Agricultural Irrigation Management Research Articles

Application of Internet-of-Things Wireless Communication Technology in Agricultural Irrigation Management: A Review

The integration of Internet-of-Things technology with traditional agricultural irrigation is a crucial factor in the advancement of traditional agricultural irrigation towards smart irrigation. Despite the widespread use of conventional irrigation methods in many areas, they lead to the significant wastage of both human and water resources. Therefore, the development of energy-saving and efficient intelligent irrigation systems through the application of Internet-of-Things technology and wireless communication technology is the way forward. This paper summarizes the common wireless communication technologies in the agricultural Internet of Things: Fifth-generation, WiFi, ZigBee, LoRa, and NB-IoT. The research status of the above wireless communication technology in agricultural irrigation management is discussed, and the agricultural irrigation management example using the above wireless communication technology is also presented. The advantages and limitations of the application of the above wireless communication technology in agricultural irrigation management are sorted out. Finally, this paper analyzes the challenges of data security issues, data fusion problems, intelligent irrigation system costs, power and energy problems, and system equipment failures faced by the use of IoT wireless communication technology in agricultural irrigation management. This review aims to assist researchers and users in choosing the most suitable wireless communication technology for diverse applications.

WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.

Real-time mapping of crop canopy temperature using a wireless network of infrared thermometers on a central pivot

Efficient irrigation is essential for sustainable food production and regional water security. Temperature measurements provide information about crop water status, which allows the estimation of water needs and precise irrigation scheduling. This paper proposes a technology for agricultural irrigation management that combines cost-effective implementation with the ability to map crop canopy temperatures using a wireless network of infrared thermometers on a center pivot. The technology integrates Wireless Sensor Networks (WSN) with scalability options, IoT capabilities, and web data availability, enabling precise plant water stress quantification based on canopy temperature and soil water content. It facilitates real-time irrigation prescriptions while the center pivot is in motion, with canopy temperature sensors providing accurate readings of +/- 0.5 °C without additional calibration. Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and Long Short-Term Memory (LSTM) neural network models were trained to predict irrigation prescriptions, achieving Root Mean Squared Error (RMSE < 4.18 mm) and correlation coefficient (r^2 > 0.82). This technology will allow the definition of real-time and site-specific prescriptions for variable-rate irrigation systems using infrared temperature sensors. Future work includes exploring LoRaWAN technologies, implementing a Differential Global Positioning System (DGPS), improving spatial resolution with data fusion algorithms, refining irrigation prescription models, reducing sensor costs and developing models considering soil and crop effects.

Less concentrated precipitation and more extreme events over the Three River Headwaters region of the Tibetan Plateau in a warming climate

Widely known as the “China Water Tower”, changes in precipitation concentration and insufficient rainfall days over the Three River Headwaters region (TRH) may threaten food and water security in TRH and downstream areas. However, current research on rainfall occurrences in TRH lacks a comprehensive understanding of the spatiotemporal variations in precipitation concentration, the seasonal distribution of rainy days across different rainfall categories, and their correlations with global warming. In this study, we utilize the CN05.1 gridded daily rainfall dataset to address these gaps. Our analysis reveals a decreasing trend in both the precipitation concentration index (PCI) and precipitation concentration degree (PCD), accompanied by an earlier onset of the precipitation concentration period (PCP) in TRH over the past six decades. The distribution of monthly rainfall becomes more uniform throughout the year, with precipitation spreading across a greater number of days annually. Notably, the reduction in both the number of rainy days and the proportion of rainfall during the summer, along with the decrease in the frequency of light and moderate rainfall days and the increase in the frequency of moderately heavy and heavier rainfall days, will lead to a more extreme distribution of summer precipitation in TRH. The increase in the number of rainy days across all intensity categories and the proportion of precipitation during winter, has favorable implications for the sustainable development and water security in TRH. Additionally, the relative increase in the occurrence of heavy and extremely heavy rainfall days in spring demonstrates the most pronounced change, with a notable rise of 12.74% to 28.24% per decade. This provides a certain degree of alleviation for spring drought in TRH. Furthermore, we find that for every 1-degree Celsius increase in global temperature, both PCI and PCD decrease by 9.82% and 8.5%, and PCP advances by 6.41 days. With the 50th percentile of precipitation serving as a dividing line, stronger precipitation events beyond this threshold exhibit a higher positive response to global warming. Conversely, weaker precipitation events falling below the 50th percentile show a higher negative response to global warming. Light rainy days decrease by 16.2% per degree Celsius of warming, while very heavy and extremely heavy rainy days increase by 26.4% and 27.5%, respectively. These findings have important implications for mitigating droughts and floods, as well as for developing a comprehensive framework for the management of water resources and agricultural irrigation in TRH.

