Die vorliegende Arbeit behandelt die elektrochemische Synthese (elektrochemische Polymerisation und Copolymerisation) und die Charakterisierung der Redox- und sensorischen Eigenschaften neuer funktionalisierter Polymere für die Ionensensorik. Die Funktionalisierung wird sowohl in der Polymer-Hauptkette (Polysalene) als auch in der Polymer-Seitenkette (ein Thiophen-Copolymer: 3-Methylthiophen/6-Hydroxy-2-(2-(3-thienyl)-ethoxy)-acetophenon) dargestellt. Die Redox-Prozesse der funktionalisierten Polymere wurden mit spektroelektrochemischen Methoden: ESR-, UV-Vis-NIR- und FTIR-Spektroelektrochemie charakterisiert. Durch diese Methoden konnten während der elektrochemischen Oxidation von funktionalisierten leitfähigen Polymeren verschiedene Polymer- bzw. Copolymer-Ladungsträger nachgewiesen werden: Polaronen, Bipolaronen beim Thiophen-Copolymer, zwei Polaronen auf einer Polymerkette im Singulettezustand beim Poly(3-methylthiophen) und eine diamagnetische Spin-Spin-Wechselwirkung zwischen ungepaarten Elektronen der Cu(II)-Ionen und der ungepaarten Elektronen von bisphenolischen Ligand-Kationradikalen beim Poly[Cu(II)-salen]. Sensorische Eigenschaften gegenüber Ni(II)-Ionen wurden durch Potentiometrie an einem Poly[Ni(II)-salen]-Derivat getestet. Es zeigt eine gute potentiometrische Ni(II)-Ionenselektivität (der Logarithmus des potentiometrischen Selektivitätskoeffizienten liegt im Bereich von -0.5 bis -1.5) in Anwesenheit von Cd(II), Mn(II), Zn(II) und Na(I).

Extensive deforestations, particularly in the (sub)tropics, have led to intense soil degradation and erosion with concomitant reduction in soil fertility. Reforestations or plantations on those degraded sites may provide effective measures to mitigate further soil degradation and erosion, and can lead to improved soil quality. However, a change in land use from, e.g., grassland to forest may have a crucial impact on water balance. This may affect water availability even under humid tropical climate conditions where water is normally not a limiting factor. In this context, it should also be considered that according to climate change projections rainfall may decrease in some of these regions. To mitigate climate change related problems (e.g. increases in erosion and drought), reforestations are often carried out. Unfortunately, those measures are seldom completely successful, because the environmental conditions and the plant specific requirements are not appropriately taken into account. This is often due to data-scarcity and limited financial resources in tropical regions. For this reason, innovative approaches are required that are able to measure environmental conditions quasi-continuously in a cost-effective manner. Simultaneously, reforestation measures should be accompanied by monitoring in order to evaluate reforestation success and to mitigate, or at least to reduce, potential problems associated with reforestation (e.g. water scarcity). To avoid reforestation failure and negative implications on ecosystem services, it is crucial to get insights into the water balance of the actual ecosystem, and potential changes resulting from reforestation. The identification and prediction of water balance changes as a result of reforestation under climate change requires the consideration of the complex feedback system of processes in the soil-vegetation-atmosphere continuum. Models that account for those feedback system are Soil-Vegetation-Atmosphere-Transfer (SVAT) models. For the before-mentioned reasons, this study targeted two main objectives: (i) to develop and test a method combination for site evaluation under data scarcity (i.e. study requirements) (Part I) and (ii) to investigate the consequences of prediction uncertainty of the SVAT model input parameters, which were derived using geophysical methods, on SVAT modeling (Part II). A water balance modeling approach was set at the center of the site evaluation approach. This study used the one-dimensional CoupModel, which is a SVAT model. CoupModel requires detailed spatial soil information for (i) model parameterization, (ii) upscaling of model results and accounting for local to regional-scale soil heterogeneity, and (iii) monitoring of changes in soil properties and plant characteristics over time. Since traditional approaches to soil and vegetation sampling and monitoring are time consuming and expensive (and therefore often limited to point information), geophysical methods were used to overcome this spatial limitation. For this reason, vis-NIR spectroscopy (visible to near-infrared wavelength range) was