Sensors,
Год журнала:
2024,
Номер
24(17), С. 5612 - 5612
Опубликована: Авг. 29, 2024
Soil
heavy
metal
contamination
in
urban
land
can
affect
biodiversity,
ecosystem
functions,
and
the
health
of
city
residents.
Visible
near-infrared
(Vis-NIR)
spectroscopy
is
fast,
inexpensive,
non-destructive,
environmentally
friendly
compared
to
traditional
methods
monitoring
soil
Cu,
a
common
found
soils.
However,
there
has
been
limited
research
on
using
spatially
nearby
samples
build
Cu
estimation
model.
Our
study
aims
investigate
how
influence
In
our
study,
we
collected
250
topsoil
(0-20
cm)
from
China's
third-largest
analyzed
their
spectra
(350-2500
nm).
For
each
unknown
validation
sample,
selected
its
construct
The
results
showed
that
method
(Rp2
=
0.75,
RMSEP
8.56,
RPD
1.73),
incorporating
greatly
improved
model
0.93,
4.02,
3.89).
As
number
increased,
performance
followed
an
inverted
U-shaped
curve-initially
increasing
then
declining.
optimal
125
(62.5%
total),
mean
distance
between
calibration
17
km.
Therefore,
conclude
significantly
enhances
should
strike
balance,
covering
moderate
area
without
being
too
few
or
many.
Geoderma,
Год журнала:
2024,
Номер
442, С. 116798 - 116798
Опубликована: Фев. 1, 2024
Soil
pH
is
one
of
the
critical
indicators
soil
quality.
A
fine
resolution
map
urgently
required
to
address
practical
issues
agricultural
production,
environmental
protection,
and
ecosystem
functioning,
which
often
fall
short
meeting
demands
for
local
applications.
To
fill
this
gap,
we
used
data
from
an
extensive
survey
13,424
surface
samples
(0–0.2
m)
across
cropland
Jiangxi
Province
in
Southern
China.
Using
digital
mapping
techniques
with
46
covariates,
produced
a
30
m
topsoil
We
integrate
different
variable
selection
algorithms
machine
learning
methods.
Our
results
indicate
Random
Forest
covariates
selected
by
recursive
feature
had
best
performance
r
0.583
RMSE
0.41.
The
prediction
interval
coverage
probability
our
was
0.92,
indicating
low
estimated
uncertainty.
Climate
identified
as
most
predicting
contribution
37.42
%,
followed
properties
(29.09
%),
management
(21.86
parent
material
(6.22
biota
(5.39
%)
factors.
mean
5.21,
great
pressure
acidification
region.
high
values
were
mainly
distributed
Northern,
Western,
Eastern
parts
region
while
majorly
located
central
part.
Compared
past
surveys
1980
s,
there
no
significant
change
surveyed
can
provide
important
implications
guidance
decisions
on
heavy
metal
pollution
remediation,
precision
agriculture,
prevention
acidification.
Geoderma,
Год журнала:
2024,
Номер
448, С. 116969 - 116969
Опубликована: Июль 15, 2024
Understanding
and
managing
soil
organic
carbon
stocks
(SOCS)
are
integral
to
ensuring
environmental
sustainability
the
health
of
terrestrial
ecosystems.
The
information
bulk
density
(BD)
is
important
in
accurately
determining
SOCS
while
it
often
missing
database.
Using
3,504
profiles
(14,170
samples)
that
represented
diverse
regions
across
China,
we
investigated
effectiveness
various
pedotransfer
functions
(PTFs),
including
traditional
PTFs,
machine
learning
(ML),
ensemble
model
(EM),
predicting
BD.
results
showed
refitting
parameter(s)
PTFs
was
essential
for
BD
prediction
(coefficient
determination
(R2)
0.299–0.432,
root
mean
squared
error
(RMSE)
0.156–0.162
g
cm−3,
Lin's
concordance
coefficient
(LCCC)
0.428–0.605).
Compared
ML
can
greatly
improve
performance
with
R2
0.425–0.616,
RMSE
0.129–0.158
cm−3
LCCC
0.622–0.765.
Our
also
EM
further
by
ensembling
four
models
(R2
=
0.630,
0.126
0.775).
model,
filled
(1207
3,112
our
database
built
SOC
stock
(4,275
17,282
samples).
This
study
be
a
good
reference
gap-filling
depending
on
data
availability,
thus
contribute
deeper
understanding
C
related
climate
change
mitigation,
ecological
balance
preservation
promotion.
Archives of Agronomy and Soil Science,
Год журнала:
2025,
Номер
71(1), С. 1 - 17
Опубликована: Янв. 6, 2025
Soil
organic
matter
(SOM)
has
a
vital
role
in
maintaining
soil
quality
and
ecosystem
functions.
However,
predicting
its
spatial
distribution
remains
challenging
task
since
it
was
affected
by
various
environmental
covariates.
To
address
this
limitation,
novel
approach
integrating
Bayesian
technique
into
the
random
forest
(RF)
algorithm
proposed
research.
A
total
of
94
surficial
samples
from
top
30
cm
eight
key
covariates
were
utilized
for
training
testing
with
70:30
ratio.
According
to
results,
enhanced
RF
model
demonstrated
significant
improvement
accuracy
(RMSE
=
0.31%;
MAE
0.25%,
R2
0.79,
Acc
0.81)
compared
traditional
0.66%;
0.48%,
0.10,
0.61).
The
four
including
rainfall,
distance
sea,
water
bodies,
altitude
explained
74.07%,
75.37%
variability
SOM
content
models,
respectively.
Locations
high
characterized
abundant
greater
proximity
rivers,
low
elevations.
These
findings
introduce
reliable
context
complex
changes.
Frontiers in Plant Science,
Год журнала:
2025,
Номер
16
Опубликована: Фев. 19, 2025
Cultivated
land
quality
degradation
is
a
critical
challenge
to
food
security,
requiring
effective
nature-based
restoration
strategies
based
on
comprehensive
assessments
of
quality.
However,
existing
methods
are
often
costly,
limited
in
scope,
and
fail
capture
the
multidimensional
complexity
processes.
This
study
integrated
vegetation
indices,
topographic
data,
soil
physical
chemical
properties
construct
model
for
identifying
cultivated
degradation.
Remote
sensing
indices
were
calculated
using
Google
Earth
Engine,
enabling
large-scale
spatial
analysis.
Machine
learning,
combined
with
SHapley
Additive
exPlanations
(SHAP),
was
employed
explore
driving
factors
The
results
indicate
that
11.86%
Yugan
County
degraded,
primarily
central
plain
riparian
zones,
driven
by
both
natural
(precipitation,
temperature)
anthropogenic
(straw
incorporation,
fertilization
management).
Soil
erosion
concentrated
southern
hills
near
rivers,
fertility
decline
occurred
plain,
acidification
evenly
distributed
generally
low
levels.
Based
these
findings,
vegetation-based
solutions,
including
deep-rooted
crops,
crop
rotation
intercropping,
straw
proposed
address
different
types
support
sustainable
management.
