A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

Utilizing commercial soil sensing technology for agronomic decisions

Planters with mounted proximal soil sensing systems can densely quantify seed zone soil variability. Technology now allows for real-time sensor information to control multiple row-unit functions on-the-go (e.g., planting depth). These and other developing sensor-based control systems have the potential to greatly improve correctness when planting, and therefore row-crop performance. For sensor-based control to be widely adopted, practitioners must understand the precision and utility of the systems. Therefore, research was conducted to: (i) determine how well commercially available sensors can estimate soil organic matter (OM) and whether sensor output was repeatable among sensing dates; (ii) evaluate OM prediction accuracy across selected soils and soil volumetric water contents with both a commercially-available, planter-mounted sensor, and machine learning techniques applied to multiple combinations of soil reflectance bands within the visible and near infrared spectrum; and (iii) investigate if planter and other proximal soil sensor data, in combination with topographic features, could predict field-scale corn emergence rate at varying planting depths. Results found that commercial sensors could estimate general trends in spatial variability of OM, but that some inconsistencies were associated with a "global" calibration that appeared susceptible to temporal variations in soil water content. In the controlled environment, results for sensor estimation of OM were similar to the field study. Further, results showed that spectral information within the entire range used by the commercial systems evaluated was required to consistently predict OM at varying volumetric water contents. Lastly, the field-scale agronomic analysis found that inherent soil and landscape variability drove the emergence rate response at the site. However, planter metrics were still usefulIncludes bibliographical references

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Modelado de las propiedades dieléctricas del suelo. Aplicación en el diseño de sensores para sistemas de control en agricultura de precisión

