The presentation was given by Mr. Mario Guevara during the GSOC map training that was held in Aguascalientes Mexico, 26-30 June 2017.

1. Building capacities for digital
soil organic carbon mapping
M. Guevara, G. F. Olmedo E. Stell, J. G. De Lira, C.
Cruz, R. Vargas

2. The University of Delaware, Plant and Soil Sciences
La Comision Nacional para el Conocimiento y Uso
de la Biodiversidad, CONABIO
Instituto Nacional de Tecnología Agropecuaria, Ar.
Instituto Nacional de Estadistica y Geografia Mx.
Many thanks to:

3. Topics
Conceptual basis
The SCORPAN model
Digital terrain analysis
Harmonization of databases
Modeling SOC
Validating and predicting
Uncertainty of SOC estimates

4. http://www.stockholmresilience.org/research/planetary-boundaries/planetary-bound
aries/about-the-research/the-nine-planetary-boundaries.html
Planetary boundaries that
must not be transgressed
are closely related to soil
organic carbon (SOC)

5. How can we quantify the
spatial variability of SOC
and predict in places where
no information is available?

6. We use digital soil mapping to ananlyze the spatial variability of SOC
and its associated uncertainty aiming to provide a baseline estimate
useful for accounting and monitoring soil carbon.
Digital Soil Mapping (DSM) is the creation and the population of a
geographically referenced soil database, generated at a given
resolution by using field and laboratory observation methods coupled
with environmental data through quantitative relationships (see
http://digitalsoilmapping.org/).
We assume that different DSM models will capture different portions of
SOC variability. We combine linear models with data driven statistics
for variable selection, prediction to new data and uncertainty
estimation.

7. Land modeling efforts to predict climate change rely on SOC
estimates but no uncertainty assessment is available.
High uncertainties and significant differences of SOC estimates
associated with the number of covariates, the pixel size and the
statistical performance of the modeling approaches.
We coupled remote sensing, geomorphometry and climate
surfaces to generate a multiscale and multisource set of prediction
factors. We model, cross validate and estimate uncertainty of SOC
variability models.

8. Overreaching goal
To provide cyber-infrastructure tools and develop country-specific, technical,
analytical and institutional capacities for digital SOC mapping across Central
America and the Caribbean .
To better inform and empower producers and protect the functioning of
the productive landscapes of our territories
Uncertainty in soil data can outweigh climate impact signals in global crop
yield simulations Folberth et al. (2017)
Country-specific information is required by regional-to-global academic and policy
relevant research efforts (GlobalSoilMap.net, FAO-GSM cookbook 2017).

9. Working principles and specific objectives
Reproducible research
Transparency in methods
Open source platforms
To teach the participants how to generate a high quality digital SOC maps based
on state of the art techniques including uncertainty of SOC estimates
The SOC resulting maps could be used as baseline estimates for reporting,
monitoring and verification of SOC stocks

10. Working objectives
To generate a regional, interpretable and predictable model of SOC variability
Country specific SOC map representative of the first 30cm of depth at the spatial
resolution of 1x1km, derived using digital soil mapping techniques and including
the uncertainty of SOC estimates.

11. Baseline datasets (SOC ground thruth)
SOC Point data (e.g. Mexico ):
http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/vectorial_seriei.aspx
http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/vectorial_serieii.aspx
Global:
http://www.earth-syst-sci-data.net/9/1/2017/
http://www.isric.org/sites/default/files/isric_report_2015_03.pdf

12. Baseline datasets (SOC covariates 1x1km)
Digital terrain parameters
Climate surfaces
Remote sensing
Legacy and polygon maps (i.e., soil, geology, land use/cover)
SoilGrids250m system
Pelletier et al. 2016, Shangguan et al., 2017, Hengl et al. 2017, Fick and Hijmans,
2017

13. McBratney
et al 2000

15. Targulian y Goryachkin
2004

16. Conceptual basis
Dokuchaev 1883
Jenny 1941
Troeh 1964
Walker 1968
Legros and Bonneric 1979
Moore et al 1993
Heuvelink and Webster 2001

17. McBratney et al 2003
Grunwald et al. 2009

18. Grunwald et al. 2011

20. Guevara et al. NACP, 2017

21. f, a data driven decision
‘I will keep my eye on the quantile culture to see what develops’. Breiman 2001
Quantile regression forests, Meinshausen 2006 The full conditional distribution, a surrogate of uncertainty
Kriging, Support Vector Machines, Cubist, Kernel weighted nearest neighbors
https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726
http://robertmarks.org/Classes/ENGR5358/Papers/NFL_4_Dummies.pdf
http://onlinelibrary.wiley.com.udel.idm.oclc.org/doi/10.1111/2041-210X.12397/abstract
http://jmlr.org/papers/volume15/delgado14a/delgado14a.pdf
https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/

23. Examples of large scale SOC mapping
Viscarra Rossel et al 2014, Hengl et al. 2017 ...

24. Digital terrain analysis (DTA) in SAGA GIS
Topography is a major driver of soil variability
Topography can be represented by digital elevation models
We have digital elevation models globally available
Using DTA we can derive topographic attributes (e.g. Slope, aspect, wetness
index).
Derive terrain parameters in SAGA GIS using a digital elevation model of 1x1 km.
http://www.saga-gis.org/

25. Global-to-local and local-to-global SOC mapping
All models are wrong but some of they are useful
Models should not compete but inform each other
Different models (and modeling cultures) will capture different portions of SOC
There are no best method on digital soil mapping (no silver bullets)
Ensemble learning should be part of the pedometrics agenda
The eternal question is, how inform nationwide policy decision based on the best
information available?
Box 1976, Breiman 2001, Ho and Pepyne 2002,, Finke 2012, Qiao et al. 2015

26. The quantile regression forests

27. Harmonization of datasets
To generate a geographical information system of all available data sets.
For points and grids, same geospatial reference
For grids, same pixel size, 1km. (note: Rasterize first polygon maps)
http://spatialreference.org/
Exercise 2.1 Harmonize SOC predictors in a regular grid of 1x1km and all
available information
Exercise 2.2 Generate a regression matrix of available SOC data and the values
of the predictors at observation points.

28. SOC covariates

29. Information sources (some)
http://worldclim.org/version2
http://onlinelibrary.wiley.com.udel.idm.oclc.org/doi/10.1002/2015MS000526/pdf
www.soilgrids.org
http://worldgrids.org/doku.php/wiki:layers
http://cci.esa.int/

30. Working folder
1. List of soil covariates available to 1km from ISRIC’s worldgrids.org
2. List of additional covariates
3. Limit of each country http://www.gadm.org/country
4. References (McBratney et al. Batjes et al, Hengl et al.)
5. Code (considering FAO DSM cookbook, http://www.fao.org/3/a-bs901e.pdf)
6. Regression matrix
7. Covariates matrix
8. Digital elevation model processing steps (derive terrain parameters in SAGA
GIS)