applied for the measurement of soil properties (physical and chemical), and remote sensing to derive vegetation characteristics (i.e. leaf area index (LAI)). Since the estimated soil properties (mainly texture) could be used to parameterize a SVAT model, this study investigated the whole processing chain and related prediction uncertainty of soil texture and LAI, and their impact on CoupModel water balance prediction uncertainty. A greenhouse experiment with bamboo plants was carried out to determine plant-physiological characteristics needed for CoupModel parameterization. Geoelectrics was used to investigate soil layering, with the intent of determining site-representative soil profiles for model parameterization. Soil structure was investigated using image analysis techniques that allow the quantitative assessment and comparability of structural features. In order to meet the requirements of the selected study approach, the developed methodology was applied and tested for a site in NE-Brazil (which has low data availability) with a bamboo plantation as the test site and a secondary forest as the reference (reference site). Nevertheless, the objective of the thesis was not the concrete modeling of the case study site, but rather the evaluation of the suitability of the selected methods to evaluate sites for reforestations and to monitor their influence on the water balance as well as soil properties. The results (Part III) highlight that one needs to be aware of the measurement uncertainty related to SVAT model input parameters, so for instance the uncertainty of model input parameters such as soil texture and leaf area index influences meaningfully the simulated model water balance output. Furthermore, this work indicates that vis-NIR spectroscopy is a fast and cost-efficient method for soil measurement, mapping, and monitoring of soil physical (texture) and chemical (N, TOC, TIC, TC) properties, where the quality of soil prediction depends on the instrument (e.g. sensor resolution), the sample properties (i.e. chemistry), and the site characteristics (i.e. climate). Additionally, also the sensitivity of the CoupModel with respect to texture prediction uncertainty with respect to surface runoff, transpiration, evaporation, evapotranspiration, and soil water content depends on site conditions (i.e. climate and soil type). For this reason, it is recommended that SVAT model sensitivity analysis be carried out prior to field spectroscopic measurements to account for site specific climate and soil conditions. Nevertheless, mapping of the soil properties estimated via spectroscopy using kriging resulted in poor interpolation (i.e. weak variograms) results as a consequence of a summation of uncertainty arising from the method of field measurement to mapping (i.e. spectroscopic soil prediction, kriging error) and site-specific ‘small-scale’ heterogeneity. The selected soil evaluation method (vis-NIR spectroscopy, structure comparison using image analysis, traditional laboratory analysis) showed that there are significant differences between the bamboo soil and the adjacent secondary forest soil established on the same soil type (Vertisol). Reflecting on the major study results, it can be stated that the selected method combination is a way forward to a more detailed and efficient way to evaluate the suitability of a specific site for reforestation. The results of this study provide insights into where and when during soil and vegetation measurements a high measurement accuracy is required to minimize uncertainties in SVAT modeling.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Umfangreiche Abholzungen, besonders in den (Sub-)Tropen, habe zu intensiver Bodendegradierung und Erosion mit einhergehendem Verlust der Bodenfruchtbarkeit geführt. Eine wirksame Maßnahme zur Vermeidung fortschreitender Bodendegradierung und Erosion sind Aufforstungen auf diesen Flächen, die bisweilen zu einer verbesserten Bodenqualität führen können. Eine Umwandlung von Grünland zu Wald kann jedoch einen entscheidenden Einfluss auf den Wasserhaushalt haben. Selbst unter humid-tropischen Klimabedingungen, wo Wasser in der Regel kein begrenzender Faktor ist, können sich Aufforstungen negativ auf die Wasserverfügbarkeit auswirken. In diesem Zusammenhang muss auch berücksichtigt werden, dass Klimamodelle eine Abnahme der Niederschläge in einigen dieser Regionen prognostizieren. Um die Probleme, die mit dem Klimawandel in Verbindung stehen zu mildern (z.B. Zunahme von Erosion und Dürreperioden), wurden und werden bereits umfangreiche Aufforstungsmaßnahmen