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. El agua es una sustancia clave para el desarrollo de la vida en La Tierra. Es por ello que la búsqueda de oportunidad de vida en otros planetas y satélites se basa en la presencia de agua en los mismos. La gestión ecológica del agua es necesaria para la sostenibilidad de los ecosistemas. Uno de los ecosistemas más amplios y donde el agua juega un papel más importante es el suelo, que alberga multitud de variedades de microorganismos cuya actividad, en parte resultante en la generación de nutrientes para el desarrollo de las especies vegetales, es totalmente dependiente del contenido de agua en el suelo. En zonas áridas y semiáridas, como es el caso de la cuenca Mediterránea, la escasez de agua supone un grave problema a la hora de gestionar los pocos recursos hídricos disponibles. En este caso, donde las condiciones geográficas son idóneas para el desarrollo de la agricultura, las soluciones pasan por una optimización de las técnicas de riego y un mayor control sobre los recursos hídricos. En este sentido, las técnicas de riego deficitario controlado se han mostrado exitosas en la reducción de la dotación hídrica a los cultivos en fases no críticas. Sin embargo, para realizar una aplicación prudente y eficiente de las mismas, resulta necesario monitorizar el estado hídrico de los cultivos, con el objetivo de que éstos no alcancen situaciones de estrés irreversible en términos de producción o estado vegetativo. Los indicadores que mayor información aportan sobre el estado hídrico de la planta suelen estar relacionados con variables medibles a partir de la propia planta, pero que son difícilmente automatizables debido a las operaciones de manejo asociadas. Este es el caso del potencial hídrico de tallo a mediodía medido con cámara de presión, considerado hasta la fecha como el indicador más fiable del estado hídrico de los cultivos en general. Es por ello que, para lograr una monitorización continua de esta variable, se busquen otras variables del continuo suelo-planta-atmósfera que puedan estar relacionadas y a partir de las cuales obtener una estimación indirecta. El suelo es la matriz de donde la planta adquiere la mayor parte del agua y los nutrientes que necesita para realizar la fotosíntesis. La relación entre el estado hídrico del suelo y el estado hídrico de los cultivos está más que demostrada. Sin embargo, la precisión alcanzada en los modelos de correlación entre ambos estados requiere de una mejora considerable para hacer un uso realmente fiable de los mismos, y esta mejora no solo pasa por encontrar mejores métodos de correlación, sino también por mejorar la precisión de las medidas obtenidas del suelo. Para monitorizar el estado hídrico del suelo, existen diversas metodologías que ofrecen parámetros medibles como el contenido de agua. El método de medida más extendido para monitorizar el contenido de agua en el suelo es a través del uso de sensores dieléctricos. Sin embargo, la precisión de los mismos está sujeta a diversos factores, entre ellos las características propias del suelo donde se instalan y su coste, relativamente alto para el pequeño y mediano agricultor, condicionando una implantación extensiva de la Agricultura de Precisión y limitando a veces la aplicación de algunos desarrollos únicamente a trabajos de investigación. Esta tesis, elaborada bajo la modalidad de compendio de publicaciones, aborda a través de cuatro artículos científicos la propuesta de soluciones accesibles para la medida del estado hídrico del suelo, con especial enfoque en el contenido de agua; explora las limitaciones y retos asociados con la calibración de los sensores dieléctricos de suelo; participa en la generación de nuevos conocimientos y propuestas para un mejor entendimiento del comportamiento del agua en el suelo y de su interacción con las ondas electromagnéticas; y establece nuevos enfoques y modelos que mejoran la predicción del estado hídrico de los cultivos a partir de medidas indirectas y automatizables en suelo y atmósfera. [ENG] This doctoral dissertation has been presented in the form of thesis by publication. Water is a fundamental substance for the development of life on Earth. That is why the search for life on other planets and satellites is based on the presence of water on them. Ecological water management is necessary for the sustainability of ecosystems. One of the most extensive ecosystems where water plays a major role is soil, which hosts a large variety of micro-organisms whose activity, partly resulting in the generation of nutrients for the development of plant species, is totally dependent on the water content of the soil. In arid and semi-arid regions, as it is the case in the Mediterranean basin, water scarcity is a serious problem when it comes to managing the few water resources available. In this case, where the geographical conditions are ideal for the development of agriculture, the solutions involve optimization of irrigation techniques and greater control over water resources. In this sense, regulated deficit irrigation strategies have proven to be successful in reducing the water supply to crops in non-critical periods. However, in order to apply them prudently and efficiently, it is necessary to monitor the water status of the crops, so that they do not reach irreversible stress situations in terms of yield or vegetative state. The indicators that provide the highest amount of information on the water status of the plant are usually related to variables that can be measured from the plant itself, but which are difficult to automate due to the labor and time-consuming associated operations. This is the case of the midday stem water potential measured with a pressure chamber, considered to date to be the most reliable indicator of the crop's water status in general. In order to achieve a continuous monitoring of this variable, it is necessary to look for other variables of the soil-plant-atmosphere continuum that may be related and from which to obtain an indirect estimate. Soil is the matrix from which the plant acquires most of the water and nutrients it needs for photosynthesis. The relationship between soil water status and crop water status is well established. However, the accuracy achieved in the correlation models between the two requires considerable improvement to make a truly reliable use of them, and this improvement is not only to find better correlation methods, but also to improve the accuracy of the measurements obtained from the soil. To monitor soil water status, there are several methodologies that provide measurable parameters such as water content. The most widespread measurement method for monitoring soil water content is through the use of dielectric sensors. However, the accuracy of these sensors is subject to various factors, including the characteristics of the soil where they are installed, and their relatively high cost for small and medium-sized farmers, conditioning the extensive implementation of precision agriculture and sometimes limiting the application of some developments only to research work. This thesis, elaborated under the modality of a compendium of publications, addresses through four scientific articles the proposal of affordable solutions for the measurement of soil water status, with special focus on water content; it explores the limitations and challenges associated with the calibration of soil dielectric sensors; participates in the generation of new insights and proposals for a better understanding of the behavior of water in soil and its interaction with electromagnetic waves; and establishes new approaches and models that improve the prediction of crop water status from indirect and automatable measurements in soil and atmosphere.Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por un total de cuatro artículos: Article I. González-Teruel, J.D., Torres-Sánchez, R., Blaya-Ros, P.J., Toledo-Moreo, A.B., Jiménez-Buendía, M., Soto-Valles, F., 2019. Design and Calibration of a Low-Cost SDI-12 Soil Moisture Sensor. Sensors, 19, 491. DOI: 10.3390/s19030491 - Article II. González-Teruel, J.D., Jones, S.B., Soto-Valles, F., Torres-Sánchez, R., Lebron, I., Friedman, S.P., Robinson, D.A., 2020. Dielectric Spectroscopy and Application of Mixing Models Describing Dielectric Dispersion in Clay Minerals and Clayey Soils. Sensors, 20, 6678. DOI: 10.3390/s20226678 Article III. González-Teruel, J.D., Jones, S.B., Robinson, D.A., Giménez-Gallego, J., Zornoza, R., Torres-Sánchez, R., 2022. Measurement of the broadband complex permittivity of soils in the frequency domain with a low-cost Vector Network Analyzer and an Open-Ended coaxial probe. Computers and Electronics in Agriculture, 195, 106847. DOI: 10.1016/J.COMPAG.2022.106847 Article IV. González-Teruel, J.D., Ruiz-Abellon, M.C., Blanco, V., Blaya-Ros, P.J., Domingo, R., Torres-Sánchez, R., 2022. Prediction of Water Stress Episodes in Fruit Trees Based on Soil and Weather Time Series Data. Agronomy, 12, 1422. DOI: 10.3390/agronomy12061422Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías Industriale

