Hands-on Digital Soil Mapping 2021

 

Preliminary programme

Sections: Overview  |  Who is it for?  |  Software installation  |  Programme  | Materials  | Registration

 

Overview

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 statistical 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.

• Gerard Heuvelink, course coordinator (geostatistics, uncertainty quantification)

• Fenny van Egmond (remote and proximal soil sensing)

• Bas Kempen (data preparation, machine learning)

• Johan Leenaars (mapping plant-available soil water and nutrients)

• Laura Poggio (data preparation, machine learning)

• Maria Ruiperez Gonzalez (mapping plant-available soil water and nutrients)

• Stephan Mantel (virtual visit World Soil Museum)

• David Rossiter (geostatistics, data preparation, sampling for statistical validation, guest lecture)

• Titia Mulder, guest lecturer (remote and proximal soil sensing)

• Dick Brus, guest lecturer (sampling for statistical validation)

 

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 at: http://youtube.com/c/ISRICorg.

 

Registration

The maximum number of participants (50) has been reached for this course. 

 

Software installation

Please note that to participate in the DSM course you need to install R and RStudio software prior to the course. Software installation instructions can be found here, and the associated R code to test your installation here. Please read and follow these carefully to ensure you are fully prepared to the Spring School.

 

Preliminary programme

All times are Central European Time zone (CET), that is UTC+1.

 

DAY 1 (Monday, 17 May 2021)

Time	Topic	Type	Lecturer
9.00 – 9.30	Course introduction and overview	Lecture	Gerard Heuvelink
9.30 – 10.30	Geostatistics for soil mapping	Lecture	Gerard Heuvelink
10.30 – 11.00	Morning break
11.00 – 12.30	Geostatistics for soil mapping	Lecture	Gerard Heuvelink
12.30 – 14.00	Lunch
14.00 – 15.30	Geostatistics for soil mapping	Computer practical	Gerard Heuvelink David Rossiter
15.30 – 16.00	Afternoon break
16.00 – 17.00	Geostatistics for soil mapping	Computer practical and feedback	Gerard Heuvelink David Rossiter

 

DAY 2 (Tuesday, 18 May 2021)

Block	Topic	Type	Lecturer
9.00 – 10.30	Geostatistics for soil mapping	Lecture	Gerard Heuvelink
10.30 – 11.00	Morning break
11.00 – 12.30	Soil and covariate data preparation	Lecture	Bas Kempen
12.30 – 14.00	Lunch
14.00 – 15.30	Soil and covariate data preparation	Lecture & computer practical	Bas Kempen Laura Poggio David Rossiter
15.30 – 16.00	Afternoon break
16.00 – 17.00	Soil and covariate data preparation	Computer practical and feedback	Bas Kempen Laura Poggio David Rossiter

 

DAY 3 (Wednesday, 19 May 2021)

Block	Topic	Type	Lecturer
9.00 – 10.30	Remote and proximal soil sensing	Lecture	Titia Mulder
10.30 – 11.00	Morning break
11.00 – 12.30	Remote and proximal soil sensing	Computer practical and feedback	Titia Mulder Fenny van Egmond
12.30 – 14.00	Lunch
14.00 – 15.30	Machine learning for soil mapping	Lecture	Bas Kempen
15:30 – 16.30	Afternoon Break
15.30 – 17.00	Machine learning for soil mapping	Computer practical	Bas Kempen Laura Poggio

 

DAY 4 (Thursday, 20 May 2021)

Block	Topic	Type	Lecturer
9.00 – 10.30	Machine learning for soil mapping	Computer practical and feedback	Bas Kempen Laura Poggio
10.30 – 11.00	Morning break
11.00 – 12.30	Sampling for statistical validation	Lecture	Dick Brus
12.30 – 14.00	Lunch
14.00 – 15.30	Sampling for statistical validation	Lecture & computer practical	Dick Brus David Rossiter
15.30 – 16.00	Afternoon break
16.00 – 17.00	Sampling for statistical validation	Computer practical and feedback	Dick Brus David Rossiter

 

DAY 5 (Friday, 21 May 2021)

Block	Topic	Type	Lecturer
9.00 – 10.30	Mapping plant-available soil water and nutrients	Lecture	Johan Leenaars
10.30 – 11.00	Morning break
11.00 – 12.30	Mapping plant-available soil water and nutrients	Computer practical and feedback	Johan Leenaars Maria Ruiperez-Gonzalez
12.30 – 14.00	Lunch
14.00 – 14.40	Virtual excursion World Soil Museum		Stephan Mantel
14.40 – 15.00	Course evaluation and certificates		Gerard Heuvelink
15.00 – 15.15	Afternoon break
15.15 – 16.15	Valuing the soil resource	Invited lecture	David Rossiter
16.15 – 16.30	Closing words		Rik van den Bosch

 

Materials

You must use your own computer or laptop and have recent releases of R and RStudio installed (see above). Participants receive all workshop materials (test datasets, R scripts, lecture slides, tutorials) during the workshop.