durchgeführt. Viele dieser Maßnahmen waren nicht immer umfassend erfolgreich, weil die Umgebungsbedingungen sowie die pflanzenspezifischen Anforderungen nicht angemessen berücksichtigt wurden. Dies liegt häufig an der schlechten Datengrundlage sowie an den in vielen Entwicklungs- und Schwellenländern begrenzter verfügbarer finanzieller Mittel. Aus diesem Grund werden innovative Ansätze benötigt, die in der Lage sind quasi-kontinuierlich und kostengünstig die Standortbedingungen zu erfassen und zu bewerten. Gleichzeitig sollte eine Überwachung der Wiederaufforstungsmaßnahme erfolgen, um deren Erfolg zu bewerten und potentielle negative Effekte (z.B. Wasserknappheit) zu erkennen und diesen entgegenzuwirken bzw. reduzieren zu können. Um zu vermeiden, dass Wiederaufforstungen fehlschlagen oder negative Auswirkungen auf die Ökosystemdienstleistungen haben, ist es entscheidend, Kenntnisse vom tatsächlichen Wasserhaushalt des Ökosystems zu erhalten und Änderungen des Wasserhaushalts durch Wiederaufforstungen vorhersagen zu können. Die Ermittlung und Vorhersage von Wasserhaushaltsänderungen infolge einer Aufforstung unter Berücksichtigung des Klimawandels erfordert die Berücksichtigung komplex-verzahnter Rückkopplungsprozesse im Boden-Vegetations-Atmosphären Kontinuum. Hydrologische Modelle, die explizit den Einfluss der Vegetation auf den Wasserhaushalt untersuchen sind Soil-Vegetation-Atmosphere-Transfer (SVAT) Modelle. Die vorliegende Studie verfolgte zwei Hauptziele: (i) die Entwicklung und Erprobung einer Methodenkombination zur Standortbewertung unter Datenknappheit (d.h. Grundanforderung des Ansatzes) (Teil I) und (ii) die Untersuchung des Einflusses der mit geophysikalischen Methoden vorhergesagten SVAT-Modeleingangsparameter (d.h. Vorhersageunsicherheiten) auf die Modellierung (Teil II). Eine Wasserhaushaltsmodellierung wurde in den Mittelpunkt der Methodenkombination gesetzt. In dieser Studie wurde das 1D SVAT Model CoupModel verwendet. CoupModel benötigen detaillierte räumliche Bodeninformationen (i) zur Modellparametrisierung, (ii) zum Hochskalierung von Modellergebnissen unter Berücksichtigung lokaler und regionaler Bodenheterogenität, und (iii) zur Beobachtung (Monitoring) der zeitlichen Veränderungen des Bodens und der Vegetation. Traditionelle Ansätze zur Messung von Boden- und Vegetationseigenschaften und deren Monitoring sind jedoch zeitaufwendig, teuer und beschränken sich daher oft auf Punktinformationen. Ein vielversprechender Ansatz zur Überwindung der räumlichen Einschränkung sind die Nutzung geophysikalischer Methoden. Aus diesem Grund wurden vis-NIR Spektroskopie (sichtbarer bis nah-infraroter Wellenlängenbereich) zur quasi-kontinuierlichen Messung von physikalischer und chemischer Bodeneigenschaften und Satelliten-basierte Fernerkundung zur Ableitung von Vegetationscharakteristika (d.h. Blattflächenindex (BFI)) eingesetzt. Da die mit geophysikalisch hergeleiteten Bodenparameter (hier Bodenart) und Pflanzenparameter zur Parametrisierung eines SVAT Models verwendet werden können, wurde die gesamte Prozessierungskette und die damit verbundenen Unsicherheiten und deren potentiellen Auswirkungen auf die Wasserhaushaltsmodellierung mit CoupModel untersucht. Ein Gewächshausexperiment mit Bambuspflanzen wurde durchgeführt, um die zur CoupModel Parametrisierung notwendigen pflanzenphysio- logischen Parameter zu bestimmen. Geoelektrik wurde eingesetzt, um die Bodenschichtung der Untersuchungsfläche zu untersuchen und ein repräsentatives Bodenprofil zur Modellierung zu definieren. Die Bodenstruktur wurde unter Verwendung einer Bildanalysetechnik ausgewertet, die die qualitativen Bewertung und Vergleichbarkeit struktureller Merkmale ermöglicht. Um den Anforderungen des gewählten Standortbewertungsansatzes gerecht zu werden, wurde die Methodik auf einem Standort mit einer Bambusplantage und einem Sekundärregenwald (als Referenzfläche) in NO-Brasilien (d.h. geringe Datenverfügbarkeit) entwickelt und getestet. Das Ziel dieser Arbeit war jedoch nicht die Modellierung dieses konkreten Standortes, sondern die Bewertung der Eignung des gewählten Methodenansatzes zur Standortbewertung für Aufforstungen und deren zeitliche Beobachtung, als auch die Bewertung des Einfluss von Aufforstungen auf den Wasserhaushalt und die Bodenqualität. Die Ergebnisse (Teil III) verdeutlichen, dass es notwendig ist, sich den potentiellen Einfluss der Messunsicherheiten der SVAT Modelleingangsparameter auf die Modellierung bewusst zu sein. Beispielsweise zeigte sich, dass die Vorhersageunsicherheiten der Bodentextur und des BFI