Anatol Helfenstein

Anatol Helfenstein
Wageningen University & Research | WUR · Department of Soil Geography and Landscape

MSc ETH Zürich

About

7
Publications
2,008
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17
Citations
Introduction
Currently, I am working on developing a high resolution 3D soil modelling and mapping platform for the Netherlands, also called "Bodemkundig Informatie Systeem plus" (BIS+). I am interested in using proxy and sensing methods, large datasets and statistical analysis to model soils over space and time to make sustainable land use decisions. Previously, I completed a BSc in Geosciences at the University of Basel and a MSc in Environmental Science at ETH Zürich.

Publications

Publications (7)
Article
Full-text available
Accurate and high resolution spatial soil information is essential for efficient and sustainable land use, management and conservation. Since the establishment of digital soil mapping (DSM) and the goals set by the GlobalSoilMap (GSM) working group, great advances have been made to attain spatial soil information worldwide. Highly populated areas s...
Article
Full-text available
Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Bi...
Article
Full-text available
Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 1...
Presentation
Since the establishment of Digital Soil Mapping (DSM) as a research field, the main focus has been on implementing new methods to improve the predictive performance of soil maps. However, considerably less effort has been invested in investigating the best way to communicate the quality of soil mapping products with users. This is essential for soi...
Preprint
Full-text available
Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purposes we developed a national Swiss soil spectral library (SSL; n = 4374) in the mid-infrared (mid-IR), calibrating 17 properties from legacy measurements on soils from the Swiss...
Preprint
Full-text available
Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 1...
Conference Paper
Full-text available
Soil data at different scales are needed for assessments and monitoring of soil functions. Soil diffuse reflectance spectroscopy using visible-Near Infrared and mid-Infrared energies can be used to estimate a range of soil properties, rapidly and inexpensively. However the spectroscopic modeling is challenging because of the large soil diversity an...

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Projects

Projects (3)
Project
[preliminary titles] * Component 1: "Developing the Swiss soil spectral library for local estimation and monitoring" * Component 2: "A proof of concept for detecting field-scale temporal changes in soil carbon for monitoring using infrared spectroscopy" * Component 3: "Combining infrared spectroscopy and biogeochemical modeling to monitor soil carbon: A sensitivity analysis"
Project
Developing a high-resolution 3D soil information system for the Netherlands
Project
- Predict Soil Carbon ranging from 1% to 52% from a local dataset containing drained peatland soil samples by using MIR spectroscopy and machine learning - Build a Swiss soil spectral library (SSL) and predict the local dataset by subsetting the SSL using the data-driven rs-local algorithm (Lobsey et al., 2017) and machine learning - spatially predict peat thickness, topsoil soil organic carbon (SOC) and water-logging depth in the upper Rhine Valley, Switzerland by using digital soil mapping (DSM)