Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Abstract Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Results Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems

Monte Carlo Simulation Approach to Soil Layer Resistivity Modelling for Grounding System Design

Soil layer resistivity modelling is a vital component of grounding system design. Grounding system for facility, equipment, power station and general system protection purposes must be designed to be able to handle the anticipated level of fault current. To achieve this; the earth rods, mats and any other equivalent alternatives deployed must be adequately sized in terms of the physical dimensions and the number of such rods required in order to achieve the desired low, overall grounding system resistance. The resistance to earth of a grounding system is a function of the resistivity of the soil in concern, and to ensure appropriate design, the resistivity profile of the soil must be determined via appropriate soil modelling. This paper presents a Monte Carlo simulation approach to two layer soil modelling using the square error as an optimization function. The result of the simulation shows an improvement in model accuracy, and it also conforms significantly with the results of published works that applied genetic algorithm

Crop Phenotyping of Sorghum bicolors Physiological Response to Salt-Affected Soils Using TLS and GPR Remote Sensing Technologies in Nevada Drylands

Saline and sodic soils are major abiotic stressors on the production of flood-irrigated crops in drylands. We conducted a crop phenotyping, remote sensing study on five genotypes of sorghum [Sorghum bicolor (L.) Moench], a drought and salt-tolerant crop, to assist in the molecular breeding of salt-tolerant cultivars. A control plot and a spatially heterogeneous saline-sodic plot (treatment plot) were established in collaboration with Dr. Yerka, Mr. Alfredo Delgado, Dr. Washington-Allen, the Nevada Agricultural Experiment Station (NAES) and the United States Department of Agriculture’s Plant Materials Center (USDA-PMC) in Fallon, Nevada. This location is representative of the variable salinity/sodicity conditions typical of Northern Nevada soils and associated belowground biomass dynamics in drylands. We generated pre- and post-harvest soil attribute maps of the treatment plot using spatial interpolation, we expected individual genotypes to be affected differently by the gradient of various soil constituents. We hypothesized that above- and belowground three-dimensional structural phenology of the five genotypes would be differently affected across the salinity gradient in the treatment plot relative to the control plot. Additionally, we hypothesized that the GPR signal return would vary with the salinity gradient. Finally, we expected an increase in belowground biomass, relative to the control plot, in response to salt-stress as an adaptation to drought. The phenology of coarse-root depth and three-dimensional structure from pre-planting to harvest was non-invasively measured 15 times using a real-time kinematic (RTK) GPS-mounted IDS GeoRadar dual channel (400MHz and 900MHz) ground penetrating radar (GPR) system. Plant height and three-dimensional structural phenology of the five varieties were mapped using a FARO Focus3D X 330 terrestrial laser scanner (TLS). We found differences in above- and belowground three-dimensional structural phenology across the five genotypes in response to the salinity and sodicity gradient. Of the five genotypes in this study, only four emerged in the treatment plot, where Richardson Seed’s Ultra-Early Hybrid performed best under the gradient of salinity and sodicity with the highest rate of emergence (68%), the highest rate of panicle production (4.1 panicles per row), and the greatest panicle volume (67.2%) relative to the control plot. Furthermore, we found that the GPR return signal was not able to detect root mass in the highly saline-sodic soil, however, I was able to detect root mass phenology in the control plot. GPR return signal was not linear in response to the salinity gradient, however, a signal pattern emerged from the different salinity ranges suggesting a gradient response. This study shows the efficacy of the use of these technologies in crop phenotyping and precision agriculture. Future work may improve TLS derived data processing efficiency by developing methods for automating the detection of phenotypic traits (e.g., panicles, leaf area index, number of individual plants). These methods likely will include machine learning algorithms, allometric equations for biomass calculations, and use of drone-mounted LiDAR to reduce occlusion. The use of GPR in the salt-affected soils of this study was not able to definitively identify root mass, however, its use in soil composition for salts and other constituents is indeed promising. Further testing of GPR’s non-detect threshold in salt-affected soils and its ability to quantify individual soil constituents has potential to be highly valuable to the field of soil science and precision agriculture. Furthermore, this study was able to detect a root mass response using GPR, future work may focus on differentiating genotypic variation in root phenology