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

Automatic Plant Watering System Using RTC And Rain Sensor

A plant is a plant that is maintained or maintained in a medium to benefit or be harvested at any given time. The growth process requires regular watering so that plants can grow and bear good fruit. The automatic crop watering system is a study aimed at increasing the efficiency of irrigation management in agriculture automatically so that it can assist farmers in Indonesia. Where farmers are currently experiencing problems in the watering problem, there is a delay in watering the crops. This study integrated Real-Time Clock (RTC) and rain sensors as major elements in plant watering automation systems. RTC is used to set the watering schedule based on a predetermined time, while the rain sensor serves as an automatic controller to stop watering when rain is detected. The system development method involves designing well-integrated hardware and software. LCD display screens are also used in systems to provide additional information to optimize the amount of water required by plants. NodeMcu is used as a system controller so that no errors occur in scheduling plant watering. Research methods using RnD (Research and Development). The implementation of an automated crop watering system is expected to contribute to the management of water resources and energy, improve agricultural efficiency, and provide environmentally friendly and petrifying solutions for farmers across Indonesia to produce crops. Test results show that this system is capable of optimizing plant watering based on environmental conditions, thus reducing water waste and increasing plant productivity on a sustainable basis.

Spatiotemporal Evolution of Precipitation Heterogeneity Characteristics in the Heilongjiang Province from 1961 to 2020

Precipitation unevenness significantly influences the rational allocation of water resources and the management of agricultural irrigation. Based on precipitation data from 29 meteorological stations in Heilongjiang Province, China, from 1961 to 2020, this study calculated the precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period (PCP) to analyze the spatial distribution characteristics of precipitation heterogeneity at three distinct timescales: year, maize growth period, and the four stages of the maize growth period. The findings reveal that the rainy season in Heilongjiang Province commences earlier in the southwest compared with the northeast and northwest, with a primary concentration in July. At the annual scale, PCI in southwestern Heilongjiang Province surpasses that in the southeastern region, displaying an approximate east–west gradient in PCD and PCP values ranging from 0.544 to 0.746 and 196 to 203, respectively. During the growth period scale, precipitation concentrates in the southwest and central regions, occurring earlier than in the northeast and northwest. In contrast to the annual scale, the PCI value is smaller, and precipitation predominantly concentrates in mid and late July. Examining the four stages of the maize growth period, PCD generally exhibits a decreasing gradient from west to east. The highest values of PCI and PCD manifest in the southwestern part of Heilongjiang Province, with precipitation concentrated in the middle of each growth stage. The research results serve as a valuable reference for policymakers and stakeholders involved in water resource allocation and agricultural water management in Heilongjiang Province.

Optimizing Irrigation and Nutrient Management in Agriculture through Artificial Intelligence Implementation

Agriculture plays a pivotal role in sustaining global food security and addressing the challenges of a growing population. However, the efficient use of water and nutrients in agriculture is crucial to mitigate environmental impact while maximizing crop yield. In recent years, the integration of artificial intelligence (AI) techniques into agricultural practices has gained momentum, offering innovative solutions for optimizing irrigation and nutrient management. This review paper examines the diverse applications of AI in agriculture, focusing on its role in enhancing irrigation scheduling and nutrient management for improved productivity and resource conservation. The paper presents an overview of various AI technologies, such as machine learning, remote sensing, and data analytics, and their contributions to sustainable agricultural practices. It also discusses the challenges and opportunities associated with the adoption of AI in agriculture, including data quality, model interpretability, and farmer acceptance. Through a comprehensive analysis of recent research and case studies, this review underscores the potential of AI to revolutionize irrigation and nutrient management strategies, ultimately fostering a more resilient and productive agricultural sector.

