Soil Moisture Measurements Research Articles

High soil moisture promotes the emergence of ground beetles and spiders from soils in wheat fields

Promoting arthropods in agricultural landscapes can contribute substantially to stop their decline and enhance pest control. Higher soil moisture and the presence of field margins can increase the abundance of arthropods in agricultural landscapes and influence their distribution within crop fields. However, little is known about the influence of soil moisture and distance from field margins on the overwintering of arthropods in arable fields. We investigated the influence of soil moisture and distance from a field margin on the numbers of arthropods, ground beetles and spiders emerging from soil in winter wheat fields. We established transects in winter wheat fields away from two different types of field margins: (i) around small standing water bodies (kettle holes) to capture a wide range of soil moisture values and (ii) other semi-natural landscape elements. At three distances (1 m, 20 m, 50 m), we sampled arthropods with emergence traps and measured soil moisture between March and June. We found that soil moisture had a positive effect on the emergence numbers of arthropods in general and ground beetles and spiders in particular. Distance from field margins generally had negative effects on the emergence numbers of ground beetles, but positive effects on the emergence numbers of spiders. Emergence numbers and soil moisture content did not differ significantly between the two types of field margins. The high emergence numbers inside the fields indicate that arable fields are important overwintering habitats for beneficial arthropods. Proper management of arable soils to promote soil water holding capacity and soil moisture content may have the added benefit of promoting the production of beneficial natural enemies from local soils.

Proposed Intelligent Irrigation System for Riyadh City Using Fuzzy Logic

Considering the insufficient rainfall and rapid evaporation of soil moisture in desert regions, irrigation becomes very challenging in Riyadh. Automation is therefore required for optimum productivity and water conservation. According to the vision of the Green Riyadh project, recycled water will be used in the irrigation system to irrigate all green lands. The method of irrigation and the quantity of water being used must both be considered. Since drought inhibits plant growth and excessive moisture limits plants' ability to absorb nutrients and raises the possibility of disease development, only the amount of water needed by plants should be used. Artificial intelligence and fuzzy logic are increasingly seen as solutions to be implemented in smart drip irrigation systems. The fuzzy logic control system will aid in water conservation given its shortage, which has become a major worldwide concern. The system also has the advantage of conserving moisture to counteract inadequate rainfall, saving electricity and reducing labor costs. For such advantages, an intelligent irrigation control system based on fuzzy logic adapted to the climate of Riyadh will be proposed. Based on the measured soil moisture, ambient temperature, air humidity and solar irradiance, the fuzzy controller calculates the necessary irrigation duration. The features of the fuzzy logic toolbox in MATLAB are used to create the proposed system.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell

One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy Office of Legacy Management (LM) is evaluating selected uranium mill tailings disposal cell covers to be managed as evapotranspiration (ET) covers, where vegetation is used to naturally remove water from the cover profile via transpiration, further reducing deep percolation. An important parameter in monitoring the performance of ET covers is soil moisture (SM). If SM is too high, water may drain into tailings material, potentially transporting contaminants into groundwater; if SM is too low, radon flux may increase through the cover. However, monitoring SM via traditional instrumentation is invasive, expensive, and may fail to account for spatial heterogeneity, especially over vegetated disposal cells. Here we investigated the potential for non-invasive SM monitoring using radar remote sensing and other geospatial data to see if this approach could provide a practical, accurate, and spatially comprehensive tool to monitor SM. We used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to SM at different depths of a field-scale (3 ha) drainage lysimeter embedded within an in-service LM disposal cell. We then evaluated a shallow and deep form of machine learning (ML) using Google Earth Engine to integrate multi-source observations and estimate the SM profile across six soil layers from depths of 0–2 m. The ML models were trained using in situ SM measurements from 2019 and validated using data from 2014 to 2018 and 2020–2021. Model predictors included backscatter observations from satellite synthetic aperture radar, vegetation, temperature products from optical infrared sensors, and accumulated, gridded rainfall data. The radar simulations confirmed that the lower frequencies (L- and P-band) and smaller incidence angles show better sensitivity to deeper soil layers and an overall larger SM dynamic range relative to the higher frequencies (C- and X-band). The ML models produced accurate SM estimates throughout the soil profile (r values from 0.75 to 0.94; RMSE = 0.003–0.017 cm3/cm3; bias = 0.00 cm3/cm3), with the simpler shallow-learning approach outperforming a selected deep-learning model. The ML models we developed provide an accurate, cost-effective tool for monitoring SM within ET covers that could be applied to other vegetated disposal cell covers, potentially including those with rock-armored covers.

