Early detection is vital to successfully treating breast cancer, and mammography screening is the most efficient and wide-spread method to reach this goal. Imaging low-contrast targets, while minimizing the radiation exposure to a large population is, however, a major challenge. Optimizing the image quality per unit radiation dose is therefore essential. In this thesis, two optimization schemes with respect to x-ray photon energy have been investigated: filtering the incident spectrum with refractive x-ray optics (spectral shaping), and utilizing the transmitted spectrum with energy-resolved photon-counting detectors (spectral imaging). Two types of x-ray lenses were experimentally characterized, and modeled using ray tracing, field propagation, and geometrical optics. Spectral shaping reduced dose approximately 20% compared to an absorption-filtered reference system with the same signal-to-noise ratio, scan time, and spatial resolution. In addition, a focusing pre-object collimator based on the same type of optics reduced divergence of the radiation and improved photon economy by about 50%. A photon-counting silicon detector was investigated in terms of energy resolution and its feasibility for spectral imaging. Contrast-enhanced tumor imaging with a system based on the detector was characterized and optimized with a model that took anatomical noise into account. Improvement in an ideal-observer detectability index by a factor of 2 to 8 over that obtained by conventional absorption imaging was found for different levels of anatomical noise and breast density. Increased conspicuity was confirmed by experiment. Further, the model was extended to include imaging of unenhanced lesions. Detectability of microcalcifications increased no more than a few percent, whereas the ability to detect large tumors might improve on the order of 50% despite the low attenuation difference between glandular and cancerous tissue. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise. / QC 20100714

Die vorliegende Arbeit stellt die Ergebnisse der methodologischen Entwicklung räumlich aufgelöster Analyse mittels Scanning Laserablation-ICP-Massenspektrometrie dar. Eine neue Behandlung zur Quantifizierung transienter analytischer Signale wurde für die Wiederherstellung von Konzentrationsprofilen vorgeschlagen. Die Anwendung der entwickelten Modelle auf die räumlich aufgelöste Analyse mittels LA-ICP-MS ermöglicht verbesserten Informationsgewinn und lässt dadurch eine höhere räumliche Auflösung erreichen. Die Anwendbarkeit der LA-ICP-MS für die räumlich aufgelöste Bestimmung der Stöchiometrie in supraleitenden Borokarbiden wurde untersucht. Der Einfluss apparativer Größen auf das analytische Signal wurde aufgeklärt, um die Messbedingungen zu optimieren. Zusätzlich wurden Fraktionierungseffekte untersucht, um die Ursache und deren Auswirkung auf die Analyse supraleitender Borokarbiden zu erklären. / This work represents the results of the methodological development of spatially resolved analysis by scanning laser ablation ICP mass spectrometry. A new approach to the quantification of transient analytical signals was proposed to reveal the concentration profile. An application of the developed models on spatially resolved analysis by LA-ICP-MS allows to gain more information from experimental data and hence to achieve better spatial resolution. The applicability of LA-ICP-MS to the spatially resolved determination of the stoichiometry of superconducting borocarbides was investigated. The effect of experimental parameters on analytical signals was elucidated in order to optimize the experimental conditions. In addition, fractionation effects were investigated to identify the causes for fractionation and their influence on the analysis of superconducting borocarbides.