einen bedeutenden Einfluss auf die Wasserhaushaltsmodellierung mit CoupModel hatte. Die Arbeit zeigt weiterhin, dass vis-NIR Spektroskopie zur schnellen und kostengünstigen Messung, Kartierung und Überwachung boden-physikalischer (Bodenart) und -chemischer (N, TOC, TIC, TC) Eigenschaften geeignet ist. Die Qualität der Bodenvorhersage hängt vom Instrument (z.B. Sensorauflösung), den Probeneigenschaften (z.B. chemische Zusammensetzung) und den Standortmerkmalen (z.B. Klima) ab. Die Sensitivitätsanalyse mit CoupModel zeigte, dass der Einfluss der spektralen Bodenartvorhersageunsicherheiten auf den mit CoupModel simulierten Oberflächenabfluss, Evaporation, Transpiration und Evapotranspiration ebenfalls von den Standortbedingungen (z.B. Klima, Bodentyp) abhängt. Aus diesem Grund wird empfohlen eine SVAT Model Sensitivitätsanalyse vor der spektroskopischen Feldmessung von Bodenparametern durchzuführen, um die Standort-spezifischen Boden- und Klimabedingungen angemessen zu berücksichtigen. Die Anfertigung einer Bodenkarte unter Verwendung von Kriging führte zu schlechten Interpolationsergebnissen in Folge der Aufsummierung von Mess- und Schätzunsicherheiten (d.h. bei spektroskopischer Feldmessung, Kriging-Fehler) und der kleinskaligen Bodenheterogenität. Anhand des gewählten Bodenbewertungsansatzes (vis-NIR Spektroskopie, Strukturvergleich mit Bildanalysetechnik, traditionelle Laboranalysen) konnte gezeigt werden, dass es bei gleichem Bodentyp (Vertisol) signifikante Unterschiede zwischen den Böden unter Bambus und Sekundärwald gibt. Anhand der wichtigsten Ergebnisse kann festgehalten werden, dass die gewählte Methodenkombination zur detailreicheren und effizienteren Standortuntersuchung und -bewertung für Aufforstungen beitragen kann. Die Ergebnisse dieser Studie geben einen Einblick darauf, wo und wann bei Boden- und Vegetationsmessungen eine besonders hohe Messgenauigkeit erforderlich ist, um Unsicherheiten bei der SVAT Modellierung zu minimieren.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Extensos desmatamentos que estão sendo feitos especialmente nos trópicos e sub-trópicos resultam em uma intensa degradação do solo e num aumento da erosão gerando assim uma redução na sua fertilidade. Reflorestamentos ou plantações nestas áreas degradadas podem ser medidas eficazes para atenuar esses problemas e levar a uma melhoria da qualidade do mesmo. No entanto, uma mudança no uso da terra, por exemplo de pastagem para floresta pode ter um impacto crucial no balanço hídrico e isso pode afetar a disponibilidade de água, mesmo sob condições de clima tropical úmido, onde a água normalmente não é um fator limitante. Devemos levar também em consideração que de acordo com projeções de mudanças climáticas, as precipitações em algumas dessas regiões também diminuirão agravando assim, ainda mais o quadro apresentado. Para mitigar esses problemas relacionados com as alterações climáticas, reflorestamentos são frequentemente realizados mas raramente são bem-sucedidos, pois condições ambientais como os requisitos específicos de cada espécie de planta, não são devidamente levados em consideração. Isso é muitas vezes devido, não só pela falta de dados, como também por recursos financeiros limitados, que são problemas comuns em regiões tropicais. Por esses motivos, são necessárias abordagens inovadoras que devam ser capazes de medir as condições ambientais quase continuamente e de maneira rentável. Simultaneamente com o reflorestamento, deve ser feita uma monitoração a fim de avaliar o sucesso da atividade e para prevenir, ou pelo menos, reduzir os problemas potenciais associados com o mesmo (por exemplo, a escassez de água). Para se evitar falhas e reduzir implicações negativas sobre os ecossistemas, é crucial obter percepções sobre o real balanço hídrico e as mudanças que seriam geradas por esse reflorestamento. Por este motivo, esta tese teve como objetivo desenvolver e testar uma combinação de métodos para avaliação de áreas adequadas para reflorestamento. Com esse intuito, foi colocada no centro da abordagem de avaliação a modelagem do balanço hídrico local, que permite a identificação e estimação de possíveis alterações causadas pelo reflorestamento sob mudança climática considerando o sistema complexo de realimentação e a interação de processos do continuum solo-vegetação-atmosfera. Esses modelos hidrológicos que investigam explicitamente a influência da vegetação no equilíbrio da água são conhecidos como modelos Solo-Vegetação-Atmosfera (SVAT). Esta pesquisa focou em dois objetivos principais: (i) desenvolvimento e teste de uma