On Various Soil Moisture Measurement Techniques Available in the Literature

The sustainability and health of soil associated with its productivity and health are closely related to soil moisture. The amount of water or moisture in the soil is an important parameter in the mobility of nutrients in the soil, in soil reclamation studies, and in the washing-transportation and mixing of fertilizers and other chemicals applied to the soil into the ground water. On the other hand, it has become important today not only to minimize water consumption but also to accurately measure the amount of moisture in the soil so that proper amount of water that the plant needs can be circulated to the root zone of the plant. In this context, information was given about various measurement methods for determining soil moisture (direct and indirect - thanks to correlations) existing in the literature, and among these studies, especially the studies on microwave methods were examined. Comparison results of the dielectric constant values obtained for three different soil samples with different gravimetric volumetric moisture rates by one of the calibration-independent non-resonant microwave measurement methods, which has a high application potential and is different from the existing calibration-requiring microwave measurement methods, and the dielectric constant values predicted by the Mironov model in the literature are presented. The comparison result showed that this new type of microwave measurement method has a high potential for measuring the moisture value of soil samples

Forest plant indicator values for moisture reflect atmospheric vapour pressure deficit rather than soil water content.

Soil moisture shapes ecological patterns and processes, but it is difficult to continuously measure soil moisture variability across the landscape. To overcome these limitations, soil moisture is often bioindicated using community-weighted means of the Ellenberg indicator values of vascular plant species. However, the ecology and distribution of plant species reflect soil water supply as well as atmospheric water demand. Therefore, we hypothesized that Ellenberg moisture values can also reflect atmospheric water demand expressed as a vapour pressure deficit (VPD). To test this hypothesis, we disentangled the relationships among soil water content, atmospheric vapour pressure deficit, and Ellenberg moisture values in the understory plant communities of temperate broadleaved forests in central Europe. Ellenberg moisture values reflected atmospheric VPD rather than soil water content consistently across local, landscape, and regional spatial scales, regardless of vegetation plot size, depth as well as method of soil moisture measurement. Using in situ microclimate measurements, we discovered that forest plant indicator values for moisture reflect an atmospheric VPD rather than soil water content. Many ecological patterns and processes correlated with Ellenberg moisture values and previously attributed to soil water supply are thus more likely driven by atmospheric water demand.

A novel AMSR2 retrieval algorithm for global C-band vegetation optical depth and soil moisture (AMSR2 IB): Parameters' calibration, evaluation and inter-comparison

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (HR) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and HR values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and HR variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and HR. As a benchmark for the calibration, we optimized the values of ω and HR by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and HR, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and HR.For the globally-constant calibration, the optimal values of ω = 0.05 and HR = 0.1 were found to yield the best results. For the land cover-based calibration, an inverse relationship between ω/HR and canopy height was observed, with ω ranging from 0.04 to 0.06 and HR ranging from 0.1 to 0.7 for heights between 0 and 30 m. The algorithm employing a land cover-based calibration (INRAE Bordeaux 2, IB2) exhibited better performance than the one utilizing a globally-constant calibration (INRAE Bordeaux 1, IB1) in evaluating retrieved SM against in situ measurements, as well as in evaluating C-VOD vs various vegetation variables including aboveground biomass (AGB), tree cover, canopy height and several optical vegetation indices. Comparison with LPRM suggested that our IB2 C-VOD retrievals present improved performances in terms of both spatial and temporal results with all considered vegetation variables (spatial correlation (R) between various vegetation variables and C-VOD of 0.76–0.83 for IB2 vs 0.69–0.79 for LPRM), and exhibited lower saturation effects when compared with AGB. In addition, the IB2 SM produced lower root mean squared error (RMSE) (0.147 vs 0.217 m3/m3), bias (−0.03 vs 0.09 m3/m3), and ubRMSE (0.066 vs 0.067 m3/m3) when compared with in situ measurements, although it showed a lower R compared to LPRM SM.