Η αντιμετώπιση της περιορισμένης χωρικής ανάλυσης των εικόνων, η οποία οφείλεται στους φυσικούς περιορισμούς που εμφανίζουν οι αισθητήρες σύλληψης εικόνας, αποτελεί το αντικείμενο μελέτης της παρούσας διδακτορικής διατριβής. Στη διατριβή αυτή αρχικά γίνεται προσπάθεια μοντελοποίησης της λειτουργίας του ψηφιοποιητή εικόνας κατά τη δημιουργία αντίγραφου ενός εγγράφου μέσω απλών μοντέλων. Στην εξομοίωση της λειτουργίας του ψηφιοποιητή, το προτεινόμενο μοντέλο θα πρέπει να προτιμηθεί έναντι των μοντέλων Gaussian και Cauchy, που συναντώνται στη βιβλιογραφία, καθώς είναι ισοδύναμο στην απόδοση, απλούστερο στην υλοποίηση και δεν παρουσιάζει εξάρτηση από συγκεκριμένα χαρακτηριστικά λειτουργίας του ψηφιοποιητή. Έπειτα, μορφοποιούνται νέες μέθοδοι για τη βελτίωση της χωρικής ανάλυσης σε εικόνες. Προτείνεται μέθοδος μη ομοιόμορφης παρεμβολής για ανακατασκευή εικόνας Super-Resolution (SR). Αποδεικνύεται πειραματικά πως η προτεινόμενη μέθοδος η οποία χρησιμοποιεί την παρεμβολή Kriging υπερτερεί της μεθόδου η οποία δημιουργεί το πλέγμα υψηλής ανάλυσης μέσω της σταθμισμένης παρεμβολής κοντινότερου γείτονα που αποτελεί συμβατική τεχνική. Επίσης, παρουσιάζονται τρεις νέες μέθοδοι για στοχαστική ανακατασκευή εικόνας SR regularized. Ο εκτιμητής Tukey σε συνδυασμό με το Bilateral Total Variation (BTV) regularization, ο εκτιμητής Lorentzian σε συνδυασμό με το BTV regularization και ο εκτιμητής Huber συνδυασμένος με το BTV regularization είναι οι τρεις μέθοδοι που προτείνονται. Μία πρόσθετη καινοτομία αποτελεί η απευθείας σύγκριση των τριών εκτιμητών Tukey, Lorentzian και Huber στην ανακατασκευή εικόνας super-resolution, άρα στην απόρριψη outliers. Η απόδοση των προτεινόμενων μεθόδων συγκρίνεται απευθείας με εκείνη μίας τεχνικής SR regularized που υπάρχει στη βιβλιογραφία, η οποία αποδεικνύεται κατώτερη. Σημειώνεται πως τα πειραματικά αποτελέσματα οδηγούν σε επαλήθευση της θεωρίας εύρωστης στατιστικής συμπεριφοράς. Επίσης, εκπονείται μία πρωτότυπη μελέτη σχετικά με την επίδραση που έχει κάθε ένας από τους όρους έκφρασης πιστότητας στα δεδομένα και regularization στη διαμόρφωση του αποτελέσματος της ανακατασκευής εικόνας SR. Τα συμπεράσματα που προκύπτουν βοηθούν στην επιλογή μίας αποτελεσματικής μεθόδου για ανακατασκευή εικόνας SR ανάμεσα σε διάφορες υποψήφιες μεθόδους για κάποια δεδομένη ακολουθία εικόνων χαμηλής ανάλυσης. Τέλος, προτείνεται μία μέθοδος παρεμβολής σε εικόνα μέσω νευρωνικού δικτύου. Χάρη στην προτεινόμενη τεχνική εκπαίδευσης το νευρωνικό δίκτυο μαθαίνει το point spread function του ψηφιοποιητή εικόνας. Τα πειραματικά αποτελέσματα αποδεικνύουν πως η προτεινόμενη μέθοδος υπερτερεί σε σχέση με τους κλασικούς αλγόριθμους δικυβικής παρεμβολής και παρεμβολής spline. Η τεχνική που προτείνεται εξετάζει για πρώτη φορά το ζήτημα της σειράς της παρουσίασης των δεδομένων εκπαίδευσης στην είσοδο του νευρωνικού δικτύου. / Coping with the limited spatial resolution of images, which is caused by the physical limitations of image sensors, is the objective of this thesis. Initially, an effort to model the scanner function when generating a document copy by means of simple models is made. In a task of scanner function simulation the proposed model should be preferred over the Gaussian and Cauchy models met in bibliography as it is equivalent in performance, simpler in implementation and does not present any dependence on certain scanner characteristics. Afterwards, new methods for improving images spatial resolution are formulated. A nonuniform interpolation method for Super-Resolution (SR) image reconstruction is proposed. Experimentation proves that the proposed method employing Kriging interpolation predominates over the method which creates the high-resolution grid by means of the weighted nearest neighbor interpolation that is a conventional interpolation technique. Also, three new methods for stochastic regularized SR image reconstruction are presented. The Tukey error norm in combination with the Bilateral Total Variation (BTV) regularization, the Lorentzian error norm in combination with the BTV regularization and the Huber error norm combined with the BTV regularization are the three proposed methods. An additional novelty is the direct comparison of the three estimators Tukey, Lorentzian and Huber in the task of super-resolution image reconstruction, thus in rejecting outliers. The performance of the proposed methods proves superior to that of a regularized SR technique met in bibliography. Experimental results verify the robust statistics theory. Moreover, a novel study which considers the effect of each one of the data-fidelity and regularization terms on the SR image reconstruction result is carried out. The conclusions reached help to select an effective SR image reconstruction method, among several potential ones, for a given low-resolution sequence of frames. Finally, an image interpolation method employing a neural network is proposed. The presented training procedure results in the network learning the scanner point spread function. Experimental results prove that the proposed technique predominates over the classical algorithms of bicubic and spline interpolation. The proposed method is novel as it treats, for the first time, the issue of the training data presentation order to the neural network input.

