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MTA ATK TAKI Institute for Soil Sciences and Agricultural Chemistry,
Centre for Agricultural Research, Hungarian Academy of Sciences
Comparison of a soil texture map synthetized from GlobalSoilMap standard layers
with a goal specifically compiled product
Annamária Laborczi, László Pásztor, Gábor Szatmári, Katalin Takács, Zsófia Bakacsi
7th Global Digital Soil Mapping Workshop 2016 Aarhus, Denmark
• Introduction two ways to compile topsoil texture class maps
• Mapping process reference data, co-variables, methods…
• Results maps, comparison, validation…
• Conclusions
Comparison of a soil texture map synthetized from GlobalSoilMap standard layers
with a goal specifically compiled product
Topsoil 0-30 cm texture class maps
Reference point data
weighted average
0-5, 5-15, 15-30 cm 0-30 cm
SYNTHETIZED MAP
EXPECTED MODEL INPUT
SPLINE
CLAY/SILT/SAND MAP
DIRECTLY COMPILED MAP
SIMS Hungarian Soil Information
and Monitoring System
Particle size (mm)
Particle size fraction
< 0,002 CLAY 0,002-0,005
SILT 0,005-0,01 0,01-0,02 0,02-0,05 0,05-0,2 SAND 0,2<
~ 1200 points
Recently sampled point database
Laboratory measurements e.g. pH, SOM, CaCO3, heavy metals & particle size distribution
Reference soil data
Environmental co-variables
TOPOGRAPHY EU-DEM and derivatives • Elevation • Slope • Aspect • General Curvature • Vertical Distance to Channel Network • SAGA Wetness Index • Topographic Wetness Index • Diurnal Anisotropic Heating • Real Surface Area • Channel Network Base Level • MRVBF - Multiresolution Index
of Valley Bottom Flatness • MRRTF - Multiresolution Index
of Ridge Top Flatness • Mass Balance Index • Stream Power Index • LS Factor • Topographic Position Index
LAND USE AND VEGETATION MODIS images March 2012 & September 2013 NDVI 16-day average
SOIL Digital Kreybig Soil Information System (DKSIS) Soil physical property categories
METEOROLOGY average annual evapotranspiration average annual precipitation average annual temperature annual evaporation
LITHOLOGY Geological Map of Hungary 1:100.000 – merged FAO categories
Environmental co-variables
Regression Kriging
Environmental co-variables
CLAY, SILT, SAND rasters
Nationwide soil texture class map
SIMS Hungarian Soil Information and Monitoring System
0-5 cm
5-15 cm
15-30 cm
Synthetized
0-30 cm
CLAY SILT SAND 0%
100%
Synt
hetiz
ed m
ap
Dire
ctly
com
pile
d m
ap
D
S
D - Directly compiled 0-30 cm USDA texture class map
S - Synthetized 0-30 cm USDA texture class map
CLAY
D
S
Histogram
SAND D S
Histogram
D
CLAY
SAND
S
D
S
Scatterplot
Difference D - S
SAND
Difference D - S
CLAY
Accordance Minor difference Major difference
USDA texture class differences (D, S) based on taxonomical distances
Validation with independent data
MARTHA Hungarian Detailed Soil Hydrophysical Database • 692 point • particle size distribution • laboratory measurements • spline
ME: Mean Error
MAE: Mean Absolute Error
RMSE: Root Mean Square Error
CLAY SAND
D S D S
ME -1,56 -1,06 1,76 1,40
MAE 6,50 6,84 12,19 12,36
RMSE 8,91 9,32 16,38 16,92
Total
Accuracy Kappa Index
D 0,85 0,42 S 0,84 0,40 D & S 0,93 0,76
clay clay loam
loam loamy sand
sand sandy clay
loam
sandy loam
silt loam
silty clay
silty clay
loam
user accuracy
D 0,82 0,81 0,83 0,74 0,86 0,72 0,83 0,86 0,89 0,87 S 0,77 0,81 0,83 0,83 0,80 0,80 0,82 0,85 0,88 0,87 diff. (%) 5 0 0 -9 6 -8 1 1 2 0
producer accuracy
D 0,70 0,80 0,87 0,82 0,86 0,73 0,84 0,87 0,88 0,86 S 0,72 0,79 0,85 0,83 0,89 0,71 0,84 0,86 0,87 0,85 diff. (%) -2 1 2 -1 -2 2 0 0 1 1
Validation with independent data
USDA texture classes (weighted Kappa according to taxonomical distances)
Conclusions
• The synthetized and the directly compiled soil texture maps have little differences.
• However, in case of particle size fractions, the differences are greater in the extreme ranges.
• Misclassification concerns more the extreme categories.
• Risk of using the synthetized map: it can intensify extremities
THANK YOU FOR YOUR KIND ATTENTION!