Socioeconomic, environmental, and health impacts of reusing treated wastewater in agriculture in some Arab countries, including occupied Palestine, in view of climate change

The increase in water stress and shortage, facing many countries around the world, is one of the main difficulties confronting practical progress and sustainable development and management. Accordingly, managing the water assets of many countries around the world is nowadays a big challenge due to immense difficulties and vulnerabilities, including rapid industrialization and urbanization processes, population growth, geopolitical instability, and the effects of environmental changes, namely global warming and climate change. Because of global fresh waters scarcity and shortage, the demand for using non-conventional water resources, such as reusing treated wastewater for irrigation and industrial purposes, has become a nessitiy. However, the reuse of effluents for agricultural irrigation can have negative impacts on crop quality and soil conditions, as well as on public health and the environment. Moreover, improper management of agricultural irrigation with treated wastewater can also cause problems for plant production and soils’ physical and chemical propeties. This paper investigates the status of freshwater and wastewater in view of climate change, and socioeconomic, environmental, and health impacts of reusing treated wastewater for irrigation in the Arab region, with the focus on the Occupied Palestinian Territories (OPT), as an example. The paper concludes that: 1) Approximately 13.2 billion cubic meters (BCM) of wastewater is yearly produced in the Arab countries, of which 5.7 BCM (43.2%) is treated and 7.5 MCM (56.8%) is untreated and dumped in open environments; 2) Regarding the OPT, where more than 87% of its fresh water resources are controlled and forcefully taken by the Israeli occupation authorities, Palestinians discharge large amounts of untreated wastewater into open lands (as in the case of the occupied West Bank) and in the Mediterranean Sea (as in the case of occupied and besieged Gaza Strip); and 3) The reused portion of treated wastewater in the OPT is close to zero.

The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance (NTR) and the normalized difference vegetation index (NDVI) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the NTR−NDVI trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers.

A Review of Advances in Groundwater Evapotranspiration Research

Groundwater evapotranspiration (ETg) is an important component of the hydrological cycle in water-scarce regions and is important for local ecosystems and agricultural irrigation management. However, accurate estimation of ETg is not easy due to uncertainties in climatic conditions, vegetation parameters, and the hydrological parameters of the unsaturated zone and aquifers. The current methods for calculating ETg mainly include the WTF method and the numerical groundwater model. The WTF method often requires data supplementation from the numerical unsaturated model to reduce uncertainty; in addition, it relies on point-monitoring data and cannot solve the spatial heterogeneity of ETg. The ETg calculation module of the numerical groundwater model is set up too simply and ignores the influence from the unsaturated zone and surface cover. Subsequent research breakthroughs should focus on the improvement of WTF calculation theory and the setting up of an aquifer water-table fluctuation monitoring network. The numerical groundwater model should couple the surface remote sensing data with the unsaturated zone model to improve the accuracy of ETg calculation.

Restoration strategies for water and salt transport in saline soils: A 20‐year bibliometric analysis

AbstractWorld Soil Day 2021 encouraged the prevention of soil salinization and improved land productivity. As research underpins this effort, we analysed trends in research on water and salt mobilization in saline soils and groundwater over the past 20 years. We found that the average studies have increased by 30%, from 110 in 2003–2015 to 143 in 2016–2022, with agriculture, water resources and environmental science being the main research fields. The most common research interests were anthropogenic impact (28.6%) and climate change (27.8%), followed by agricultural irrigation management (20%). A keyword clustering analysis revealed that the studies could be classified into four categories: “Agricultural production,” “Freshwater and groundwater salinization,” “Seawater intrusion,” and “Solute dynamic migration.” The most frequently used keyword was “transport” while the use of “freshwater,” “groundwater,” and “seawater intrusion” has increased sharply, suggesting that seawater intrusion and freshwater salinization are an area of increasing interest related to climate change. We identified comprehensive simulating systems for seawater intrusion as an important area for future research. We recommend that promoting a comprehensive and quantitative understanding of water and salt transport in saline soil is needed to ensure stakeholders are provided with science‐based information required for policymaking aimed towards sustainable development.