combinação de métodos para avaliação de áreas que sofrem com a escassez de dados (pré-requisito do estudo) (Parte I), e (ii) a investigação das consequências da incerteza nos parâmetros de entrada do modelo SVAT, provenientes de dados geofísicos, para modelagem hídrica (Parte II). A fim de satisfazer esses objetivos, o estudo foi feito no nordeste brasileiro,por representar uma área de grande escassez de dados, utilizando como base uma plantação de bambu e uma área de floresta secundária. Uma modelagem do balanço hídrico foi disposta no centro da metodologia para a avaliação de áreas. Este estudo utilizou o CoupModel que é um modelo SVAT unidimensional e que requer informações espaciais detalhadas do solo para (i) a parametrização do modelo, (ii) aumento da escala dos resultados da modelagem, considerando a heterogeneidade do solo de escala local para regional e (iii) o monitoramento de mudanças nas propriedades do solo e características da vegetação ao longo do tempo. Entretanto, as abordagens tradicionais para amostragem de solo e de vegetação e o monitoramento são demorados e caros e portanto muitas vezes limitadas a informações pontuais. Por esta razão, métodos geofísicos como a espectroscopia visível e infravermelho próximo (vis-NIR) e sensoriamento remoto foram utilizados respectivamente para a medição de propriedades físicas e químicas do solo e para derivar as características da vegetação baseado no índice da área foliar (IAF). Como as propriedades estimadas de solo (principalmente a textura) poderiam ser usadas para parametrizar um modelo SVAT, este estudo investigou toda a cadeia de processamento e as incertezas de previsão relacionadas à textura de solo e ao IAF. Além disso explorou o impacto destas incertezas criadas sobre a previsão do balanço hídrico simulado por CoupModel. O método geoelétrico foi aplicado para investigar a estratificação do solo visando a determinação de um perfil representante. Já a sua estrutura foi explorada usando uma técnica de análise de imagens que permitiu a avaliação quantitativa e a comparabilidade dos aspectos estruturais. Um experimento realizado em uma estufa com plantas de bambu (Bambusa vulgaris) foi criado a fim de determinar as caraterísticas fisiológicas desta espécie que posteriormente seriam utilizadas como parâmetros para o CoupModel. Os resultados do estudo (Parte III) destacam que é preciso estar consciente das incertezas relacionadas à medição de parâmetros de entrada do modelo SVAT. A incerteza presente em alguns parâmetros de entrada como por exemplo, textura de solo e o IAF influencia significantemente a modelagem do balanço hídrico. Mesmo assim, esta pesquisa indica que vis-NIR espectroscopia é um método rápido e economicamente viável para medir, mapear e monitorar as propriedades físicas (textura) e químicas (N, TOC, TIC, TC) do solo. A precisão da previsão dessas propriedades depende do tipo de instrumento (por exemplo da resolução do sensor), da propriedade da amostra (a composição química por exemplo) e das características das condições climáticas da área. Os resultados apontam também que a sensitividade do CoupModel à incerteza da previsão da textura de solo em respeito ao escoamento superficial, transpiração, evaporação, evapotranspiração e ao conteúdo de água no solo depende das condições gerais da área (por exemplo condições climáticas e tipo de solo). Por isso, é recomendado realizar uma análise de sensitividade do modelo SVAT prior a medição espectral do solo no campo, para poder considerar adequadamente as condições especificas do área em relação ao clima e ao solo. Além disso, o mapeamento de propriedades de solo previstas pela espectroscopia usando o kriging, resultou em interpolações de baixa qualidade (variogramas fracos) como consequência da acumulação de incertezas surgidas desde a medição no campo até o seu mapeamento (ou seja, previsão do solo via espectroscopia, erro do kriging) e heterogeneidade especifica de uma pequena escala. Osmétodos selecionados para avaliação das áreas (vis-NIR espectroscopia, comparação da estrutura de solo por meio de análise de imagens, análise de laboratório tradicionais) revelou a existência de diferenças significativas entre o solo sob bambu e o sob floresta secundária, apesar de ambas terem sido estabelecidas no mesmo tipo de solo (vertissolo). Refletindo sobre os principais resultados do estudo, pode-se afirmar que a combinação dos métodos escolhidos e aplicados representam uma forma mais detalhada e eficaz de avaliar se uma determinada área é adequada para ser reflorestada. Os resultados apresentados fornecem percepções sobre onde e quando, durante a medição do solo e da vegetação, é necessário se ter uma precisão mais alta a fim de minimizar incertezas potenciais na modelagem com o modelo SVAT.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185