La disponibilité de nouveaux moyens d’acquisition en télédétection, satellitaire (PLEIADES, HYPXIM), aéroportée ou par drone (UAV) à très haute résolution spatiale ouvre la voie à leur utilisation pour l’étude de milieux complexes telles que les villes. En particulier, la connaissance de la ville pour l’étude des îlots de chaleur, la planification urbaine, l’estimation de la biodiversité de la végétation et son état de santé nécessite au préalable une étape de classification des matériaux qui repose sur l’utilisation de l’information spectrale accessible en télédétection hyperspectrale 0,4-2,5μm. Une des principales limitations des méthodes de classification réside dans le non traitement des zones à l’ombre. Des premiers travaux ont montré qu’il était possible d’exploiter l’information radiative dans les ombres des bâtiments. En revanche, les méthodes actuelles ne fonctionnent pas dans les ombres des arbres du fait de la porosité de leur couronne. L’objectif de cette thèse vise à caractériser les propriétés optiques de surface à l’ombre de la végétation arborée urbaine au moyen d’outils de transfert radiatif et de correction atmosphérique. L’originalité de ce travail est d’étudier la porosité d’un arbre via la grandeur de transmittance de la couronne. La problématique a donc été abordée en deux temps. Premièrement, la caractérisation de la transmittance d’un arbre isolé a été menée avec l’utilisation de l’outil DART à travers la mise en œuvre d’un plan d’expériences et d’études de sensibilité qui ont permis de la relier à des paramètres biophysiques et externes. Une campagne de mesures terrain a ensuite été réalisée afin d’évaluer son estimation à partir de différents niveaux de modélisation de l’arbre, dont un modèle réel acquis par mesures lidar terrestre. Deuxièmement, une nouvelle méthode de correction atmosphérique 3D adaptée à la végétation urbaine, ICARE-VEG, a été développée à partir des résultats précédents. Une campagne aéroportée et de mesures terrain UMBRA a été dédiée à sa validation. Ses performances comparées à d’autres outils existants ouvrent de larges perspectives pour l’interprétation globale d’une image par télédétection et pour souligner la complexité de modéliser des processus physiques naturels à une échelle spatiale très fine. / The new advances in remote sensing acquisitions at very high spatial resolution, either spaceborne (PLEIADES, HYPXIM), airborne or unmanned aerial vehicles borne, open the way for the study of complex environments such as urban areas. In particular, the better understanding of urban heat islands, urban planning, vegetation biodiversity, requires the knowledge of detailed material classification mapsbased on the use of spectral information brought by hyperspectral imagery 0.4-2.5μm. However, one of the main limitations of classification methods relies on the absence of shadow processing. Past studies have demonstrated that spectral information was possible to be extracted from shadows cast by buildings. But existing methods fail in shadows cast by trees because of their crown porosity. The objective of this thesis aims to characterize surface optical properties in urban tree shadows by means of radiative transfer and atmospheric correction tools. The originality of this work is to study the tree crown porosity through the analysis of the tree crown transmittance. Therefore, the issue has been divided into two parts. Firstly, an experimental design with the use of DART tool has been carried out in order to examine the relationships between the transmittance of an isolated tree and different biophysical and external variables. Then, the estimation of the tree crown transmittance has been assessed with several tree 3D modelling strategies derived from reference terrestrial lidar acquisitions. Secondly, a new atmospheric correction method appropriate to the processing of tree shadows, ICARE-VEG, was implemented fromthese previous results. An airborne and field campaign UMBRA was dedicated to its validation. Moreover, its performances was compared to other existing tools. Finally, the conclusions open large outlooks to the overall interpretation of remote sensing images and highlight the complexity to model physical natural processes with finer spatial resolutions.