Modeling of Climatic Variables using Stochastic Approaches in Sudan

The climatic variables take a significant role in agricultural process and irrigation management because we need to know all changes related to the climate which absolutely will be affected agricultural yield. For this purpose, the ARIMA models were suggested in this study for modeling daily average temperature, relative humidity, and solar radiation variables related to five main meteorological stations (Wad Madani, Khartoum, Al Gadaref, Al Damazin, and Dongola) in Sudan. The daily variables were obtained from the period 2013-to 2020 years. Time series analysis methods are used for estimating and modeling the climatic variables using ARIMA (Autoregressive Integrated Moving Average Models) which are called Box Jenkins models. For modeling purposes, linear stochastic models were used to estimate future values of daily variables. The Augmented Dickey-Fuller test (ADF) was applied to the time series to check the stationarity at 1%, 5%, and 10% confidence levels. The time series of variables were stationarity and without trend. Diagnostic checks were used for checking models and selecting the best models from the autocorrelation (ACF) and partial autocorrelation (PACF) function graphs. The best models were selected according to the adjusted R2, Standard error (S.E), Akaike information criterion (AIC), and Bayesian information criterion (BIC) values. The best results were observed in ARIMA (1,0,1) and (1,0,2) which can be effective for predicting future values. The ARIMA models obtained satisfactory results for temperature, relative humidity, and solar radiation variables. So, this study might be extremely helpful for agricultural engineers to achieve all the processes related to agricultural practices.

Experimental Investigations on Influence of Fracture Networks on Overland Flow and Water Infiltration in Soil

Soil preferential flow is an essential process that affects the movement and relocation of soil water and solutes. This study was conducted on cropland in an arid and semi-arid area in Zhongning County, Ningxia. According to the different cracks, rain intensity, rainfall duration, and slope, there were three groups, and 17 dye tracer experiments were conducted in the field. We quantified the characteristics of soil preferential flow by investigating and analyzing the infiltration depth, dyeing area, saturation, runoff coefficient, and rainfall infiltration coefficient using the dye tracer method. The results showed that increasing the rainfall or irrigation intensity could promote the activation of the fracture channel as the preferential flow channel, which is advantageous to the preferential flow formation. The fractures dominated the formation of the preferential flow. The fractures slowed the formation of runoff, reduced the velocity of slope flow, reduced the flow of the slope, and increased the amount of soil water infiltration. These results have theoretical and practical significance for understanding soil water transportation, especially for agricultural irrigation management and improving cropland water use efficiency in arid and semi-arid areas.

Розроблення та впровадження Системи інформаційного забезпечення землеробства

Goal. To develop and implement the System of Information Support for Agriculture in Research Farms of the National Academy of Agrarian Sciences of Ukraine. Methods. The Information System will have 4 blocks: 1 — a database of general data on agricultural resources used in the process of agricultural production; 2 — a block for filling in information about individual agricultural enterprises; 3 — a block of planning and operational management of agricultural technologies, irrigation and water regulation; 4 — a program complex for multivariate simulation modeling of prospective scenarios of the development of individual economic units, their associations within territorial communities, land reclamation systems, homogeneous in terms of agricultural resource potential of regions or territories. Results. The agricultural information support system will make it possible to objectively assess the agricultural resource potential of research farms of the National Academy of Sciences, to quickly provide information on the state of crops and features of the development of crops, and to justify agrotechnological and other management decisions. In addition, the System will provide the possibility of independent current and prospective planning of the activities of research farms of the National Academy of Sciences based on understanding the impact and mutual influence of interrelated dynamic factors of the environment and various components of agricultural production. As a result, the staff of research farms of the NAAS system will be able to make informed management decisions in a specific situation and in the future, based on the reference information, as well as thanks to the use of complex and branch software products. Conclusions. After the creation and implementation of the Agricultural Information Support System in the experimental farms of the National Academy of Sciences, their management staff will be able to more effectively use the available agricultural resources in their optimal combination, systematically improve and develop the structure of agricultural production, which guarantees a purposeful and radical increase in the economic, social and environmental efficiency of the activities of the experimental farms of the National Academy of Sciences.

Changes and determining factors of crop evapotranspiration derived from satellite-based dual crop coefficients in North China Plain

Evaluating actual crop evapotranspiration (ETc) variations and their determining factors under changing climates is crucial for agricultural irrigation management and crop productivity improvement in non-humid regions. This study analyzed the spatiotemporal characteristics and detected the determining factors of ETc for winter wheat and summer maize rotation system from 2000 to 2017 in the North China Plain (NCP), by combining the FAO-56 dual crop coefficient approach with remotely sensed vegetation indices (VIs). The results indicated that daily air temperature increased in varying degrees while wind speed and sunshine hours decreased slightly during the growing season of winter wheat and summer maize over the study period. The trends of relative humidity and effective precipitation varied in crop growing seasons. Based on the validated relationship of dual crop coefficients and VIs, the estimated multi-year average ETc of winter wheat (370.29 ± 31.28 mm) was much higher than summer maize (281.85 ± 20.14 mm), and the rotation cycle was 652.43 ± 27.67 mm. Annual ETc of winter wheat and the rotation cycle increased by 2.96 mm a−1 and 1.77 mm a−1, respectively. However, the ETc of summer maize decreased with distinct spatial variation. Spatially, winter wheat ETc increased significantly in the northeast NCP, covering the Beijing-Tianjin-Hebei areas. Meanwhile, significant increases in summer maize ETc were detected in the southwest NCP. The sensitivity and contribution analysis showed that ETc of winter wheat and summer maize was positively sensitive to temperature, wind speed, and sunshine hours while negatively to relative humidity. Moreover, wind speed and sunshine hours contributed most to changes in ETc (around 20%–40%).

Application of Cosmic-Ray Neutron Sensor Method to Calculate Field Water Use Efficiency

Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation.

Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.

Prediction Model of Soil Electrical Conductivity Based on ELM Optimized by Bald Eagle Search Algorithm

Soil electrical conductivity is one of the indispensable and important parameters in fine agriculture management, and a suitable soil electrical conductivity can promote good plant growth. Prediction model of soil electrical conductivity is constructed to effectively obtain the conductivity values of soil, which can provide a reference basis for irrigation and fertilization management and prediction evaluation in fine agriculture. Prediction model of soil electrical conductivity based on extreme learning machine (ELM) optimized by bald eagle search (BES) algorithm is proposed in this paper. In the prediction model, the input weights and bias values of the ELM network were optimized using the BES algorithm, and the performance of the model was evaluated with parameters such as mean square error (MSE), coefficient of determination (R^2). Also, the correlations of parameters such as soil temperature, moisture content, pH, and water potential in the soil conductivity prediction model were determined using the exploratory data analysis (EDA) and HeatMap heat map tools. Finally, the proposed model was compared with back propagation neural network (BP), radial basis function networks (RBF), support vector machine (SVM), gated recurrent neural network (GRNN), long short-term memory (LSTM), particle swarm algorithm (PSO) optimization ELM, genetic algorithm (GA) optimized ELM prediction model. The experimental results showed that MSE, R^2 of the proposed model are 4.09 and 0.941, which are better than the other models. Also the results verified the effectiveness of the proposed method, which is a feasible prediction method to guide the irrigation and fertilization management in fine agriculture, because of its good prediction effect on soil conductivity.

Evaluating the adaptability of an irrigation district to seasonal water availability using a decade of remotely sensed evapotranspiration estimates

Competition for fresh water is increasing in highly populated areas especially those regions located in arid and semi-arid climates. Water uses are under scrutiny to determine the potential for water savings. Particularly, the agricultural sector is identified because it is the largest consumer of water. However, information is limited on the year-to-year variability of agricultural irrigation management, specifically the variability related to seasonal water availability. Remote sensing is a useful input into models to estimate actual evapotranspiration (ETa) for large irrigation districts and provides an archive of historical data. In this paper, a decade of remote sensing data are used applying the METRIC algorithm and linear interpolation to achieve ETa estimates. Inter-annual variations in seasonal ETa are related to snowpack, weather, irrigation diversions and crop data to understand the factors having an impact. It was found that consecutive (second) years of dry or wet events impacted the seasonal ETa, indicating limitations in the buffer capacity of the irrigation district. This study shows that the irrigation district is capable of buffering dry or wet events during the first year, but need to adapt during a consecutive year of extreme dry or wet weather. Additionally, cropping patterns indicate that the crop choices in the irrigation district do not change during extreme events. In contrast, irrigation diversions are influenced by prolonged dry events. The depleted fraction indicated good performance of the irrigation system, with potential for improvements in dry years. This study demonstrates the dynamic consumptive water use of an irrigation district, thereby indicating the importance for considering multiple years of data for an assessment of the agricultural sectors’ water use. Additionally, the impact of seasonal water availability gives insight on the adaptability of an irrigation district and its’ capability to cope with current and future variability in weather conditions and extreme events.