Die vorliegende Arbeit behandelt die elektrochemische Synthese (elektrochemische Polymerisation und Copolymerisation) und die Charakterisierung der Redox- und sensorischen Eigenschaften neuer funktionalisierter Polymere für die Ionensensorik. Die Funktionalisierung wird sowohl in der Polymer-Hauptkette (Polysalene) als auch in der Polymer-Seitenkette (ein Thiophen-Copolymer: 3-Methylthiophen/6-Hydroxy-2-(2-(3-thienyl)-ethoxy)-acetophenon) dargestellt. Die Redox-Prozesse der funktionalisierten Polymere wurden mit spektroelektrochemischen Methoden: ESR-, UV-Vis-NIR- und FTIR-Spektroelektrochemie charakterisiert. Durch diese Methoden konnten während der elektrochemischen Oxidation von funktionalisierten leitfähigen Polymeren verschiedene Polymer- bzw. Copolymer-Ladungsträger nachgewiesen werden: Polaronen, Bipolaronen beim Thiophen-Copolymer, zwei Polaronen auf einer Polymerkette im Singulettezustand beim Poly(3-methylthiophen) und eine diamagnetische Spin-Spin-Wechselwirkung zwischen ungepaarten Elektronen der Cu(II)-Ionen und der ungepaarten Elektronen von bisphenolischen Ligand-Kationradikalen beim Poly[Cu(II)-salen]. Sensorische Eigenschaften gegenüber Ni(II)-Ionen wurden durch Potentiometrie an einem Poly[Ni(II)-salen]-Derivat getestet. Es zeigt eine gute potentiometrische Ni(II)-Ionenselektivität (der Logarithmus des potentiometrischen Selektivitätskoeffizienten liegt im Bereich von -0.5 bis -1.5) in Anwesenheit von Cd(II), Mn(II), Zn(II) und Na(I).

Tesis por compendio / [ES] El conocimiento del estado nutricional de los cultivos permite corregir o ajustar cualquier exceso o deficiencia nutricional en los mismos, a lo largo de su ciclo vegetativo, asegurando un alto rendimiento en la producción y una óptima calidad del fruto. Tradicionalmente, para la realización del diagnóstico nutricional se ha utilizado el análisis de la ionómica de diferentes órganos de la planta, especialmente las hojas, por su facilidad de muestreo y por ser el órgano fotosintético de excelencia en las plantas. Por ello es necesario implementar estrategias sostenibles que nos permitan ajustar la dosis de fertilización según las necesidades del cultivo con el mínimo riesgo de contaminación. El objetivo de esta tesis doctoral es desarrollar métodos y modelos que permitan el diagnóstico nutricional en cultivos mediante métodos ópticos no destructivos, como la espectroscopia y la imagen hiperespectral en el rango Vis-NIR, en combinación con técnicas de quimiometría. De este modo, el primer bloque centrado en el cultivo de caqui cv. 'Rojo Brillante', comprende los estudios publicados en dos artículos científicos. En el primero de estos artículos se estudió el potencial de la espectroscopia Vis-NIR (430-1040 nm), con el objetivo de predecir macros y micronutrientes utilizando modelos de regresión PLS. Los resultados mostraron que es posible predecir de forma precisa macronutrientes como, fósforo (P), calcio (Ca) y magnesio (Mg), con un coeficiente de determinación en la predicción (R2P) de 0,78 a 0,63. En los micronutrientes, el boro (B) y el manganeso (Mn) fueron los que obtuvieron mejores coeficientes de predicción, con R2P de 0,79 y 0,69, respectivamente. En el segundo artículo se ha evaluado, para la estimación de la concentración de nutrientes, el uso de imágenes hiperespectrales en el rango entre 500 y 980 nm. Los resultados mostraron la predicción de los macronutrientes como N, P, potasio (K), Ca y Mg con R2P de 0,80 a 0,62 y, para los micronutrientes, solo en el B se obtuvo un valor aceptable para la estimación (R2p = 0,69). Además, utilizando el método de reducción de variables de influencia en la proyección (VIP) se obtuvo una predicción fiable para los nutrientes de N (R2P = 0,76) y B (R2P = 0,61). En el segundo bloque, se ha estudiado otro cultivo emblemático en la Comunidad Valenciana por su importancia social y económica, como son los cítricos. En este caso, se desarrollaron herramientas de estimación del cv. 