Optimal identification of unknown groundwater contaminant sources in conjunction with designed monitoring networks

Human activities and improper management practices have resulted in widespread deterioration of groundwater quality worldwide. Groundwater contamination has seriously threatened its beneficial use in recent decades. Remediation processes are necessary for groundwater management. In the remediation of contaminated aquifer sites, identification of unknown groundwater contaminant sources has a crucial role. In other words, an effective groundwater remediation process needs an accurate identification of contaminant sources in terms of contaminant source locations, magnitudes and time-release. On the other hand, the efficiency and reliability of contaminant source identification depend on the availability, adequacy, and accuracy of hydrogeologic information and contaminant concentration measurements data. Whereas, generally when groundwater contaminations are detected, only limited and sparse measured contaminant concentration values are available. Usually, groundwater contaminations are detected after a long time, years or even decades after the starting of contaminant source activities or even after their extinction. Therefore, usually, there is not enough information regarding the number of contaminant sources, the duration of sources' activities and the contaminant magnitudes, as well as the hydrogeologic parameters of the contaminated aquifers. Simulations of groundwater flow and solute transport involve intrinsic uncertainties due to this sparse information or lack of enough hydrogeologic information of the porous medium. Therefore, for groundwater management, developing and applying an efficient procedure for identification of unknown contaminant sources is essential. Moreover, available observed contaminant concentration values are usually erroneous and this erroneous data could cause instability in the solution results. Various combinations of source characteristics can result in similar effects at observation locations and cause non-uniqueness in the solution. Due to these instabilities and non-uniqueness in solution (Datta, 2002), the source identification problem is known as an "ill-posed problem" (Yeh, 1986). The non-uniqueness and uncertainties involved in this ill-posed problem make this problem a difficult and complex task. Suggested methodologies to tackle this task are not completely efficient. For instance, the crux of previous approaches is highly vulnerable to the accuracy and adequacy of contaminant concentration measurements and hydrogeologic data. As a result, many of the previously suggested approaches are not applicable to real-world cases and application of relevant approaches to real-world contaminant aquifer sites is usually tedious and time-consuming. The suggested methodologies involve enormous computational time and cost due to repeated runs of the numerical simulation models within the optimisation algorithms. Therefore, to identify the unknown characteristics of contaminant sources, different surrogate models were developed. Three different algorithms were utilized for developing the surrogate models: Self-Organising Maps (SOM), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS). Performance of the developed procedures was assessed for potential applicability in two hypothetical, an experimental, and a real-world contaminated aquifer sites. In the used contaminated aquifer sites, only limited contaminant concentrations data were assumed to be available. In three cases, it was also assumed that the contaminant concentrations data were collected a long time after the start of the first potential contaminant source activities. The performance evaluations of the developed surrogate models show that these models could accurately mimic the behaviour of simulation models of groundwater flow and solute transport. These surrogate models solutions showed acceptable errors