National
soil
organic
carbon
(SOC)
maps
are
essential
to
improve
greenhouse
gas
accounting
and
support
climate-smart
agriculture.
Large-scale
SOC
models
based
on
wall-to-wall
information
from
remote
sensing
remain
a
challenge
due
the
high
diversity
of
natural
conditions
difficulty
for
spatial
location
samples.
In
this
study,
we
tested
if
implementation
local
ensemble
(LEM)
can
be
used
predictions
Landsat-based
reflectance
composites
(SRC)
Germany.
We
divided
research
area
into
30
times
km
tiles
calculated
generalized
linear
(GLM)
random,
nearby
observations.
Based
GLMs,
were
predicted
aggregated
using
moving
window
approach.
The
variable
importance
was
analyzed
identify
dependencies
in
correlation
between
SRC
SOC.
For
final
map,
Random
Forest
(RF)
model
trained
predictions,
SRC,
full
set
training
samples
agricultural
inventory.
results
show
that
LEM
able
accuracy
(R2
=
0.68;
RMSE
5.6
g
kg–1),
compared
single,
global
0.52;
6.8
kg–1).
spectral
bands
showed
clear
patterns
throughout
area.
Differences
explained
by
conditions,
influencing
properties.
Compared
widely
adopted
integration
distance
covariates
such
as
geographical
coordinates,
reduce
autocorrelation
greater
extent
prediction
accuracy,
especially
underrepresented
values.
presents
new
method
account
increase
interpretability
DSM
models.
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(12), P. 3070 - 3070
Published: June 12, 2023
Soils
are
at
the
crossroads
of
many
existential
issues
that
humanity
is
currently
facing.
a
finite
resource
under
threat,
mainly
due
to
human
pressure.
There
an
urgent
need
map
and
monitor
them
field,
regional,
global
scales
in
order
improve
their
management
prevent
degradation.
This
remains
challenge
high
often
complex
spatial
variability
inherent
soils.
Over
last
four
decades,
major
research
efforts
field
pedometrics
have
led
development
methods
allowing
capture
nature
As
result,
digital
soil
mapping
(DSM)
approaches
been
developed
for
quantifying
soils
space
time.
DSM
monitoring
become
operational
thanks
harmonization
databases,
advances
modeling
machine
learning,
increasing
availability
spatiotemporal
covariates,
including
exponential
increase
freely
available
remote
sensing
(RS)
data.
The
latter
boosted
DSM,
resolution
assessing
changes
through
We
present
review
main
contributions
developments
French
(inter)national
research,
which
has
long
history
both
RS
DSM.
Thanks
SPOT
satellite
constellation
started
early
1980s,
communities
pioneered
using
sensing.
describes
data,
tools,
imagery
support
predictions
wide
range
properties
discusses
pros
cons.
demonstrates
data
frequently
used
(i)
by
considering
as
substitute
analytical
measurements,
or
(ii)
covariates
related
controlling
factors
formation
evolution.
It
further
highlights
great
potential
provides
overview
challenges
prospects
future
sensors.
opens
up
broad
use
natural
monitoring.
Geoderma,
Journal Year:
2024,
Volume and Issue:
444, P. 116850 - 116850
Published: March 19, 2024
National
soil
organic
carbon
(SOC)
maps
are
essential
to
improve
greenhouse
gas
accounting
and
support
climate-smart
agriculture.
Large-scale
SOC
models
based
on
wall-to-wall
information
from
remote
sensing
remain
a
challenge
due
the
high
diversity
of
natural
conditions
difficulty
for
spatial
location
samples.
In
this
study,
we
tested
if
implementation
local
ensemble
(LEM)
can
be
used
predictions
Landsat-based
reflectance
composites
(SRC)
Germany.
For
this,
divided
research
area
into
30
times
km
tiles
calculated
generalized
linear
(GLM)
random,
nearby
observations.
Based
GLMs,
were
predicted
aggregated
using
moving
window
approach.
The
variable
importance
was
analyzed
identify
dependencies
in
correlation
between
SRC
SOC.
final
map,
Random
Forest
(RF)
model
trained
predictions,
SRC,
full
set
training
samples
agricultural
inventory.
results
show
that
LEM
able
accuracy
(R2
=
0.68;
RMSE
5.6
g
kg−1),
compared
single,
global
0.52;
6.8
kg−1).
spectral
bands
showed
clear
patterns
throughout
area.
Differences
explained
by
conditions,
influencing
properties.