Estimation of change in soil water nitrate-nitrogen concentration using impedance spectra

A fast and reliable method for in situ monitoring of soil nitrate-nitrogen (NO3-N) concentration is vital for evaluation of N management practices focused on reduction of NO3-N losses to ground and surface waters from agricultural systems. Using dielectric measurements at multiple frequencies can help to estimate several physical and chemical soil properties simultaneously. Hence the goal of this study is to examine the feasibility to estimate changes in pore water NO3-N concentration together with volumetric water content (VWC) from the dielectric measurements obtained at multiple frequencies below several MHz where conductive behavior of soil dominates. An initial experiment with two off-the-shelf capacitance probes showed that at a relatively high frequency response of the probe was primarily correlated with VWC, while measurements at a lower frequency made by the second probe also incorporated the effect induced by changes in pore water ionic concentration. These results confirmed that using measurements at multiple frequencies can provide information about several soil properties, including NO3-N concentration. Consequently two follow-up laboratory experiments used impedance spectroscopy to estimate changes in NO3-N concentration in pure solutions and soil water, respectively, using a multivariate chemometric analysis, particularly partial least squares (PLS) regression. The results showed that change in NO3-N concentration could be estimated with sufficient accuracy when its concentration was greater than concentration of other anions (chloride in our case). In addition, estimation of NO3-N in soil water improved significantly with increasing VWC. A good agreement was found between actual and estimated NO3-N concentration when the PLS model was built using permittivity data obtained at VWC ≥ 0.20 m3 m-3. R2 and the root mean square error (RMSE) of NO3-N estimation for the best model (VWC ≥ 0.20 m3 m-3 and concentration of chloride \u3c 500 mg L-1) were 0.84 and 28 mg L-1, respectively. In general, the study demonstrated that PLS regression method coupled with the dielectric measurements obtained at multiple frequencies below several MHz can be used to indirectly estimate VWC and NO3-N concentration, but after the proper calibration equally covering the expected variations in VWC and NO3-N. For in situ application other environmental variables such as temperature should also be incorporated into the calibration process