in comparison to the more robust numerical model solutions. These surrogate models were also used for identification of unknown groundwater contaminant sources when utilized to solve the inverse problem. The SOM algorithm was chosen as the surrogate model type in this study for directly addressing the source identification problem as well. The SOM algorithm was chosen for its classification capabilities. In source identification problems, the number of actual contaminant sources is uncertain and usually, a set of a larger number of potential contaminant sources are assumed. Therefore, screening the active sources by SOM-based Surrogate Models (SOM-based SMs) may simplify the source identification problems. The performance of the developed SOM-based SMs was assessed for different scenarios. Results indicate that the developed models could also accurately screen the active sources among all potential contaminant sources with sparse contaminant concentrations data and uncertain hydrogeologic information. For comparison purposes, MARS and GPR algorithms that are precise prediction tools were also utilized for developing MARS and GPR-based Surrogate Models (MARS and GPR-based SM) for source identification. Performance of the developed surrogate models for source identification was evaluated in terms of Normalized Absolute Error of Estimation (NAEE). For example, the performance of the developed SOM, MARS and GPR-based SMs was assessed in an illustrative hypothetical contaminated aquifer site. The results for testing data in terms of NAEE were equal to 16.3, 4.9 and 6.6%, respectively. Performance of the developed SOM, MARS and GPR-based SMs was also evaluated in an experimental contaminated aquifer site. The results for testing data in terms of NAEE were equal to 15.8, 14.1 and 16.2%. These performance evaluation results of the developed surrogate models indicate that the MARS-based SMs can be more accurate models than the SOM and GPR-based SMs in source identification problems. The most important advantage of the developed methodologies is their direct application for source identification in an inverse mode without linking to an optimisation model. Surrogate Model-Based Optimisation (SMO) was also developed and utilized for source identification. In this developed SMO, MARS and Genetic Algorithm (GA) were utilized as the surrogate model and the optimisation model types, respectively. MARS-based SMOs performance was assessed in an illustrative hypothetical contaminated aquifer site and in a real-world contaminated aquifer site. The result of the developed MARS-based SMO for testing data in the illustrative hypothetical contaminated aquifer site in terms of Root Mean Square Error (RMSE) was equal to 0.92. Obtained solution results of the developed MARS-based SM in the real contaminated study area for testing data in terms of RMSE was equal to 42.5. The performance evaluation results of the developed methodologies in different hypothetical and real contaminated study areas demonstrate the capabilities of the constructed SOM, GPR, and MARS-based SMs and MARS-based SMO for source identification. Also, in order to increase the accuracy of source identification results, and based on the preliminary solution results of the developed SOM-based SMs, a sequential sampling method can be applied adaptively for updating the developed surrogate models. Information from a hypothetical contaminated aquifer site was used to assess the performance of this procedure. Performance evaluation results of adaptively developed MARS and GPR-based SMs in terms of NAEE were equal to 1.9 and 2.1%, respectively. The results show 3 and 4.5% improvements for source identification results by applying adaptively developed MARS and GPR-based SMs, respectively. Another difficulty with source identification problems has been the limitation and sparsity of observed contaminant concentrations data. Previously suggested methodologies usually need long-term observation data at numerous locations which can involve large costs. Therefore, developing an effective monitoring network design procedure was one of the main goals of this study. In designing the monitoring networks, two main objectives were considered: 1. Maximizing the accuracy of source identification results, and 2. Limiting the number of monitoring locations. It was supposed that by implementing obtained results from the designed monitoring networks for developing surrogate models, the source identification results would significantly improve. In this study, different algorithms were utilized to identify potentially important