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Digital Soil Mapping: test and develop methods to (visually) communicate uncertainty
Uncertainty is important for modelers and scientific community. However it is not easy to communicate to stakeholders. This project will explore mathematical indices to summarize uncertainty but also visualization techniques to ma ...
Supervisor: Titia Mulder (SGL WUR) and Laura Poggio (ISRIC)
Department: Soil Geography and Landscape |
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Remote Sensing: Using the EM signature of the Earth surface for DSM
Remote and proximal sensing data are frequently used for predicting and mapping soil properties. Typically, the spectral bands are being used individually as predictors in statistical models to predict soil properties. However, th ...
Supervisor: Titia Mulder (SGL WUR) and Laura Poggio (ISRIC)
Department: Soil Geography and Landscape |
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Spectral libraries: Comparison of MIR and NIR sensors
In this research you will compare the predictability of some key soil properties from both NIR and MIR sensors, with focus on the associated uncertainty, costs, time, repeatability. The outcome will highly depend on the instrument ...
Supervisor: Titia Mulder (SGL WUR) and Fenny van Egmond (ISRIC/WENR)
Department: Soil Geography and Landscape |
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Uncertain data: Positional accuracy of soil sample locations
Digital soil mapping depends on the statistical relationships between soil measurements and environmental covariates at the sample locations. Whenever an error is present in these locations, a discrepancy between the measurement a ...
Supervisor: Cynthia van Leeuwen (SGL WUR / ISRIC) and Titia Mulder (SGL WUR)
Department: Soil Geography and Landscape |
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