This course introduces methods and software for management, analysis and mapping of soil type and soil properties within the R environment for statistical computing. The course alternates between lectures and computer exercises and covers a variety of subjects, such as geostatistics, machine learning for soil mapping, soil functional mapping, proximal soil sensing, quantification of uncertainty, sampling for mapping and soil map validation. The course aims at soil geographers and environmental scientists who want to learn more about the theory and practice of digital soil mapping. After this course, participants will be able to apply the methods learnt to their own datasets. Lecturers are experienced pedometricians and soil data analysis specialists.
Who is it for?
This course is intended for soil and environmental professionals, researchers and PhD-students interested in producing soil maps and/or using local, regional and global soil datasets for digital soil mapping. Participants must have a basic level of statistics, geo-information science and soil/environmental science. Experience with computer programming in R is advantageous but not required. Those not familiar with R will be asked to run a self-study tutorial prior to the course. A number of recorded sessions from a previous Spring School can be accessed via the ISRIC YouTube channel.
Former occurrences of this course
17-21 May 2021 | 11-25 May 2020 (cancelled due to COVID) | 17-24 May 2019