and effective monitoring locations which probably could improve source identification results. These algorithms are Random Forests (RF), Tree Net (TN) and CART. The performance of these algorithms was evaluated in different scenarios. Results indicate the potential applicability of these algorithms in recognising the most important components of prediction models. As a result, these algorithms could apply for designing monitoring networks for improving the source identification efficiency and accuracy. Concentration measurement information from a designed monitoring network and from a set of arbitrary monitoring sites was utilized to develop MARS-based surrogate models for source identification. The solution results for these two scenarios of designed monitoring and arbitrary measurements were compared for a hypothetical study area for evaluation purpose. Performance evaluation results of the developed surrogate model using information from the designed monitoring network showed improvement in source identification error in terms of RMSE for testing data by 0.7. The obtained information from the designed monitoring network was used to develop MARSbased SM for source identification of testing data in a real contaminated aquifer site. Source identification results of the developed MARS-based SM with testing data for the real contaminated aquifer site showed improvement by 35.3 in terms of RMSE compared to the solution results of MARS-based SM, which was developed by using obtained information from arbitrary monitoring locations. Performance evaluation results for the developed monitoring network procedure demonstrate the potential applicability of this procedure for source identification

Characterisation of the subglacial environment using geophysical constrained Bayesian inversion techniques

An accurate characterization of the inaccessible subglacial environment is key to accurately modelling the dynamic behaviour of ice sheets and glaciers, crucial for predicting sea-level rise. The composition and water content of subglacial material can be inferred from measurements of shear wave velocity (Vs) and bulk electrical resistivity (R), themselves derived from Rayleigh wave dispersion curves and transient electromagnetic (TEM) soundings. Conventional Rayleigh wave and TEM inversions can suffer from poor resolution and non-uniqueness. In this thesis, I present a novel constrained inversion methodology which applies a Markov chain Monte Carlo implementation of Bayesian inversion to produce probability distributions of geophysical parameters. MuLTI (Multimodal Layered Transdimensional Inversion) is used to derive Vs from Rayleigh wave dispersion curves, and its TEM variant, MuLTI-TEM, for evaluating bulk electrical resistivity. The methodologies can include independent depth constraints, drawn from external data sources (e.g., boreholes or other geophysical data), which significantly improves the resolution compared to conventional unconstrained inversions. Compared to such inversions, synthetic studies suggested that MuLTI reduces the error between the true and best-fit models by a factor of 10, and reduces the vertically averaged spread of the Vs distribution twofold, based on the 95% credible intervals. MuLTI and MuLTI-TEM were applied to derive Vs and R profiles from seismic and TEM electromagnetic data acquired on the terminus of the Norwegian glacier Midtdalsbreen. Three subglacial material classifications were determined: sediment (Vs 1600 m/s, R > 500 Ωm) and weathered/fractured bedrock containing saline water (Vs > 1900 m/s, R < 50 Ωm). These algorithms offer a step-change in our ability to resolve and quantify the uncertainties in subsurface inversions, and show promise for constraining the properties of subglacial aquifers beneath Antarctic ice masses. MuLTI and MuLTITEM have both been made publicly available via GitHub to motivate users, in the cryosphere and other environmental settings, for continued advancement

Advanced Feature Learning and Representation in Image Processing for Anomaly Detection

Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved

Unlocking the benefits of spaceborne imaging spectroscopy for sustainable agriculture

