develop novel empirical models to enhance evapotranspiration (ET0) estimation accuracy in Nigeria. The coefficients of non-linear equations were optimised using the particle swarm optimisation (PSO) algorithm for the development of the two new ET0 models for Nigeria, Awhari1 (temperature-based) and Awhari2 (mass transfer-based). ERA5 reanalysis data with a 0.1° × 0.1° resolution was used. The models were rigorously assessed against the FAO-56 Penman–Monteith method, resulting in Kling–Gupta efficiency (KGE) and percentage bias (Pbias) values of 0.75, 6.49, and 0.92, 5.67, respectively. The spatial distribution analysis of performance metrics showed both equations exhibited superior accuracy in estimating ET0 across diverse climatic zones in Nigeria. The incorporation of PSO in model development, coupled with spatial analysis, highlights the study's multidimensional approach. The spatial performance of the models indicates that they can be valuable tools for water resource management, irrigation planning, and sustainable agriculture practices in Nigeria.

The Impact of Water-Saving Irrigation on Rice Growth and Comprehensive Evaluation of Irrigation Strategies

To explore the effects of different water-saving treatments on rice plant growth and select suitable water-saving irrigation strategies for aerobic rice varieties, we conducted relevant field experiments from June to October 2023 at Jiangsu Runguo Agricultural Development Co., Ltd., China, which is located in a north subtropical monsoon climate where the soil is alkaline sandy loam. Four water treatments were set up, including the control of local conventional irrigation (CK, without water stress), mild water-saving treatment (W1, 20% more water saved than CK), moderate water-saving treatment (W2, 30% more water saved than CK), and severe water-saving treatment (W3, 40% more water saved than CK). The experiment results showed that rice plant heights were inhibited and leaf chlorophyll contents increased under all water-saving treatments compared to CK. Among them, the MDA content in paddy leaves under the W1 treatment decreased, while the activities of SOD and POD were enhanced and the membrane lipid peroxidation capacity of rice was also enhanced. Meanwhile, the results showed that the rice yield and quality under the W1 treatment significantly improved. Based on those experiments, a comprehensive evaluation of rice plant height, chlorophyll content, grain yield, yield components, and rice quality was conducted using the TOPSIS entropy weight method. It was preliminarily concluded that the suitable irrigation scheme for south and central Jiangsu was 20% water-saving irrigation compared with CK. In summary, under the premise of maintaining the economic yield of rice cultivation, an appropriate water irrigation plan helped save water resources and promote rice growth.

Assessing crop yield and water balance in crop rotation irrigation systems: Exploring sensitivity to soil hydraulic characteristics and initial moisture conditions in the North China Plain

Multiple cropping is an effective measure to enhance the intensity of land use. The North China Plain is one of China’s most important grain production areas, with 70 % of the arable land under double rotation of winter wheat and summer maize. The allocation of irrigation water between two crop seasons depends on soil water flow and crop water consumption. This is because the amount of moisture left in the soil at the end of one season affects how much moisture is in the soil at the beginning of the next season. As a result, the amount of crops produced and the amount of water needed for irrigation are influenced by the initial soil moisture conditions and the soil’s hydraulic properties. This study aims to analyze how various factors affect crop yield when faced with water scarcity. The factors considered include initial soil moisture condition, soil texture, and irrigation scheduling. The simulation results indicate that, in most cases, initial soil moisture conditions have a more significant impact on crop yield than soil hydraulic characteristics. The impact of irrigation can differ based on the irrigation method and availability of water. Hence, when distributing irrigation water over a year, it is crucial to consider the soil’s water transport between two crop cycles to achieve the ideal full and deficit irrigation in a crop rotation system. Additionally, a joint optimal irrigation plan can significantly reduce the adverse effects of unfavorable soil hydraulic characteristics. Moreover, the optimal irrigation strategy can enhance crop water productivity and food security simultaneously.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Spatial mapping of soil moisture content using very-high resolution UAV-based multispectral image analytics

Assessing soil moisture content (SMC) is necessary for managing water at a spatial scale. Remote sensing technologies provide a robust approach for detecting the spatial-temporal fluctuations of SMC. The aim of this study was to estimate SMC at different soil depths using very high-resolution unmanned aerial vehicle (UAV)-based multispectral (MS) images and machine learning algorithms and generate spatial maps of SMC using the best-performed machine learning (ML) algorithm. The UAV-based multispectral images of bare soil were captured at 40 m altitude with a very high spatial resolution (2.89 cm) during the rabi 2021/22 season. At the same time, the soil samples were collected from different soil depths, and the gravimetric SMC was measured. Five machine-learning algorithms (Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR)) were used to train the model between SMC and MS data (MS band reflectance and vegetation indices). The soil with high SMC has low spectral reflectance and soil with low SMC shows high spectral reflectance. For the prediction of surface SMC, the linear regression (R2 = 0.89; RMSE = 2.80 %) and 5 cm depth SMC, the SVR (R2 = 0.64; RMSE = 3.03 %) were performed well compared to other ML algorithms. For surface SMC, blue band reflectance, and 5 cm depth SMC, the Ratio Vegetation Index (RVI) correlated well compared to others. All models failed to predict the SMC at the deeper soil depths. The spatial SMC mapping described the visual color variations in SMC within the field. Crop irrigation scheduling can be significantly improved through the insights this spatial SMC estimation approach provides, making it a valuable tool for farmers and irrigation planners.

Evaluating Trends of Temperature Index in Gia Lai Province Based on IITA and IPTA Approaches under Climate Change

This study performs historical trend analysis for average, maximum, and minimum temperatures using the integration of the Improved visualization for trend analysis (IITA) and Innovative Polygon Trend Analysis (IPTA) of Şen to evaluate the tendency of extreme temperatures on different time scales (annual, monthly and seasonal) in Gia Lai province from 1980 to 2019 at three stations, including Pleiku, AyunPa, and An Khe. Results recorded over the past four decades, the temperature trend in Gia Lai has been gradually warming. The average temperature in each month of the year ranges from 19 to 28°C, December and January usually have the lowest temperature of the year with a variation lower than 20°C. On the contrary, the months of April, May, and June often have the highest temperatures of the year, with a variation greater than 26°C. The outcomes of this research have provided a general picture of temperature characteristics, useful information for the management and planning of land use, irrigation and water supply in Gia Lai province under unpredictable climate change.