Examining the Sensitivity of Satellite-Derived Vegetation Indices to Plant Drought Stress in Grasslands in Poland.

In this study, the emphasis is on assessing how satellite-derived vegetation indices respond to drought stress characterized by meteorological observations. This study aimed to understand the dynamics of grassland vegetation and assess the impact of drought in the Wielkopolskie (PL41) and Podlaskie (PL84) regions of Poland. Spatial and temporal characteristics of grassland dynamics regarding drought occurrences from 2020 to 2023 were examined. Pearson correlation coefficients with standard errors were used to analyze vegetation indices, including NDVI, NDII, NDWI, and NDDI, in response to drought, characterized by the meteorological parameter the Hydrothermal Coefficient of Selyaninov (HTC), along with ground-based soil moisture measurements (SM). Among the vegetation indices studied, NDDI showed the strongest correlations with HTC at r = -0.75, R2 = 0.56, RMSE = 1.58, and SM at r = -0.82, R2 = 0.67, and RMSE = 16.33. The results indicated drought severity in 2023 within grassland fields in Wielkopolskie. Spatial-temporal analysis of NDDI revealed that approximately 50% of fields were at risk of drought during the initial decades of the growing season in 2023. Drought conditions intensified, notably in western Poland, while grasslands in northeastern Poland showed resilience to drought. These findings provide valuable insights for individual farmers through web and mobile applications, assisting in the development of strategies to mitigate the adverse effects of drought on grasslands and thereby reduce associated losses.

Development of forest ecosystems on biota monitoring plots in the area of influence of Gabčíkovo Waterwork

Abstract The construction and operation of waterworks has a significant impact on natural ecosystems. The evaluation of their negative impact and the proposal to minimize their impact, as well as the revitalization, especially of large rivers, have been given great importance in recent decades. The main goal of the study is to present impact of Gabčíkovo Waterwork on forest ecosystems after 30 years of damming. Specially evaluated are monitoring sites where changes are observed in the Slovak part of the so-called inland delta, i.e., between old and new riverbed of the Danube. The assessment of changes in terrestrial vegetation on selected monitoring sites was compared with the assessment of parallel measured soil moisture data. At the same time, data from the National Forestry Centre were also used to monitor changes in the state of forest ecosystems in the whole area of interest. When comparing the species composition from state to 2015 and from the period before GW was put into operation (in 1990) an increase was found in the area share of hardwood floodplain forest by 5.77% and the area shares of softwood floodplain forests decreased by 1.71%. Between 1990 and 2015, 68.43% of the territory remained unchanged at the level of forest type groups. A change in habitat conditions towards drier forest types was recorded on 23.61% of the territory.

Laboratory Evaluation of Soil Moisture Sensors for Precision Irrigation in Agriculture

Water is essential for agricultural production and food security, making efficient water use critical. Accurate measurement of soil moisture is vital for scheduling irrigation, ensuring that crops receive the right amount of water at the right time. This prevents both under- and over-irrigation, conserving water and maximizing crop production. Currently, digital soil moisture sensors are used for their accuracy and instant measurement capabilities. The soil moisture sensor's functionality was evaluated through observations of four Capacitive soil moisture sensors 1.2 (SMS A, B, C, D) at various moisture levels, with gravimetric method verification. The calibrated linear equations demonstrated a strong linear relationship for each sensor. The coefficient of determination (R2) were also found to be 0.92, 0.93, 0.91 and 0.93 respectively. These R² values indicate a strong linear relationship for each sensor. An automated drip irrigation module was developed by integrating these soil moisture sensors with the Arduino platform. The system activates the irrigation motor when soil moisture content falls below the field capacity or a desired set value, and turns it off once the required moisture level is reached. This sensor-based automated drip irrigation module enables farmers to irrigate their fields precisely and efficiently, delivering the right quantity of water at the right time. The system is also suitable for deficit irrigation practices, improving crop yield and water use efficiency.