Abstract<p>Since 1999, Very High spatial Resolution satellite data (Ikonos-2, QuickBird and OrbView-3) represent the surface of the Earth with more detail. However, information extraction by multispectral pixel-based classification proves to have become more complex owing to the internal variability increase in the land-cover units and to the weakness of spectral resolution. <p>Therefore, one possibility is to consider the internal spectral variability of land-cover classes as a valuable source of spatial information that can be used as an additional clue in characterizing and identifying land cover. Moreover, the spatial resolution gap that existed between satellite images and aerial photographs has strongly decreased, and the features used in visual interpretation transposed to digital analysis (texture, morphology and context) can be used as additional information on top of spectral features for the land cover classification.<p>The difficulty of this approach is often to transpose the visual features to digital analysis.<p>To overcome this problem region-based classification could be used. Segmentation, before classification, produces regions that are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each region becomes then a unit analysis, which makes it possible to avoid much of the structural clutter and allows to measure and use a number of features on top of spectral features. These features can be the surface, the perimeter, the compactness, the degree and kind of texture. Segmentation is one of the only methods which ensures to measure the morphological features (surface, perimeter.) and the textural features on non-arbitrary neighbourhood. In the pixel-based methods, texture is calculated with mobile windows that smooth the boundaries between discrete land cover regions and create between-class texture. This between-class texture could cause an edge-effect in the classification.<p><p>In this context, our research focuses on the potential of land cover region-based classification of VHR satellite data through the study of the object extraction capacity of segmentation processes, and through the study of the relevance of region features for classifying the land-cover classes in different kinds of Belgian landscapes; always keeping in mind the parallel with the visual interpretation which remains the reference.<p><p>Firstly, the results of the assessment of four segmentation algorithms belonging to the two main segmentation categories (contour- and region-based segmentation methods) show that the contour detection methods are sensitive to local variability, which is precisely the problem that we want to overcome. Then, a pre-processing like a filter may be used, at the risk of losing a part of the information. The “region-growing” segmentation that uses the local variability in the segmentation process appears to be the best compromise for the segmentation of different kinds of landscape.<p>Secondly, the features calculated thanks to segmentation seem to be relevant to identify some land-cover classes in urban/sub-urban and rural areas. These relevant features are of the same type as the features selected visually, which shows that the region-based classification gets close to the visual interpretation. <p>The research shows the real usefulness of region-based classification in order to classify the land cover with VHR satellite data. Even in some cases where the features calculated thanks to the segmentation prove to be useless, the region-based classification has other advantages. Working with regions instead of pixels allows to avoid the salt-and-pepper effect and makes the GIS integration easier.<p>The research also highlights some problems that are independent from the region-based classification and are recursive in VHR satellite data, like shadows and the spatial resolution weakness for identifying some land-cover classes.