'clementina de Nules', descritas en otros dos artículos científicos. En el tercer artículo se ha estudiado la capacidad de la espectroscopía para determinar la concentración de nutrientes en las hojas de los cítricos en un ciclo vegetativo completo. Los resultados mostraron una predicción con un R2P de 0,70 a 0,65 para el P, K Ca y B. Utilizando el coeficiente de regresión de ponderado (BW) se determinó un subconjunto de bandas importantes para determinar la concentración de P, K y B. Los resultados mostraron que las bandas de mayor relevancia para estos nutrientes se situaron en la región del visible (430-750 nm), asociada a la absorción de pigmentos fotosintéticos. Finalmente, en el cuarto artículo se ha estudiado el potencial de la imagen hiperespectral para discriminar entre hojas jóvenes y hojas de ciclos vegetativos anteriores, lo que mejoraría el diagnóstico dado que las tablas de referencia en este cultivo están realizadas en hojas de la brotación de primavera. Partiendo de esa hipótesis, se obtuvo que es posible realizar la discriminación entre ambos tipos de hojas. Posteriormente se realizó la predicción de concentración de nutrientes de hojas jóvenes, utilizando 49 bandas espectrales, obteniendo mejores resultados para los nutrientes P, K, Ca, hierro (Fe) y Mn con R2P de 0,69 a 0,60. Además, se realizó la predicción de estos nutrientes minimizando el número de bandas a diez, con el BW y se obtuvo un R2P de 0,67 a 0,57. / [CA] El coneixement de l'estat nutricional dels cultius permet corregir o ajustar qualsevol excés o deficiència nutricional en estos, al llarg del seu cicle vegetatiu, assegurant un alt rendiment en la producció i una òptima qualitat del fruit. Tradicionalment, per a la realització del diagnòstic nutricional s'han utilitzat l'anàlisi de la ionómica de diferents òrgans de la planta, especialment les fulles, per la seua facilitat de mostreig i per ser l'òrgan fotosintètic d'excellència en les plantes. Per això és necessari implementar estratègies sostenibles que ens permeten ajustar la dosi de fertilització segons les necessitats del cultiu amb el mínim risc de contaminació. L'objectiu d'esta tesi doctoral és desenvolupar mètodes i models que permeten la predicció del diagnòstic nutricional en cultius mitjançant mètodes òptics no destructius, com l'espectroscòpia Vis-NIR, en combinació amb tècniques quimio mètriques. D'esta manera, el primer capítol de publicacions es centra en el cultiu del caqui cv. Rojo Brillante, comprés per dos articles (I i II). En el primer d'estos articles es va estudiar el potencial de l'espectroscòpia Vis-NIR (430-1040 nm), amb l'objectiu de predir macros i micronutrients utilitzant models de regressió PLS. En este estudi es van aplicar tractaments diferencials per als nutrients de N (0 %, 33 %, 50 % i 100 %) i per a K2O (0 %, 50 % i 100 %) de la demanda del cultiu. Els resultats van mostrar que, sí que és possible predir de manera precisa macronutrients com, fòsfor (P), calci (Ca)i magnesi (Mg), amb un coeficient de determinació en la predicció (R2P) de 0,78 a 0,63. En els micronutrients, com el bor (B) i el manganés (Mn) van ser els que van obtindre millors coeficients de predicció, amb R2P de 0,79 i 0,69, respectivament. En el segon article s'ha avaluat, per a l'estimació de la concentració de nutrients, l'ús d'imatges hiperespectrales en un rang (500-980 nm). Els resultats van mostrar la predicció dels macronutrients com a nitrogen (N), P, potassi (K), Ca i Mg amb R2P de 0,80 a 0,62 i, per als micronutrients, només en el B es va obtindre un valor acceptable per a l'estimació (R2p 0,69). A més, utilitzant el mètode de reducció de variables d'influència en la projecció (VIP) es va obtindre una predicció fiable per als nutrients de N (R2P 0,76) i B (R2P 0,61). En el segon capítol, s'ha estudiat un altre cultiu emblemàtic a la Comunitat Valenciana d'importància econòmica, com són els cítrics. En este cas, es van desenvolupar ferramentes d'estimació del cv. 'clementina de Nules' compreses en dos articles (III i IV). De tal manera, que en el tercer article s'ha estudiat la capacitat de les tècniques espectromètriques per a determinar la concentració de nutrients en un cicle vegetatiu complet. Els resultats van mostrar una predicció amb un R2P de 0,70 a 0,65 per al P, K, Ca i B. Utilitzant el coeficient de regressió de pes (BW) es va determinar un subconjunt de bandes més influents per als nutrients P, K i B. Els resultats van mostrar que les bandes de major importància, per a estos nutrients, es situen a la regió del Vis (430-750 nm), el qual està associada a l'absorció de pigments fotosintètics. Finalment, en el quart article s'ha estudiat el potencial de les HSI per a discriminar fulles joves de fulles de cicles vegetatius anteriors, la qual cosa milloraria el diagnòstic atés que les taules de referència en este cultiu estan realitzades en fulles de la brotada de primavera. Posteriorment es va realitzar la predicció de concentració de nutrients de fulles joves, utilitzant 49 bandes espectrals, obtenint millors resultats per als nutrients P, K, Ca, ferro (Fe) i Mn amb R2P de 0,69 a 0,60. A més, es va realitzar la predicció d'estos nutrients minimitzant el nombre de bandes a deu, amb el BW i es va obtindre un R2P de 0,67 a 0,57. / [EN] Knowledge of the nutritional status of crops allows for correcting or adjusting any nutritional excess or deficiency throughout their vegetative cycle, ensuring high yields in production and optimal fruit quality. Traditionally, the analysis of the ionomics of different plant organs has been used for nutritional diagnosis, especially the leaves, due to their ease of sampling and being the photosynthetic organ par excellence in plants. These analyses are carried out by expensive conventional laboratory methods that are destructive, polluting, time-consuming and costly. Therefore, it is necessary to implement sustainable strategies that allow the fertilisation dose to be adjusted according to the crop's needs with the minimum risk of contamination. This doctoral thesis aims to develop methods and models for nutritional diagnosis prediction in crops using non-destructive optical methods, such as Vis-NIR spectroscopy, combined with chemometric techniques. Thus, the first chapter of the publications focuses on cultivating persimmon cv. 'Rojo Brillante', comprising two articles (I and II). In the first of these articles, the potential of Vis-NIR spectroscopy (430-1040 nm) was studied to predict macronutrients and micronutrients using PLS regression models. This study applied differential treatments for N nutrients (0 %, 33 %, 50 % and 100 %) and K2O (0 %, 50 % and 100 %) of crop demand. The results showed that it is possible to accurately predict macronutrients such as phosphorus (P), calcium (Ca) and magnesium (Mg), with a coefficient of determination in the prediction (R2P) of 0.78 to 0.63. Boron (B) and manganese (Mn) obtained the best micronutrient prediction coefficients, with R2P of 0.79 and 0.69, respectively. The second article evaluated hyperspectral imaging (HSI) in the range (500-980 nm) for nutrient concentration estimation. The results showed the prediction of macronutrients such as nitrogen (N), P, potassium (K), Ca and Mg with R2P from 0.80 to 0.62 and, for micronutrients, only in B, an acceptable value for the estimation was obtained (R2p 0.69). In addition, using the projection influence variable reduction (VIP) method, a reliable prediction was obtained for N (R2P 0.76) and B (R2P 0.61) nutrients. In the second chapter, another emblematic crop of economic importance in the Valencian Community, citrus, was studied. Estimation tools were developed for citrus cv. 'Clementina de Nules' and the results were published in two articles (III and IV). Thus, in the third article, the capacity of spectrometric techniques to determine the concentration of nutrients in a complete vegetative cycle was studied. The results showed prediction with an R2P of 0.70 to 0.65 for P, K Ca and B. Using the weight regression coefficient (BW), a subset of more influential bands was determined for P, K and B nutrients. The results showed that the bands of greatest importance for these nutrients are located in the Vis region (430-750 nm), which is associated with photosynthetic pigment uptake. Finally, in the fourth article, the potential of HSI to discriminate young leaves from leaves of previous vegetative cycles has been studied, which would improve the diagnosis given that the reference tables in this crop are made on leaves of spring sprouting. Subsequently, the prediction of nutrient concentration of young leaves was carried out using 49 spectral bands, obtaining better results for the nutrients P, K, Ca, iron (Fe) and Mn with R2P from 0.69 to 0.60. In addition, these nutrients were predicted by minimizing the number of bands to ten, with the BW and an R2P of 0.67 to 0.57. ¿ / Maylin Acosta thanks IFARHU-SENACYT for the Professional Excellence Scholarships, contract No. 270-2021-020. Sandra Munera thanks the Juan de la Cierva-Formación contract (FJC2021-047786-I) co-funded by MCIN/AEI/10.13039/501100011033 and EU NextGenerationEU/PRTR. This work is co-funded by MICIN-AEI through project TED2021-130117B-C31, GVA-IVIA through projects 52203 and 52204, and the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2021–2027. / Acosta Tello, MO. (2024). Predicción de macro y micronutrientes en hojas de cítricos y caqui utilizando métodos ópticos no destructivos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/207010 / Compendio