High-resolution operational soil moisture monitoring for forests in central Germany

Abstract. The forests of central Germany (Saxony, Saxony-Anhalt, and Thuringia) are vital components of the local ecosystems, the economy, and recreation. However, in recent years, these forests have faced significant challenges due to prolonged climate-change-induced droughts, causing water shortages, tree stress, and pest outbreaks. One of the key components of the forests' vitality and productivity is the availability of soil moisture. Given the anticipated increase in the frequency and severity of drought events, there is a growing demand for accurate and real-time soil moisture information. This underscores the need for development of an appropriate monitoring tool to make forest management strategies more effective. The article introduces an operational high-resolution soil moisture monitoring framework for the forests in central Germany. The key components of this system include the advanced LWF-BROOK90 1D water balance model, a large database of the National Federal Forest Inventory, high-resolution forest soil maps, real-time climate data from the German Meteorological Service, and a web information platform for the presentation of daily updated results. This system informs the public and empowers forest managers and other decision-makers to take targeted, local measures for sustainable forest management, aiding in both drought mitigation and long-term forest health in the face of climate change. The validation of the system using soil moisture measurements from 51 stations with various sensor depths (up to 100 cm) showed an overall good agreement (0.76 median Pearson correlation), which was found to be higher for deciduous rather than coniferous forests. Finally, the framework is discussed against the background of the main limitations of existing monitoring systems and how operational soil moisture measurements contribute to better interpretation of simulations.

Performance analysis of capacitive soil moisture, temperature sensors and their applications at farmer's field.

This study aims to assess the effectiveness of PCB-based capacitive soil moisture sensors for local field conditions. The electrical scheme of designed sensors has been presented in this study. The PCB-based capacitive soil moisture sensors are calibrated using a linear equation developed between analog values of capacitive sensors and soil moisture content measured from the gravimetric method. The performance of the designed soil moisture sensors was assessed at five different locations at varying depths (i.e., 15cm, 30cm, and 45cm). The calibration results indicated a positive correlation between the soil moisture content and measurement frequency of the sensor for wheat crop, with R2 values of 0.72, 0.83, and 0.83 for 15cm, 30cm, and 45cm depths, respectively. Results reveal that 85% of the sensors accurately detected the patterns in soil moisture fluctuations during the cropping period. The designed capacitive sensors demonstrated a maximum relative error of 5.87% for 45cm depth. However, the relative error remained below 5% for the 15cm and 30cm soil depths. For the sugarcane crop, R2 values vary from 0.66 to 0.82, with the highest relative error of 5.22% at a 15cm depth. These sensors offer a highly cost-effective solution for farmers, with the entire wireless sensor network system including one sensor node, three soil moisture sensors, and one soil temperature sensor, which is priced at approximately $150, making it a practical and affordable option for widespread adoption.

Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMD-LSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area’s green cover.

Emergence of Internet of Things technology in food and agricultural sector: A review

AbstractFood processing is an indispensable sector crucial for controlling the food losses. Discrete measures were introduced and implemented to enhance the food shelf life and preservation techniques. However, poor handling and preservation methods usher to the food deprivation. In such milieu, amalgamation of futuristic technologies like sophisticated sensors tether with Internet of Things (IoT) could shoot up the food safety and minimize the deprivation. Research across the globe have proved that integration of IoT‐smart sensors in scrutinizing ecological factors such as temperature, radiation, gaseous composition, relative humidity, and moisture content that are critical for food processing and preservation. IoT has the prospects to ameliorate nationwide explicable execution, slash energy depletion, slash manufacturing expenses, inflate worker health and safety during food processing unit. Smart agricultural techniques also enable measurement of temperature, relative humidity, soil moisture, and nitrogen contents in smart farming and helps the user to determine the status of crops and commodity. This article aims to focus a few aspects and budding areas of IoT in the food and agricultural sectors. With this outlook, advancement and smartness in agriculture and food processing can be created by collaborating with IoT technology.