<p><p>Résumé<p>Depuis 1999, les données satellitaires à très haute résolution spatiale (IKONOS-2, QuickBird and OrbView-3) représentent la surface de la terre avec plus de détail. Cependant, l’extraction d’information par une classification multispectrale par pixel devient plus complexe en raison de l’augmentation de la variabilité spectrale dans les unités d’occupation du sol et du manque de résolution spectrale de ces données. Cependant, une possibilité est de considérer cette variabilité spectrale comme une information spatiale utile pouvant être utilisée comme une information complémentaire dans la caractérisation de l’occupation du sol. De plus, de part la diminution de la différence de résolution spatiale qui existait entre les photographies aériennes et les images satellitaires, les caractéristiques (attributs) utilisées en interprétation visuelle transposées à l’analyse digitale (texture, morphologie and contexte) peuvent être utilisées comme information complémentaire en plus de l’information spectrale pour la classification de l’occupation du sol.<p><p>La difficulté de cette approche est la transposition des caractéristiques visuelles à l’analyse digitale. Pour résoudre ce problème la classification par région pourrait être utilisée. La segmentation, avant la classification, produit des régions qui sont plus homogène en elles-mêmes qu’avec les régions voisines et qui représentent des objets ou des aires dans l’image. Chaque région devient alors une unité d’analyse qui permet l’élimination de l’effet « poivre et sel » et permet de mesurer et d’utiliser de nombreuses caractéristiques en plus des caractéristiques spectrales. Ces caractéristiques peuvent être la surface, le périmètre, la compacité, la texture. La segmentation est une des seules méthodes qui permet le calcul des caractéristiques morphologiques (surface, périmètre, …) et des caractéristiques texturales sur un voisinage non-arbitraire. Avec les méthodes de classification par pixel, la texture est calculée avec des fenêtres mobiles qui lissent les limites entre les régions d’occupation du sol et créent une texture interclasse. Cette texture interclasse peut alors causer un effet de bord dans le résultat de la classification.<p><p>Dans ce contexte, la recherche s’est focalisée sur l’étude du potentiel de la classification par région de l’occupation du sol avec des images satellitaires à très haute résolution spatiale. Ce potentiel a été étudié par l’intermédiaire de l’étude des capacités d’extraction d’objet de la segmentation et par l’intermédiaire de l’étude de la pertinence des caractéristiques des régions pour la classification de l’occupation du sol dans différents paysages belges tant urbains que ruraux. / Doctorat en sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Die vorliegende Arbeit stellt die Ergebnisse der methodologischen Entwicklung räumlich aufgelöster Analyse mittels Scanning Laserablation-ICP-Massenspektrometrie dar. Eine neue Behandlung zur Quantifizierung transienter analytischer Signale wurde für die Wiederherstellung von Konzentrationsprofilen vorgeschlagen. Die Anwendung der entwickelten Modelle auf die räumlich aufgelöste Analyse mittels LA-ICP-MS ermöglicht verbesserten Informationsgewinn und lässt dadurch eine höhere räumliche Auflösung erreichen. Die Anwendbarkeit der LA-ICP-MS für die räumlich aufgelöste Bestimmung der Stöchiometrie in supraleitenden Borokarbiden wurde untersucht. Der Einfluss apparativer Größen auf das analytische Signal wurde aufgeklärt, um die Messbedingungen zu optimieren. Zusätzlich wurden Fraktionierungseffekte untersucht, um die Ursache und deren Auswirkung auf die Analyse supraleitender Borokarbiden zu erklären. / This work represents the results of the methodological development of spatially resolved analysis by scanning laser ablation ICP mass spectrometry. A new approach to the quantification of transient analytical signals was proposed to reveal the concentration profile. An application of the developed models on spatially resolved analysis by LA-ICP-MS allows to gain more information from experimental data and hence to achieve better spatial resolution. The applicability of LA-ICP-MS to the spatially resolved determination of the stoichiometry of superconducting borocarbides was investigated. The effect of experimental parameters on analytical signals was elucidated in order to optimize the experimental conditions. In addition, fractionation effects were investigated to identify the causes for fractionation and their influence on the analysis of superconducting borocarbides.

The Gamma Knife is a non-invasive stereotactic radiosurgery system used for treatments of deep targets in the brain. Accurate patient positioning is needed for precise radiation delivery to the target. The two latest versions of the Gamma Knife allow fractionated treatment by co-registering Cone-beam computed tomography (CBCT) images of the patient's position in the Gamma Knife with a diagnostic magnetic resonance (MR) image used for treatment planning. However, CBCT images often suffer from artifacts that degrade image quality, which may result in less accurate co-registration. This thesis project investigates the potential of a new reconstruction framework developed by Elekta, which incorporates scattering correction and noise filters, for the reconstruction of Gamma Knife CBCT images. The performance of the new reconstruction framework, along with its noise filter and scatter correction, is quantified using image quality metrics of phantoms, including contrast, uniformity, spatial resolution, and CT-number accuracy. Additionally, brain CBCT images of five patients are co-registered with their diagnostic MR images, and the mean target registration error is measured. The results indicate that the new reconstruction framework, without using scatter correction and noise filtering, performs equally well as the current framework in reconstructing Gamma Knife CBCT images, as it achieved similar image quality and co-registration accuracy. However, when the scatter correction was used, there were improvements in image uniformity and CT-number accuracy without compromising spatial resolution. Additionally, the introduction of a noise filter resulted in an improved contrast-to-noise ratio and low contrast visibility with minimal compromise of spatial resolution. Despite these image quality enhancements, there were no consistent improvements in co-registration accuracy, indicating that the co-registration is not sensitive to scatter or noise artefacts. / Gammakniven är en medicinteknisk apparat som används för icke-invasiv stereotaktisk strålkirurgi vid behandling av djupa mål i hjärnan. För att uppnå precision i strålbehandlingen krävs noggrann patientpositionering. De två senaste versionerna av Gammakniven tillåter fraktionerad behandling genom att co-registrera cone-beam computed tomography (CBCT)-bilder av patientens position i Gammakniven med en diagnostisk magnetresonans (MR)-bild som används för behandlingsplanering. Tyvärr lider CBCT-bilder ofta av artefakter som kan försämra bildkvaliteten och därmed minska precisionen i co-registreringen. Detta examensarbete undersöker ett nytt rekonstruktionsramverk som utvecklats av Elekta. Det nya rekonstruktionsramverket och dess tillhörande brusfilter och spridningskorrigering utvärderas för rekonstruktion av Gammaknivens CBCT bilder med hjälp av bildkvalitetsmått för fantomer, såsom kontrast, uniformitet, spatial upplösning och noggrannhet i CT-nummer. Dessutom co-registreras CBCT-bilder från fem patienter med deras diagnostiska MR-bilder, och det genomsnittliga registreringsfelet mäts. Resultaten visar att det nya rekonstruktionsramverket, utan användning av spridningskorrigering och brusfiltrering, presterar lika bra som det nuvarande ramverket för rekonstruktion av CBCT-bilder från Gammakniven. Båda ramverken ger liknande bildkvalitet och noggrannhet i co-registreringen av bilderna. Vid användning av spridningskorrigering observerades förbättringar i uniformiteten och noggrannheten i CT-nummer utan att den spatiala upplösningen försämrades. Införandet av brusfilter resulterade i ett förbättrat kontrast-brus-förhållande och synlighet av svaga kontrastskillnader med endast lite avkall på den spatiala upplösningen. Trots dessa förbättringar i bildkvaliteten observerades ingen konsekvent förbättring av noggrannheten i co-registreringen av bilderna, vilket tyder på att co-registreringen inte påverkas av spridnings- eller brusartefakter i stor utsträckning.

Background: PET nuclides can have a considerable influence on the spatial resolution and image quality of PET/CT scans, which can influence diagnostics in oncology, for example. The individual impact of the positron energy of ¹⁸F, ⁶⁸Ga, and ⁶⁴Cu on spatial resolution and image quality was compared for PET/CT scans acquired using a clinical, digital scanner. - Methods: A Jaszczak phantom and a NEMA PET body phantom were filled with ¹⁸F-FDG, ⁶⁸Ga-HCl, or ⁶⁴Cu-HCl, and PET/CT scans were performed on a Siemens Biograph Vision. Acquired images were analyzed regarding spatial resolution and image quality (recovery coefficients (RC), coefficient of variation within the background, contrast recovery coefficient (CRC), contrast–noise ratio (CNR), and relative count error in the lung insert). Data were compared between scans with different nuclides.- Results: We found that image quality was comparable between ¹⁸F-FDG and ⁶⁴Cu-HCl PET/CT measurements featuring similar maximal endpoint energies of the positrons. In comparison, RC, CRC, and CNR were degraded in ⁶⁸Ga-HCl data despite similar count rates. In particular, the two smallest spheres of 10 mm and 13 mm diameter revealed lower RC, CRC, and CNR values. The spatial resolution was similar between ¹⁸F-FDG and ⁶⁴Cu-HCl but up to 18% and 23% worse compared with PET/CT images of the NEMA PET body phantom filled with ⁶⁸Ga-HCl. - Conclusions: The positron energy of the PET nuclide influences the spatial resolution and image quality of a digital PET/CT scan. The image quality and spatial resolution of ⁶⁸Ga-HCl PET/CT images were worse than those of ¹⁸F-FDG or ⁶⁴Cu-HCl despite similar count rates.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

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