As ornamental container nurseries face diminishing water allocations, many are looking to automated irrigation solutions to increase their water application efficiency. This thesis presents the findings of a study conducted at a commercial container nursery to determine 1) whether a capacitance or load cell sensor was better suited for monitoring volumetric water content in the substrate; 2) if the actual irrigation distribution conformed to the expected pattern, how uniform were the weights of plants, and how these combined with plant canopy affected the leaching fraction; and 3) the reliability of the wireless network used to transmit the data to a central database. It was found that 1) the load cells outperformed the capacitance-based sensors because the load cells took an integrated measure; 2) the actual irrigation pattern followed the expected pattern, the variation of irrigation sections were low (C.V. = 0.06) and similar (C.V. ranging from 0.029 to 0.12), and unpruned plant canopies produced greater leaching fraction than pruned canopies (P < 0.18); and 3) wireless network transmission reliability was low (75.2%), suggesting that the system was not suitable for real-time irrigation control, but was sufficient for calculating irrigation length and monitoring net effective irrigation application and evapotranspirative consumption. / Graduation date: 2012

No description available.

In the past, derivative-based optimization algorithms have not frequently been used to calibrate conceptual rainfall -riff (CRR) models, partially due to difficulties associated with obtaining the required derivatives. This research applies a recently- developed technique of analytically computing derivatives of a CRR model to a complex, widely -used CRR model. The resulting least squares response surface was found to contain numerous discontinuities in the surface and derivatives. However, the surface and its derivatives were found to be everywhere finite, permitting the use of derivative -based optimization algorithms. Finite difference numeric derivatives were computed and found to be virtually identical to analytic derivatives. A comparison was made between gradient (Newton- Raphsoz) and direct (pattern search) optimization algorithms. The pattern search algorithm was found to be more robust. The lower robustness of the Newton-Raphsoi algorithm was thought to be due to discontinuities and a rough texture of the response surface.

Visando a melhoria dos processos agrícolas para produção sustentável de alimentos, o conhecimento do teor de umidade relativa vem a cooperar no processo de irrigação, apontada como uma grande consumidora de água, além de auxiliar em estudos hidrológicos, meteorológicos, geotécnicos, dentre outros. Este trabalho apresenta o desenvolvimento do hardware e firmware de um protótipo para ser utilizado com técnicas de ultrassom e análise de impedância, visando o desenvolvimento de futuros produtos para avaliar o teor de água no solo. Para a análise da impedância foi utilizado um sensor de placas paralelas, separadas 24 mm uma da outra, utilizadas juntamente com o hardware e firmware desenvolvido, para detectar a variação da constante dielétrica do solo com acréscimo de água, através da variação da fase dos sinais de tensão e corrente ao passar pelo sensor inserido no meio. Obteve-se, ao analisar 15 amostras, uma relação logarítmica crescente da variação de fase em função do acréscimo de água no solo. No estudo da caracterização da umidade do solo através do ultrassom, obteve-se resultados preliminares expressivos para as mudanças da amplitude dos sinais ultrassônicos de 40 kHz e 100 kHz no modo transmissão/recepção ao propagar-se no solo com diferentes níveis de umidade. / To improve agricultural processes for sustainable food production, knowledge of the moisture is useful to cooperate in the irrigation process, identified as a major consumer of water, besides helping in hydrological studies, meteorological, geotechnical, among others. This paper presents the development of the hardware and firmware of a prototype to be used with ultrasound techniques and impedance analysis for the development of future products to evaluate the water content in soil. For the impedance analysis, it was used a parallel plate sensor, with the plates separated from 24 mm each other, used together with the hardware and firmware developed to detect the variation of dielectric constant with the increase of soil water, by varying the phase of voltage and current signals in the sensor inserted in the medium. The results obtained, when analyzing 15 samples, presented a ratio of logarithmic growing of the phase variation due to increased soil water. In the study of characterization of soil moisture using a transmission/reception ultrasound system, the preliminary results have shown significant changes in the amplitude of ultrasonic waves of 40 kHz and 100 kHz propagating through the soil at different moisture levels.