Preleminary Study: Need Analysis for Research and Development of Automatic Sprinkling System based on Arduino and Soil Moisture Sensor as The Instructional Aids in Soil Moisture Measurement

Instructional aids will made student easier to understanding and applying physics concepts. This study aims to obtain data and analyze the student’s needs toward to the instructional ais soil moisture measurement in physics lesson. Samples in study were student of 33 peoples, taken from class XH at SMAN 71 Jakarta with technique purposive sampling. This study resulted: physics learning motivation of student are in the medium category, with attention 87.88%, relevance 55.30%, confidence 60.61% and satisfaction 87.12%. Curriculum analysis show in class X, measurement lesson still can be developed to soil moisture measurement based on contextual learning approach. Based on style learning, 4,48% students who have visual learning style, auditory 24.24% and kinesthetic 27.27%. A total of 59.85% students experience difficulty in learning physics. As many as 79.55% of students agree that hands on method in learning can reduce difficulty in learning physics. As many as 87.12% of students agree that they need automated instrument in physics practice and only 39.39% of students has known about Arduino. The frequently used learning media in class were on audio-visual category, starting from power point slide presentation, Canva’s slides presentation, video, simulation and animation. All of those findings were supported research and development of oto-sprinkler system by using humidity sensor and Arduino Uno. Result of this study will become the base contextual in developing contextual teaching aids in form of soil moisture measurement, so that can increase the physics learning motivation in student.

Effect of Different Fertigation Scheduling Methods on the Yields and Photosynthetic Parameters of Drip-Fertigated Chinese Chive (Allium tuberosum) Grown in a Horticultural Greenhouse

This study investigated the performance of four different fertigation scheduling methods in greenhouse-grown, drip-fertigated Chinese chive (Allium tuberosum) cultivation. These methods were based on (1) the use of a timer (control), (2) accumulated radiation (AR), (3) estimated evapotranspiration (ET), and (4) measured soil moisture (SM), with fertilizer application proportional to the supplied water. These methods caused considerable variations in the amount of fertigation water (I), soil volumetric water content (θ), and bulk soil electrical conductivity, leading to variations in the harvested fresh weight (FW). The SM-based method maintained the target θ and achieved the highest irrigation water productivity (WP; the ratio of FW to ΣI), while the ET-based method led to insufficient I and FW loss. The AR-based method over-fertigated, but no FW loss was observed. Compared to the WP of the control, those of the SM-, ET-, and AR-based methods varied by +1%, −14%, and −57%, respectively. Different fertigation methods did not significantly affect leaf photosynthetic capacity, but under-fertigation caused a significant decline in stomatal conductance. Compared to the ET- and AR-based methods, the SM-based method seemed to have a lower risk of under-/over-fertigation because I in the SM-based method could be adjusted according to θ.

Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This article proposes a solution to improve irrigation efficiency, which is called DRLIC (deep reinforcement learning for irrigation control). DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances in which the output of our DRL control agent is unsafe, such as irrigating too much or too little. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively conservative action instead. To demonstrate the real-world application of our approach, we develop an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compare the water consumption of DRLIC with a widely used irrigation scheme. Our results indicate that DRLIC outperforms the traditional irrigation method by achieving water savings of up to 9.52%.

Analysis and Development of an IoT System for an Agrivoltaics Plant

This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.

Smart Solutions for Sustainable Agriculture: Design and Development of IoT-Powered Soil Health Identification Systems

This research examines smart solutions for agricultural sustainability using IoT-powered soil health detection systems. Innovative solutions are needed to manage resources and protect the environment as the world population grows and agricultural resources are under strain. IoT-enabled soil health monitoring has great potential. The suggested system uses IoT sensors to measure soil moisture, pH, nutrient content, and temperature in real time. This data is analyzed using powerful data analytics and machine learning algorithms to provide farmers soil health information. Monitoring soil quality allows farmers to make educated irrigation, fertilization, and crop selection choices, optimizing resource use and yields while minimizing environmental effect. The study also examines sensor selection, data transmission methods, and data security for IoT-powered soil health detection systems. Widespread use of these technologies might boost agricultural output, lower input costs, and enhance food security. Finally, IoT-powered soil health detection systems are a major step toward agricultural sustainability. These devices enable precision farming that is ecologically friendly and profitable by giving real-time soil data. However, effective implementation would need agricultural value chain stakeholders to collaborate and continued research to address growing issues and possibilities.

A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion

ABSTRACT Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25–40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m3/m3 with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy.