digital soil mapping presentation by Dr. Mario Guevara. Presented in 46 congress of soil science, Mexico 2022

1. La promesa del mapeo digital de suelos para
mejorar la calidad, cantidad y acceso a datos
e información sobre suelos en México
Mario Guevara1,3
, Todd Skaggs2
, Elia Scudiero1
1 University of California, Riverside, Department of Environmental Sciences Riverside CA
2 United States Department of Agriculture, Soil Salinity National Laboratory, Riverside CA
3 Centro de Geociencias - UNAM Campus Juriquilla, Qro. MX.
Oct. 6, 2022
46 CNCS, Coahuila MEX

2. Liebig et al., 2017
Legislacion
Modelos
Manejo
La información de suelos es requerida para entender el
potencial de uso de la tierra

3. 1. Mapeo digital de suelos y su potencial para mejorar la
calidad, cantidad y acceso a datos e información de
suelos
2. Sinergia de la ciencia de datos, de las ciencias de la
información geográfica y de la computación
3. La ciencia de la inteligencia artificial son útiles para el
mapeo de suelos pero subutilizadas
4. Principales fuentes de información para el mapeo
digital de suelos.
5. Alternativa efectiva para resolver el problema de los
datos y la información del suelo en México.
6. Grandes retos de investigación en el mapeo digital de
suelos.

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M Guevara – Digital soil mapping
McBratney et al., 2019

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Mapeo digital de suelos (mapeo pedometrico)
Marco de referencia
Producción de
modelos de
variabilidad de
suelos usando
computadoras
Informacion de suelos
Ciencia de datos y
ciencia del suelo
habilitan el
monitoreo de
suelos
Conocimiento en suelos
Hay una
demanda muy
grande de mapas
digitales de
suelos
Hengl et al. (2003), Scull et al., (2003), McBratney et al.(2003)
Sc,a
= f (s, c, o, r, p, a, n)

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La inteligencia artificial y la ciencia de datos
pueden fortalecer el proceso mapeo, reporte y
verificación de suelos

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https://smap.jpl.nasa.gov/

10. Mapa convencional de suelos Mapa digital de suelos
Estudios sobre la variabilidad de suelos
Grunwald et al., 2022

11. Grunwald et al., 2022

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Mapa digital de salinidad de suelos
EC dS/m

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Mapa digital de carbono orgánico del suelo

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Vargas et al., 2017

17. FAO, 2022
Potencial de captura de carbono en suelos

18. Se agradece el apoyo de: UNESCO-IGCP-IUGS, 2022
(#765), UNAM-PAPIIT, 2021 (#IA204522) and
USDA-NIFA-AFRI, USA, 2019 (#2019-67022-29696).

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Digital soil mapping
Geostatistics and Machine learning
Remote sensing
N
https://soilgrids.org/

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Hsieh, W. W. (2022). Evolution of machine learning
in environmental science—A perspective.
Environmental Data Science, 1. doi:
10.1017/eds.2022.2
Webster, R. (2000). Is soil variation random?
Geoderma, 97(3), 149–163. doi:
10.1016/S0016-7061(00)00036-7

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Salinity in North America 1/3
USDA-ARS US Salinity Laboratory (ca. 1970)

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Salinity in North America 2/3
Salinity in the Canadian Prairies (Eilers et al.
1997)

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Salinity in North America 3/3
TEXOCO area
Fernandez-Buces et al. (2006)
J. Arid. Environ. 65:644–667
YAQUI Valley
Lobell et al. (2007)
Soil Sci. Soc. Am. J. 71:777-783 AHOME area
Avila Aceves et al. (2018)
Pol. J. Environ. Stud.
doi: 10.15244/pjoes/81693

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Short-scale spatial variability/heterogeneity
and how to map it
Spatial variability of salinity
influenced by multiple factors
which result in high
short-range variability at the
field scale.
Often, because of different
management (irrigation water
quality) neighboring fields are
characterized by dramatically
different salinity levels.
High-resolution remote
sensing is an ideal covariate
to capture such short-scale
changes

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Surface Vs. root zone soil salinity
Surface Soil Salinity
GREAT PAPER 🡪 Aldabaa et al. (2015).
Combination of proximal and remote
sensing methods for rapid soil salinity
quantification. Geoderma, 239, 34-46.
Mapping root zone soil
salinity is more relevant in
agricultural soils
Scudiero et al. (In preparation)

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Surface reflectance is influenced by:
• Relative crop status
🡪 stressed vs. non-stressed
(e.g., B,G,R 🡪 photosynthesis activity; NIR
🡪 turgor):
• Crop type
• Growth stage
• Soil type (texture, SOC, iron, salt crust, …)
Root-zone salinity via crop canopy measurements
GROUND-TRUTH SAMPLING IS NEEDED!!

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Visible & near-infrared vegetation indices
Thermal imagery can be used too!
Ivushkin et al. (2017). Satellite Thermography for Soil Salinity Assessment of Cropped
Areas in Uzbekistan. Land Degradation & Development, 28, 870-877
Landsat 7 surface reflectance
• 30x30m resolution

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Multiyear max CRSI and soil salinity
MAX
CRSI
•Under similar management, salinity stress (permanent stress) is fairly
constant in the root-zone through a limited amount of time
•Plant performance (measured with CRSI) is maximum when transient stress
sources are at minimum🡪 salinity effect on plant growth is highlighted

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• 22 fields sampled in 2013 (ca. 550 ha) in western San Joaquin Valley, CA
• ~42,000 APPARENT ELECTRICAL CONDUCTIVITY (ECa
) measurements
at 0-0.75 & 0-1.5 m (with EM38 Dual Dipole)
• 267 soil samples (0.3 m intervals, down to 1.5 m): Salinity (ECe
), pH, SP, WC …
🡪 focus on 0-1.2 m aggregate: “root-zone”
• ~6000 ground truth cells. Overall accuracy R2
= 0.93
Ground-truth measurements
ECa
-ECe
Linear
modeling
kriging
30x30m
~24 ha
ECa
depends on several
soil properties:
- Salinity (ECe
) ↑
- Texture (Sand ↓; Clay ↑)
- Water Content ↑
-…
A calibration is needed to convert ECa
measurements to ECe

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Multiyear max CRSI and soil salinity
MAX CRSI as main explanatory variable.
Other covariates?
-Crop type?
-Meteorology?
-Soil information (texture, elevation, …)?
-Management?
Scudiero et al. (2015). Remote Sensing of Environment

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Remote sensing assessment model
•Crop type / Management
CropScape database (Han et al., 2012)
•Meteorology: PRISM model
- Yearly tot rainfall mm
- Ave min temp, ºC
@ 4x4 km (Daly et al., 2008)
•Clay % (available at 267 locations)
10% increase of explained variance
ECe
= α0
+ βcrop
× CRSIt-max
+ α1
× RAINt-max
+ α2
× TEMPt-max
+ α3
× CLAY + ε
βcrop
= different for fallow pixels or
otherwise (cropped)
Scudiero et al. (2015). Remote Sens Environ

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Model evaluation
satisfactory?
•Spatially-independent
cross-validation
(e.g., leave-one-field-out)
•Independent data
•Ask a farmer!
Extrapolate
to whole region
Scudiero et al. (2017). California Agriculture

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Limitations and research gaps
• Uncertainty of predictions at low salinity values
• Halophyte reflectance properties are problematic
• Published work does not focus on tree crops
(complex spatiotemporal dynamics of salinity in the root zone especially
with micro-irrigation)
• Selection of satellite products
(need for high spatial and temporal resolution for VIS+NIR+Thermal)
• Improve root-zone depth for different crops
(x soil x management)
• Now working on: spatiotemporal prediction model
(with ground-truth data spanning over 30 years)

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Closing remarks
• Remote sensing of salinity over large farmland regions
with ECe
< 20-30 dS m-1
is possible
• Further research is needed to improve accuracy of remote
sensing predictions
• North-America-wide problem… let’s talk about it!
Moving towards a continental inventory
– Please contact me for collaborations
– “North American Alliance for Soil Salinity Mapping”?

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THANKS FOR YOUR ATTENTION
Elia Scudiero, PhD
UC Riverside, Environmental Sciences Department
& USDA-ARS, U.S. Salinity Laboratory
elia.scudiero@ars.usda.gov
elia.scudiero@ucr.edu

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• Many crops are tolerant to salinity ECe
< 4 dS m-1
🡪 No detectable effect on NDVI/EVI/CRSI...
• Halophytes
🡪 Weak plant performance at low and extreme salinity
Uncertainties at low salinity levels...

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Salinity assessment with MODIS (250x250m)
Whitney et al. (2017). Ecological Indicators

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Salinity assessment with WorldView 2 (2x2m)
Scudiero et al. (2017). California Agriculture

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Multi-year stability
Lawrie & Eldridge
(2004)

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