New technologies for forest monitoring in Alpine region

The science of forest digitalization via technological innovation offers an opportunity to develop new methods for mass monitoring forest resources. A key constraint is the ability to collect data, store and analyze said retrieved data. The TreeTalker® (TT+) is a multisensory IoT-driven platform designed to detect and collect information on individual trees where its nested sensor approach captures several key eco-physiological parameters autonomously and in semi-real-time. Key parameters are: 1) tree radial growth, as an indicator of photosynthetic carbon allocation in biomass; 2) sap flux density, as an indicator of tree transpiration and functionality of xylem transport; 3) stem water content as an indicator of hydraulic functionality 4) light penetration in the canopy in terms of fractional absorbed radiation 5) light spectral components related to foliage dieback and phenology and 6) tree stability parameters to allow real time forecast of potential tree fall. The focus of this study is to design/calibrate and validate sensors for stem water content and sap flow measurement using the TreeTalker platform with the application of these platforms for monitoring ecophysiological parameters at a single tree scale providing time series data for forest monitoring. i. Stem water content To demonstrate the capability of the TreeTalker, a three-phase experimental process was performed including (1) sensor sensitivity analysis, (2) sensor calibration, and (3) long-term field data monitoring. A negative linear correlation was demonstrated under temperature sensitivity analysis, and for calibration, multiple linear regression was applied on harvested field samples, explaining the relationship between the sample volumetric water content and the sensor output signal. Furthermore, in a field scenario, TreeTalkers were mounted on adult Fagus sylvatica L. and Quercus petraea L. trees, from June 2020 to October 2021, in a beech-dominated forest near Marburg, Germany, where they continuously monitored sap flux density and stem volumetric water content (stem VWC). The results show that the range of stem VWC registered is highly influenced by the seasonal variability of climatic conditions. Depending on tree characteristics, edaphic and microclimatic conditions, variations in stem VWC and reactions to atmospheric events occurred. Low sapwood water storage occurs in response to drought, which illustrates the high dependency of trees on stem VWC under water stress. Consistent daily variations in stem VWC were also clearly detectable. Stem VWC constitutes a significant portion of daily transpiration (using TreeTalkers, up to 4% for the beech forest in our experimental site). The diurnal–nocturnal pattern of stem VWC and sap flow revealed an inverse relationship. ii. Sap flow: an empirical approach Here, a new IoT-based multisensing device, TreeTalker® with its tailored firmware is exploited to input different heating duration to capture high-frequency data of both heating and cooling phases. Using this advance in technology, its application, we aim to assess the applicability and thus merit of the TreeTalker toward sap flux density measurement and computation. Capability analysis of TT+ is verified both under a lab scenario using an artificial hydraulic column of sawdust and a stem segment of F. sylvatica L. in the field via mounted TT+ devices and with the comparison of commercial sap flow sensors on different species. Installing a TT+ on the artificial flow system, temperature evolution data from heating and reference probes are recorded both in heating and cooling phases to compute values of different flow indices under different flux densities. Applied continuous heating mode and a transient regime with four different combinations of heating and cooling times (in minutes) 10/10, 5/10, 15/45, and 10/50 are tested by TT+ and calibration of flux density vs flow indices conducted by applying optimal fitting curve on the source data up to 8 (L dm-2 h-1). Nonetheless, comparing TT+ set on the transient regime (10H/50C) performance across different species of Norway spruce, European beech, and oak in situ with well know thermal approaches (TDP: Continous Heating and HPV: Heat Pulse Velocity method) proved that the TT+ is capable to measure sap flow with reasonable accuracy (≈80%) for network-based mass monitoring in remote areas with low power consumption. iii. Semi-analytical solution for transient regime Measurement of xylem sap flow via thermal dissipation probes (TDP) and the transient regime (TTD), which is essentially derived from the TDP system, are two widely accepted and applied methods for estimating whole-tree transpiration. So far, thermal dissipation approaches use empirical equations to estimate sap flow and although robust, by nature, are limited by their accuracy. To overcome the limitations typically associated with the empirical approach, a novel method is introduced to solve the heat partial differential equation driven by the mechanisms of conduction/convection for the transient thermal dissipation method (TTD) with heating/cooling cycles. Also, a simple semi-analytical method was developed to exploit the convolution integral of the heat flow equation. The capability of the novel solution is approved by comparing its results with observations under the controlled condition as well as the output of the available well-known empirical equations under field circumstances. An essential feature of the TreeTalker platform, therefore, is to capture the full heat flow curve at the microprocessor level and integrate a semi-analytical approach to mathematically evaluate the amount of sap velocity and thermal diffusivity at a large scale and in real-time. iv. TT+ applications at forest monitoring In this investigation, two sites (Molveno and Val Canali) are established with a total of 84 TT+ in the Alpine zone, Northern Italy. The Italian Alps are important ecosystems supporting rich landscapes and biodiversity with their forests supporting several key ecosystem services. Thus, monitoring these ecosystems is of critical importance to track the variation of individuals’ ecological demands in different species. For this study, we focus on two of the most dominant tree species across the Central European Forest, Fagus Sylvatica L. and Picea Abies L., to evaluate the TT+ as a novel biosensing platform for mass monitoring. Furthermore, we explore the relationships between site characteristics and abiotic factors using collected TreeTalkers data. Although not a complete substitute for field data collection, platforms such as the Treetalker can enhance established methods for mass monitoring, offers big data solutions on individual trees, and further the pursuit of forest digitalization. Yet, as with any new technology challenges remain related to obstacles such as sensor green character, durability, flexible design, maintenance, precision, and accuracy

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells