USDA-NRCS Soil Survey Data

• object inspection

idname(sp4) # pedon ID name
horizonDepths(sp4) # colum names containing top and bottom depths
depth_units(sp4) # defaults to 'cm'
metadata(sp4) # data.frame with 1 row
profile_id(sp4) # vector of profile IDs

• overloads to common functions

length(sp4) # number of profiles in the collection
nrow(sp4) # number of horizons in the collection
names(sp4) # column names from site and horizon data
min(sp4) # shallowest profile depth in collection
max(sp4) # deepest profile depth in collection
sp4[i, j] # get profile "i", horizon "j"

• getting / setting of components

horizons(sp4) # get / set horizon data
site(sp4)  # get / set site data
diagnostic_hz(sp4) # get / set diagnostic horizons
proj4string(sp4) # get / set CRS
coordinates(sp4) # get / set coordinates

• coercion to SpatialPointsDataFrame or data.frame

as(sp4, 'SpatialPointsDataFrame')
as(sp4, 'data.frame')

basic morphologic / taxonomic data from OSD and SC databases [SoilWeb]

# soils of interest
s.list <- c('hornitos', 'argonaut', 'mokelumne', 'dunstone', 'auburn', 'pentz', 'pardee', 'peters', 'amador', 'laniger')

# fetch data from SoilWeb server and return as SoilProfileCollection
s <- fetchOSD(s.list)

# plot
par(mar=c(0,0,0,0))
plot(s, name='hzname', id.style='side', cex.name=0.75, axis.line.offset=-4.5)

profile sketches organized by taxonomy

# soils of interest
s.list <- c("hornitos", "perkins", "argonaut", "inks", "mokelumne", "dunstone", "auburn", "pentz", "pardee", 
    "peters", "amador", "laniger")

# fetch data from SoilWeb server and return as SoilProfileCollection
s <- fetchOSD(s.list)

# organize and plot according to subgroup level taxonomic data
SoilTaxonomyDendrogram(s, cex.taxon.labels = 0.8)

how about some examples

• simulating data to feed / test models

sim(SPC, n=10, hz.sd=2)
random_profile(id, n=c(3, 4, 5), min_thick=5, max_thick=30, n_prop=5)

• profile sketches ordered by meaningful gradient

plot(SPC, plot.order=new.order)

• applying functions by profile

profileApply(SPC, <function>)

• “slicing”: depth-wise alignment and extraction of data

slice(SPC, 0:50 ~ sand + silt + clay)

• aggregating by “slab”: group / depth-wise summaries

# assuming no NA
slab(SPC, ~ sand + silt + clay, slab.fun=mean)
slab(SPC, ~ sand + silt + clay, slab.structure=c(0,10), slab.fun=mean)
slab(SPC, group ~ sand + silt + clay, slab.fun=mean)

simulation based on a random walk– similar to highly stratified soils

# implicit loop via plyr::ldply, result is a data.frame
d <- ldply(1:10, random_profile, n=c(6, 7, 8), n_prop=1, method='random_walk')
# promote to SoilProfileCollection and plot
depths(d) <- id ~ top + bottom
par(mar=c(0,0,3,0))
plot(d, color='p1', axis.line.offset=-4, max.depth=150)

simulation based on the logistic power peak function– more realistic anisotropy

# implicit loop via plyr::ldply, result is a data.frame
d <- ldply(1:10, random_profile, n=c(6, 7, 8), n_prop=1, method='LPP', 
lpp.a=5, lpp.b=10, lpp.d=5, lpp.e=5, lpp.u=25)
# promote to SoilProfileCollection and plot
depths(d) <- id ~ top + bottom
par(mar=c(0,0,3,0))
plot(d, color='p1', axis.line.offset=-4, max.depth=150)

data From McGahan et al.

# load sample dataset, comes with aqp package
data(sp4)
# inspect first 4 rows x 12 columns
sp4[1:4, 1:12]

      id name top bottom   K   Mg  Ca CEC_7 ex_Ca_to_Mg sand silt clay
1 colusa    A   0      3 0.3 25.7 9.0  23.0        0.35   46   33   21
2 colusa  ABt   3      8 0.2 23.7 5.6  21.4        0.23   42   31   27
3 colusa  Bt1   8     30 0.1 23.2 1.9  23.7        0.08   40   28   32
4 colusa  Bt2  30     42 0.1 44.3 0.3  43.0        0.01   27   18   55

# upgrade to SoilProfileCollection
depths(sp4) <- id ~ top + bottom

# custom function for computing hz-thick wt. mean, accounting for missing data
wt.mean.ca.mg <- function(i) {
    # use horizon thickness as a weight
    thick <- i$bottom - i$top
    # function is from the Hmisc package
    m <- wtd.mean(i$ex_Ca_to_Mg, weights=thick, na.rm=TRUE)
    return(m)
    }

# apply custom function to each profile, save as "site-level" attribute
sp4$wt.mean.ca.to.mg <- profileApply(sp4, wt.mean.ca.mg)

# generate index ordering from small -> large Ca:Mg
new.order <- order(sp4$wt.mean.ca.to.mg)

# plot the data using our new order based on Ca:Mg
par(mar=c(4,0,3,0))
plot(sp4, name='name', color='ex_Ca_to_Mg', plot.order=new.order, cex.name=0.75, id.style='side', axis.line.offset=-4,)

# add an axis labeled with the sorting criteria
axis(1, at=1:length(sp4), labels=round(sp4$wt.mean.ca.to.mg, 3), cex.axis=1)
mtext(1, line=2.25, text='Horizon Thickness Weighted Mean Ex. Ca:Mg', cex=1)

# read in OSD color data as CSV
x <- read.csv('data/osd-colors-original.csv.gz')

Ap,0,20,10YR,4,4,10YR,3,4,CECIL
Bt1,20,66,10R,4,8,10R,3,7,CECIL
Bt2,66,107,10R,4,8,10R,3,7,CECIL
BC,107,127,2.5YR,4,8,2.5YR,3,7,CECIL
C,127,203,2.5YR,4,8,2.5YR,3,7,CECIL

# re-order by series, then depth
x <- x[order(x$series, x$top), ]

# convert Munsell to RGB
x.rgb <- with(x, munsell2rgb(matrix_wet_color_hue, 
                             matrix_wet_color_value, 
                             matrix_wet_color_chroma, 
                             return_triplets=TRUE))

# init SoilProfileCollection object
depths(g) <- series ~ top + bottom

# slice at specific depths, keeping only  r, g, b
# ignore bad horizonation with strict=FALSE
g.slices <- slice(g, c(5, 10, 15, 25) ~ r + g + b, just.the.data=TRUE, strict=FALSE)

# save depth slices as CSV files, import into GIS and make map
write.csv(g.slices[g.slices$top == 5, c('series', 'r', 'g', 'b')], ...)
write.csv(g.slices[g.slices$top == 10, c('series', 'r', 'g', 'b')], ...)
write.csv(g.slices[g.slices$top == 15, c('series', 'r', 'g', 'b')], ...)
write.csv(g.slices[g.slices$top == 25, c('series', 'r', 'g', 'b')], ...)