Visando a melhoria dos processos agrícolas para produção sustentável de alimentos, o conhecimento do teor de umidade relativa vem a cooperar no processo de irrigação, apontada como uma grande consumidora de água, além de auxiliar em estudos hidrológicos, meteorológicos, geotécnicos, dentre outros. Este trabalho apresenta o desenvolvimento do hardware e firmware de um protótipo para ser utilizado com técnicas de ultrassom e análise de impedância, visando o desenvolvimento de futuros produtos para avaliar o teor de água no solo. Para a análise da impedância foi utilizado um sensor de placas paralelas, separadas 24 mm uma da outra, utilizadas juntamente com o hardware e firmware desenvolvido, para detectar a variação da constante dielétrica do solo com acréscimo de água, através da variação da fase dos sinais de tensão e corrente ao passar pelo sensor inserido no meio. Obteve-se, ao analisar 15 amostras, uma relação logarítmica crescente da variação de fase em função do acréscimo de água no solo. No estudo da caracterização da umidade do solo através do ultrassom, obteve-se resultados preliminares expressivos para as mudanças da amplitude dos sinais ultrassônicos de 40 kHz e 100 kHz no modo transmissão/recepção ao propagar-se no solo com diferentes níveis de umidade. / To improve agricultural processes for sustainable food production, knowledge of the moisture is useful to cooperate in the irrigation process, identified as a major consumer of water, besides helping in hydrological studies, meteorological, geotechnical, among others. This paper presents the development of the hardware and firmware of a prototype to be used with ultrasound techniques and impedance analysis for the development of future products to evaluate the water content in soil. For the impedance analysis, it was used a parallel plate sensor, with the plates separated from 24 mm each other, used together with the hardware and firmware developed to detect the variation of dielectric constant with the increase of soil water, by varying the phase of voltage and current signals in the sensor inserted in the medium. The results obtained, when analyzing 15 samples, presented a ratio of logarithmic growing of the phase variation due to increased soil water. In the study of characterization of soil moisture using a transmission/reception ultrasound system, the preliminary results have shown significant changes in the amplitude of ultrasonic waves of 40 kHz and 100 kHz propagating through the soil at different moisture levels.

- Einleitung: Projektvorstellung 3D – Saat - Material und Methode zur Einflussermittlung von Bodenparametern auf die Ergebnisse der Feuchtemessung - Präsentation und Diskussion der Ergebnisse - Zusammenfassung und Ausblick

This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these wetlands and nearby water bodies. Remote sensing techniques have been used to study wetlands in recent decades; however, scientific studies have rarely addressed hydrologic connectivity and hydrologic permanence, in spite of the documented importance of these properties. Research steps were designed to be straightforward for easy repeatability: 1) locate sample sites, 2) predict wetness with water indices, 3) estimate wetness with soil samples from the field, 4) validate the index predictions against the soil samples from the field, and 5) in the demonstration step, estimate hydrologic connectivity and hydrological permanence. Results indicate that both indices predicted the presence of large, open water features with clarity; that dry conditions were predicted by MNDWI with more subtle differentiation; and that NDWI results seem more sensitive to sites with vegetation. Use of this low-cost method to discover patterns of surface moisture in the landscape could directly improve the ability to manage wetland environments.

Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Achieving and maintaining sustainability in irrigated agriculture production in the era of rapidly increasing stress on our natural resources require, among other essential actions, optimum control and management of the applied water. Thus, a significant upgrade of the currently available soil water monitoring technologies is needed. The primary goal of this work was to reduce the uncertainties of spatially variable soil water in the field. Two approaches are suggested: 1) The Bayesian decision model that implicitly accounts for spatial variability at minimal cost based on limited field data, and 2) The Actively Heated Fiber Optic (AHFO) method that explicitly accounts for spatial variability with high sampling density at relatively low cost per measurement point. The Bayesian decision model uses an algorithm to integrate information embodied in independent estimates of soil water depletion to derive a posterior estimation of soil water status that has the potential to reduce the risk of costly errors in irrigation scheduling decisions. The sources of information are obtained from an ET based water balance model, soil water measurements, and expert opinion. The algorithm was tested in a numerical example based on a field experiment where soil water depletion measurements were made at 43 sites in an agricultural field under center pivot irrigation. The results showed that the estimates of the average soil water depletion in the field obtained from the posterior distributions of soil water depletion proved to outperform simple averaging of n soil water depletion measurements, up to n = 35 measurements. For n< 3, the model also provided a 39% average reduction in risk of error derived from non-representative measurements. The AHFO method observes the heating and cooling of a buried fiber optic (FO) cable through the course of a pulse application of energy as monitored by a distributed temperature sensing (DTS) system to reveal soil water content simultaneously at sub-meter scale along the FO cable that can potentially exceeds kilometers in length. A new and simple interpretation of heat data that takes advantage of the characteristics of FO temperature measurements is presented. The results demonstrate the feasibility of AHFO method application to obtain <0.05 m³m⁻³ error distributed measurements of soil water content under laboratory controlled conditions. The AHFO method was then tested under field conditions using 750 m of FO cables buried at 30, 60, and 90 cm depths in agricultural field. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse was developed in the lab. It was successively applied to the 30 and 60 cm depths cables, while the 90 cm depth cable illustrated the challenges of soil heterogeneity for this technique. The method was used to map with high spatial (1m) and temporal (1hr) resolution the spatial variability of soil water content and fluxes induced by the non-uniformity of water application at the surface. / Graduation date: 2012

Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements. Estimation of soil parameters by inverse modelling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise for heterogeneous layered soils. Although extensive soil data is becoming more and more available at various scales in the form of digital soil maps there is still a large gap between this available information and the input parameters needed for hydrological models. Inverse modeling has been extensively used but the spatial variability of the parameters and insufficient data sets restrict its applicability at the catchment scale. Use of remote sensed soil moisture data to estimate soil properties using the inverse modeling approach received attention in recent years but yielded only an estimate of the surface soil properties. However, in multilayered and heterogeneous soil systems the estimation of soil properties of different layers yielded poor results due to uncertainties in simulating root zone soil moisture from remote sensed surface soil moisture. Surface soil properties can be estimated by inverse approach using surface soil moisture data retrieved from remote sensing data. Since soil moisture retrieved from remote sensing is representative of the top 5 cm only, inversion of models using surface soil moisture cannot give good estimates of soil properties of deeper layers. Crop variables like biomass and leaf area index are sensitive to the deeper layer soil properties. The main focus of this study is to develop a methodology of estimation of root zone soil hydraulic properties in heterogeneous soils by crop model based inversion techniques. Further the usefulness of the radar soil moisture and leaf area index in retrieving soil hydraulic properties using the develop approach is be tested in different soil and crop combinations. A brief introduction about the soil hydraulic properties and their importance in agro-hydrological model is discussed in Chapter 1. Soil water retention parameters are explained in detail in this chapter. A detailed review of the literature is presented in chapter 2 to establish the state of art on the following: (i) estimation of soil hydraulic properties, (ii) role of crop models in estimating soil hydraulic properties, (iii) retrieval of surface soil moisture using water cloud model from SAR data, (iv) retrieval of leaf area index from SAR (synthetic aperture radar) data and (v) modeling of root zone soil moisture and potential recharge. The thesis proposes a methodology for estimating the root zone soil hydraulic properties viz. field capacity, wilting point and soil thickness. To test the methodology developed in this thesis for estimating the soil hydraulic properties and their uncertainty, three synthetic experiments were conducted by inversion of STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) model for maize crop using the GLUE (Generalized Likelihood Uncertainty Estimation) approach. The estimability of soil hydraulic properties in a layer-wise heterogeneous soil was examined with several sets of likelihood combinations, using leaf area index, surface soil moisture and above ground biomass. The robustness of the approach is tested with parameter estimation (model inversion) in two different meteorological conditions. The details of the numerical experiments and the several likelihood and meteorological cases examined are given in Chapter 3. The likelihood combination of leaf area index and surface soil moisture provided consistently good estimates of soil hydraulic properties for all soil types and different meteorological cases. Relatively wet year provided better estimates of soil hydraulic properties as compared with a dry year. To validate the approach of estimating root zone soil properties and to test the applicability of the approach in several crops and soil types, field measurements were carried out in the Berambadi experimental watershed located in the Kabini river basin in south India. The profile soil measurements were made for every 10 cm upto 1 m depth. Maize, Marigold, Sunflower, Sorghum and Turmeric crops were monitored during the four year period from 2010 to 2013. Crop growth parameters viz. leaf area index, above ground biomass, yield, phenological stages and crop management activities were measured/monitored at 10 day frequency for all the five crops in the study area. The details of the field experiments performed, the data collected and the results of the model inversion using the ground measured data are given in Chapter 4. The likelihood combination of leaf area index and surface soil moisture provided consistently lower root mean square error (1.45 to 2.63 g/g) and uncertainty in the estimation of soil hydraulic properties for all soil crop and meteorological cases. The uncertainty in the estimation of soil hydraulic properties was lower in the likelihood combination of leaf area index and soil moisture. Estimability of depth of root zone showed sensitivity to the rooting depth. Estimating root zone soil properties at field plot scale using SAR data (incidence angle 24o, wave length 5.3 GHz) of RADARSAT-2 is presented in the Chapter 5. In the first step, an approach of estimating leaf area index from radar vegetation index using the parametric growth curve of leaf area index and the retrieval of soil moisture using water cloud model are given in Chapter 5. The parameters of the growth curve and the leaf area index are generated using a time series of RADARSAT-2 for two years 2010-2011 and 2011-12 for the crops (maize, marigold, sunflower, sorghum and turmeric) considered in this study. The surface soil moisture is retrieved using the water cloud model, which is calibrated using the ground measured values of leaf area index and surface soil moisture for different soils and crops in the study area. The calibration and validation of LAI and water cloud models are discussed in this Chapter. Eventually, the retrieved leaf area index and surface soil moisture from RADARSAT-2 data were used to estimate the soil hydraulic properties and their uncertainty in a similar manner as discussed in Chapter 4 for various crop and soil plots and the results are presented in Chapter 5. The mean and uncertainty in the estimation of soil hydraulic properties using inversion of remote sensing data provided results similar to the estimates from inversion of ground data. The estimates of soil hydraulic properties compared well (R2 of 0.7 to 0.80 and RMSE of 2.1 to 3.16 g/g) with the physically measured vales of the parameters. In Chapter 6, root zone soil moisture and potential recharge are modelled using the STICS model and the soil hydraulic parameters estimated using the RADARSAT-2 data. The potential recharge is highly sensitive to the water holding capacity of rooting zone. Variability in the root zone soil moisture for wet and dry years for different soil types on irrigated and non-irrigated crops were investigated. Potential recharge from different crop and soil types were compared. The uncertainty in the estimation of potential recharge due to uncertainty in the estimation of field capacity is quantified. The root zone soil moisture modeled by STICS showed good agreement with the measured root zone soil moisture in all crop and soil cases. This was tested for both dry and wet year and provides similar results. The temporal variability of root zone soil moisture was also modeled well by the STICS model; the model also predicted well the intra-soil variability of soil moisture of root zone. The results of the modeling of root zone soil moisture and potential recharge are presented in Chapter 6. At the end, in Chapter 7, the major conclusions drawn from the various chapters are summarized.