With the Environmental Mapping and Analysis Program (EnMAP) mission, launched on April 1st 2022, new opportunities unfold for precision farming and agricultural monitoring. The recurring acquisition of spectrometric imagery from space, contiguously resolving the electromagnetic spectrum in the optical domain (400—2500 nm) within close narrow bands, provides unprecedented data about the interaction of radiation with biophysical and biochemical crop constituents. These interactions manifest in spectral reflectance, carrying important information about crop status and health. This information may be incorporated in agricultural management systems to support necessary efforts to maximize yields against the backdrop of an increased food demand by a growing world population. At the same time, it enables the effective optimization of fertilization and pest control to minimize environmental impacts of agriculture. Deriving biophysical and biochemical crop traits from hyperspectral reflectance thereby always relies on a model. These models are categorized into (1) parametric, (2) nonparametric, (3) physically-based, and (4) hybrid retrieval schemes. Parametric methods define an explicit parameterized expression, relating a number of spectral bands or derivates thereof with a crop trait of interest. Nonparametric methods comprise linear techniques, such as principal component analysis (PCA) which addresses collinearity issues between adjacent bands and enables compression of full spectral information into dimensionality reduced, maximal informative principal components (PCs). Nonparametric nonlinear methods, i.e., machine learning (ML) algorithms apply nonlinear transformations to imaging spectroscopy data and are therefore capable of capturing nonlinear relationships within the contained spectral features. Physically-based methods represent an umbrella term for radiative transfer models (RTMs) and related retrieval schemes, such as look-up-table (LUT) inversion. A simple, easily invertible and specific RTM is the Beer-Lambert law which may be used to directly infer plant water content. The most widely used general and invertible RTM is the one-dimensional canopy RTM PROSAIL, which is coupling the Leaf Optical Properties Spectra model PROSPECT and the canopy reflectance model 4SAIL: Scattering by Arbitrarily Inclined Leaves. Hybrid methods make use of synthetic data sets created by RTMs to calibrate parametric methods or to train nonparametric ML algorithms. Due to the ill-posed nature of RTM inversion, potentially unrealistic and redundant samples in a LUT need to be removed by either implementing physiological constraints or by applying active learning (AL) heuristics. This cumulative thesis presents three different hybrid approaches, demonstrated within three scientific research papers, to derive agricultural relevant crop traits from spectrometric imagery. In paper I the Beer-Lambert law is applied to directly infer the thickness of the optically active water layer (i.e., EWT) from the liquid water absorption feature at 970 nm. The model is calibrated with 50,000 PROSPECT spectra and validated over in situ data. Due to separate water content measurements of leaves, stalks, and fruits during the Munich-North-Isar (MNI) campaigns, findings indicate that depending on the crop type and its structure, different parts of the canopy are observed with optical sensors. For winter wheat, correlation between measured and modelled water content was most promising for ears and leaves, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26%, and in the case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These results led to the general recommendation to collect destructive area-based plant organ specific EWT measurements instead of the common practice to upscale leaf-based EWT measurements to canopy water content (CWC) by multiplication of the leaf area index (LAI). The developed and calibrated plant water retrieval (PWR) model proved to be transferable in space and time and is ready to be applied to upcoming EnMAP data and any other hyperspectral imagery. In paper II the parametric concept of spectral integral ratios (SIR) is introduced to retrieve leaf chlorophyll a and b content (Cab), leaf carotenoid content (Ccx) and leaf water content (Cw) simultaneously from imaging spectroscopy data in the wavelength range 460—1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The approach was validated over a physiologically constrained PROSAIL simulated database, considering natural Ccx-Cab relations and green peak locations. Validation on airborne spectrometric HyMAP data achieved satisfactory results for Cab (R2 = 0.84; RMSE = 9.06 µg cm-2) and CWC (R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Mapping of the SIR results as multiband images (3-segment SIR) allows for an intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is applicable on satellite imaging spectroscopy data. In paper III a hybrid workflow is presented, combining RTM with ML for inferring crop carbon content (Carea) and aboveground dry and fresh biomass (AGBdry, AGBfresh). The concept involves the establishment of a PROSAIL training database, dimensionality reduction using PCA, optimization in the sampling domain using AL against the 4-year MNI campaign dataset, and training of Gaussian process regression (GPR) ML algorithms. Internal validation of the GPR-Carea and GPR-AGB models achieved R2 of 0.80 for Carea, and R2 of 0.80 and 0.71 for AGBdry and AGBfresh, respectively. Validation with an independent dataset, comprising airborne AVIRIS NG imagery (spectrally resampled to EnMAP) and in situ measurements, successfully demonstrated mapping capabilities for both bare and green fields and generated reliable estimates over winter wheat fields at low associated model uncertainties (< 40%). Overall, the proposed carbon and biomass models demonstrate a promising path toward the inference of these crucial variables over cultivated areas from upcoming spaceborne hyperspectral acquisitions, such as from EnMAP. As conclusions, the following important findings arise regarding parametric and nonparametric hybrid methods as well as in view of the importance of in situ data collection. (1) Uncertainties within the RTM PROSAIL should always be considered. A possible reduction of these uncertainties is thereby opposed to the invertibility of the model and its intended simplicity. (2) Both physiological constraints and AL heuristics should be applied to reduce unrealistic parameter combinations in a PROSAIL calibration or training database. (3) State-of-the-art hybrid ML approaches with the ability to provide uncertainty intervals are anticipated as most promising approach for solving inference problems from hyperspectral Earth observation data due to their synergistic use of RTMs and the high flexibility, accuracy and consistency of nonlinear nonparametric methods. (4) Parametric hybrid approaches, due to their algorithmic transparency, enable deeper insights into fundamental physical limitations of optical remote sensing as compared to ML approaches. (5) Integration-based indices that make full use of available hyperspectral information may serve as physics-aware dimensionality reduced input for ML algorithms to either improve estimations or to serve as endmember for crop type discrimination when additional time series information is available. (6) The validation of quantitative model-based estimations is crucial to evaluate and improve their performance in terms of the underlying assumptions, model parameterizations, and input data. (7) In the face of soon-to-be-available EnMAP data, collection of in situ data for validation of retrieval methods should aim at high variability of measured crop types, high temporal variability over the whole growing season, as well as include area- and biomass-based destructive measurements instead of LAI-upscaled leaf measurements. Provided the perfect functionality of the payload instruments, the success of the EnMAP mission and the here presented methods depend critically on a low-noise, accurate atmospherically corrected reflectance product. High-level outputs of the retrieval methods presented in this thesis may be incorporated into agricultural decision support systems for fertilization and irrigation planning, yield estimation, or estimation of the soil carbon sequestration potential to enable a sustainable intensive agriculture in the future.Mit der am 1. April 2022 gestarteten Satellitenmission Environmental Mapping and Analysis Program (EnMAP) eröffnen sich neue Möglichkeiten für die Präzisionslandwirtschaft und das landwirtschaftliche Monitoring. Die wiederkehrende Erfassung spektrometrischer Bilder aus dem Weltraum, welche das elektromagnetische Spektrum im optischen Bereich (400—2500 nm) innerhalb von engen, schmalen Bändern zusammenhängend auflösen, liefert nie dagewesene Daten über die Interaktionen von Strahlung und biophysikalischen und biochemischen Pflanzenbestandteilen. Diese Wechselwirkungen manifestieren sich in der spektralen Reflektanz, die wichtige Informationen über den Zustand und die Gesundheit der Pflanzen enthält. Vor dem Hintergrund einer steigenden Nachfrage nach Nahrungsmitteln durch eine wachsende Weltbevölkerung können diese Informationen in landwirtschaftliche Managementsysteme einfließen, um eine notwendige Ertragsmaximierung zu unterstützen. Gleichzeitig können sie eine effiziente Optimierung der Düngung und Schädlingsbekämpfung ermöglichen, um die Umweltauswirkungen der Landwirtschaft zu minimieren. Die Ableitung biophysikalischer und biochemischer Pflanzeneigenschaften aus hyperspektralen Reflektanzdaten ist dabei immer von einem Modell abhängig. Diese Modelle werden in (1) parametrische, (2) nichtparametrische, (3) physikalisch basierte und (4) hybride Ableitungsmethoden kategorisiert. Parametrische Methoden definieren einen expliziten parametrisierten Ausdruck, der eine Reihe von Spektralkanälen oder deren Ableitungen mit einem Pflanzenmerkmal von Interesse in Beziehung setzt. Nichtparametrische Methoden umfassen lineare Techniken wie die Hauptkomponentenanalyse (PCA). Diese adressieren Kollinearitätsprobleme zwischen benachbarten Kanälen und komprimieren die gesamte Spektralinformation in dimensionsreduzierte, maximal informative Hauptkomponenten (PCs). Nichtparametrische nichtlineare Methoden, d. h. Algorithmen des maschinellen Lernens (ML), wenden nichtlineare Transformationen auf bildgebende Spektroskopiedaten an und sind daher in der Lage, nichtlineare Beziehungen innerhalb der enthaltenen spektralen Merkmale zu erfassen. Physikalisch basierte Methoden sind ein Oberbegriff für Strahlungstransfermodelle (RTM) und damit verbundene Ableitungsschemata, d. h. Invertierungsverfahren wie z. B. die Invertierung mittels Look-up-Table (LUT). Ein einfaches, leicht invertierbares und spezifisches RTM stellt das Lambert-Beer'sche Gesetz dar, das zur direkten Ableitung des Wassergehalts von Pflanzen verwendet werden kann. Das am weitesten verbreitete, allgemeine und invertierbare RTM ist das eindimensionale Bestandsmodell PROSAIL, eine Kopplung des Blattmodells Leaf Optical Properties Spectra (PROSPECT) mit dem Bestandsreflexionsmodell 4SAIL (Scattering by Arbitrarily Inclined Leaves). Bei hybriden Methoden werden von RTMs generierte, synthetische Datenbanken entweder zur Kalibrierung parametrischer Methoden oder zum Training nichtparametrischer ML-Algorithmen verwendet. Aufgrund der Äquifinalitätsproblematik bei der RTM-Invertierung, müssen potenziell unrealistische und redundante Simulationen in einer solchen Datenbank durch die Implementierung natürlicher physiologischer Beschränkungen oder durch die Anwendung von Active Learning (AL) Heuristiken entfernt werden. In dieser kumulativen Dissertation werden drei verschiedene hybride Ansätze zur Ableitung landwirtschaftlich relevanter Pflanzenmerkmale aus spektrometrischen Bilddaten vorgestellt, die anhand von drei wissenschaftlichen Publikationen demonstriert werden. In Paper I wird das Lambert-Beer'sche Gesetz angewandt, um die Dicke der optisch aktiven Wasserschicht (bzw. EWT) direkt aus dem Absorptionsmerkmal von flüssigem Wasser bei 970 nm abzuleiten. Das Modell wird mit 50.000 PROSPECT-Spektren kalibriert und anhand von In-situ-Daten validiert. Aufgrund separater Messungen des Wassergehalts von Blättern, Stängeln und Früchten während der München-Nord-Isar (MNI)-Kampagnen, zeigen die Ergebnisse, dass je nach Kulturart und -struktur, unterschiedliche Teile des Bestandes mit optischen Sensoren beobachtet werden können. Bei Winterweizen wurde die höchste Korrelation zwischen gemessenem und modelliertem Wassergehalt für Ähren und Blätter erzielt und sie erreichte Bestimmtheitsmaße (R2) von bis zu 0,72 bei einem relativen RMSE (rRMSE) von 26%, bei Mais entsprechend nur für die Blattfraktion (R2 = 0,86, rRMSE = 23%). Diese Ergebnisse führten zu der allgemeinen Empfehlung, Kompartiment-spezifische EWT-Bestandsmessungen zu erheben, anstatt der üblichen Praxis, blattbasierte EWT-Messungen durch Multiplikation mit dem Blattflächenindex (LAI) auf den Bestandswassergehalt (CWC) hochzurechnen. Das entwickelte und kalibrierte Modell zur Ableitung des Pflanzenwassergehalts (PWR) erwies sich als räumlich und zeitlich übertragbar und kann auf bald verfügbare EnMAP-Daten und andere hyperspektrale Bilddaten angewendet werden. In Paper II wird das parametrische Konzept der spektralen Integralratios (SIR) eingeführt, um den Chlorophyll a- und b-Gehalt (Cab), den Karotinoidgehalt (Ccx) und den Wassergehalt (Cw) simultan aus bildgebenden Spektroskopiedaten im Wellenlängenbereich 460-1100 nm zu ermitteln. Das SIR-Konzept basiert auf der automatischen Separierung der jeweiligen Absorptionsmerkmale durch lokale Maxima- und Schnittpunkt-Analyse zwischen log-transformierter Reflektanz und konvexen Hüllen. Der Ansatz wurde anhand einer physiologisch eingeschränkten PROSAIL-Datenbank unter Berücksichtigung natürlicher Ccx-Cab-Beziehungen und Positionen der Maxima im grünen Wellenlängenbereich validiert. Die Validierung mit flugzeuggestützten spektrometrischen HyMAP-Daten ergab zufriedenstellende Ergebnisse für Cab (R2 = 0,84; RMSE = 9,06 µg cm-2) und CWC (R2 = 0,70; RMSE = 0,05 cm). Die ermittelten Ccx-Werte wurden anhand einer Plausibilitätsanalyse entsprechend der Cab-Ccx-Abhängigkeit als sinnvoll bewertet. Die Darstellung der SIR-Ergebnisse als mehrkanalige Bilder (3 segment SIR) ermöglicht zudem eine auf die drei betrachteten biochemischen Variablen bezogene, intuitive Visualisierung der dominanten Absorptionen. Der vorgestellte SIR-Algorithmus ermöglicht somit wenig rechenintensive und RTM-gestützte robuste Ableitungen der beiden wichtigsten Pigmente sowie des Wassergehalts und kann in auf jegliche zukünftig verfügbare Hyperspektraldaten angewendet werden. In Paper III wird ein hybrider Ansatz vorgestellt, der RTM mit ML kombiniert, um den Kohlenstoffgehalt (Carea) sowie die oberirdische trockene und frische Biomasse (AGBdry, AGBfresh) abzuschätzen. Das Konzept umfasst die Erstellung einer PROSAIL-Trainingsdatenbank, die Dimensionsreduzierung mittels PCA, die Reduzierung der Stichprobenanzahl mittels AL anhand des vier Jahre umspannenden MNI-Kampagnendatensatzes und das Training von Gaussian Process Regression (GPR) ML-Algorithmen. Die interne Validierung der GPR-Carea und GPR-AGB-Modelle ergab einen R2 von 0,80 für Carea und einen R2 von 0,80 bzw. 0,71 für AGBdry und AGBfresh. Die Validierung auf einem unabhängigen Datensatz, der flugzeuggestützte AVIRIS-NG-Bilder (spektral auf EnMAP umgerechnet) und In-situ-Messungen umfasste, zeigte erfolgreich die Kartierungsfähigkeiten sowohl für offene Böden als auch für grüne Felder und führte zu zuverlässigen Schätzungen auf Winterweizenfeldern bei geringen Modellunsicherheiten (< 40%). Insgesamt zeigen die vorgeschlagenen Kohlenstoff- und Biomassemodelle einen vielversprechenden Ansatz auf, der zur Ableitung dieser wichtigen Variablen über Anbauflächen aus künftigen weltraumgestützten Hyperspektralaufnahmen wie jenen von EnMAP genutzt werden kann. Als Schlussfolgerungen ergeben sich die folgenden wichtigen Erkenntnisse in Bezug auf parametrische und nichtparametrische Hybridmethoden sowie bezogen auf die Bedeutung der In-situ-Datenerfassung. (1) Unsicherheiten innerhalb des RTM PROSAIL sollten immer berücksichtigt werden. Eine mögliche Verringerung dieser Unsicherheiten steht dabei der Invertierbarkeit des Modells und dessen beabsichtigter Einfachheit entgegen. (2) Sowohl physiologische Einschränkungen als auch AL-Heuristiken sollten angewendet werden, um unrealistische Parameterkombinationen in einer PROSAIL-Kalibrierungs- oder Trainingsdatenbank zu reduzieren. (3) Modernste ML-Ansätze mit der Fähigkeit, Unsicherheitsintervalle bereitzustellen, werden als vielversprechendster Ansatz für die Lösung von Inferenzproblemen aus hyperspektralen Erdbeobachtungsdaten aufgrund ihrer synergetischen Nutzung von RTMs und der hohen Flexibilität, Genauigkeit und Konsistenz nichtlinearer nichtparametrischer Methoden angesehen. (4) Parametrische hybride Ansätze ermöglichen aufgrund ihrer algorithmischen Transparenz im Vergleich zu ML-Ansätzen tiefere Einblicke in die grundlegenden physikalischen Grenzen der optischen Fernerkundung. (5) Integralbasierte Indizes, die die verfügbare hyperspektrale Information voll ausschöpfen, können als physikalisch-basierte dimensionsreduzierte Inputs für ML-Algorithmen dienen, um entweder Schätzungen zu verbessern oder um als Eingangsdaten die verbesserte Unterscheidung von Kulturpflanzen zu ermöglichen, sobald zusätzliche Zeitreiheninformationen verfügbar sind. (6) Die Validierung quantitativer modellbasierter Schätzungen ist von entscheidender Bedeutung für die Bewertung und Verbesserung ihrer Leistungsfähigkeit in Bezug auf die zugrunde liegenden Annahmen, Modellparametrisierungen und Eingabedaten. (7) Angesichts der bald verfügbaren EnMAP-Daten sollte die Erhebung von In-situ-Daten zur Validierung von Ableitungsmethoden auf eine hohe Variabilität der gemessenen Pflanzentypen und eine hohe zeitliche Variabilität über die gesamte Vegetationsperiode abzielen sowie flächen- und biomassebasierte destruktive Messungen anstelle von LAI-skalierten Blattmessungen umfassen. Unter der Voraussetzung, dass die Messinstrumente perfekt funktionieren, hängt der Erfolg der EnMAP-Mission und der hier vorgestellten Methoden entscheidend von einem rauscharmen, präzise atmosphärisch korrigierten Reflektanzprodukt ab. Die Ergebnisse der in dieser Arbeit vorgestellten Methoden können in landwirtschaftliche Entscheidungsunterstützungssysteme für die Dünge- oder Bewässerungsplanung, die Ertragsabschätzung oder die Schätzung des Potenzials der Kohlenstoffbindung im Boden integriert werden, um eine nachhaltige Intensivlandwirtschaft in der Zukunft zu ermöglichen