Compared
widely
adopted
integration
distance
covariates
such
as
geographical
coordinates,
reduce
autocorrelation
greater
extent
prediction
accuracy,
especially
underrepresented
values.
presents
new
method
integrate
increase
interpretability
DSM
models.
Geoderma,
Journal Year:
2024,
Volume and Issue:
441, P. 116765 - 116765
Published: Jan. 1, 2024
Acoustic
waves
offer
a
non-destructive,
safe,
and
cost-effective
means
of
monitoring
the
environment,
with
potential
application
in
soil
water
content
monitoring.
However,
extracting
information
from
acoustic
signals
is
still
challenging.
To
tackle
this
issue,
we
have
developed
low-frequency
swept
signal
detection
device
system.
We
conducted
penetration
testing
using
signals.
The
swept-frequency
passing
through
were
transformed
into
time–frequency
spectrogram.
Using
Swin-Transformer
model,
established
regression
model
between
spectrogram
frequencies
content.
Predictions
made
both
on
laboratory
test
dataset
field
trials
calibrated
model.
results
indicate
that
RMSE,
MAE,
R2
values
observed
model's
outputs
(%)
for
are
0.191,
0.081,
0.999,
respectively,
In
case
trials,
predicted
6.715
%,
1.829
0.711,
respectively.
These
studies
demonstrate
method
highly
effective
predicting
content,
best
achieved
at
resolution
20
PPI
(Pixels
Per
Inch)
within
frequency
range
260–360
Hz.
It
provides
an
efficient
approach
detection,
effectively
resolves
difficulty
building
models
caused
by
single-parameter
limitation
traditional
European Journal of Soil Science,
Journal Year:
2025,
Volume and Issue:
76(1)
Published: Jan. 1, 2025
ABSTRACT
Multispectral
imaging
satellites
such
as
Sentinel‐2
are
considered
a
possible
tool
to
assist
in
the
mapping
of
soil
organic
carbon
(SOC)
using
images
bare
soil.
However,
reported
results
variable.
The
measured
reflectance
surface
is
not
only
related
SOC
but
also
several
other
environmental
and
edaphic
factors.
Soil
texture
one
factor
that
strongly
affects
reflectance.
Depending
on
spatial
correlation
with
SOC,
influence
may
improve
or
hinder
estimation
from
spectral
data.
This
study
aimed
investigate
these
influences
local
models
at
34
sites
different
pedo‐climatic
zones
across
10
European
countries.
were
individual
agricultural
fields
few
close
proximity.
For
each
site,
predict
clay
particle
size
fraction
developed
temporal
mosaics
images.
Overall,
predicting
was
difficult,
prediction
performances
ratio
performance
deviation
(RPD)
>
1.5
observed
8
12
for
clay,
respectively.
A
general
relationship
between
evident
explained
small
part
large
variability
we
sites.
Adding
information
additional
predictors
improved
average,
benefit
varied
average
relative
importance
bands
indicated
red
far‐red
regions
visible
spectrum
more
important
than
prediction.
opposite
true
region
around
2200
nm,
which
models.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(5), P. 1903 - 1903
Published: Feb. 26, 2024
This
paper
conducts
an
in-depth
exploration
of
carbon
farming
at
the
confluence
advanced
technology
and
EU
policy,
particularly
within
context
European
Green
Deal.
Emphasizing
technologies
readiness
levels
(TRL)
6–9,
study
critically
analyzes
synthesizes
their
practical
implementation
potential
in
agricultural
sector.
Methodologically,
integrates
a
review
current
with
analysis
policy
frameworks,
focusing
on
application
these
alignment
directives.
The
results
demonstrate
symbiotic
relationship
between
emerging
evolving
policies,
highlighting
how
technological
advancements
can
be
effectively
integrated
existing
proposed
legal
structures.
is
crucial
for
fostering
practical,
market-ready,
sustainable
practices.
Significantly,
this
underscores
importance
bridging
theoretical
research
commercialization.
It
proposes
pathway
transitioning
insights
into
innovative,
market-responsive
products,
thereby
contributing
to
approach
not
only
aligns
Deal
but
also
addresses
market
demands
environmental
evolution.
In
conclusion,
serves
as
critical
link
applications
farming.
offers
comprehensive
understanding
both
landscapes,
aiming
propel
solutions
step
dynamic
goals.
Geoderma,
Journal Year:
2024,
Volume and Issue:
449, P. 116987 - 116987
Published: Aug. 1, 2024
Sustainable
cropland
management
requires
quantitative
and
up-to-date
information
on
the
spatial
pattern
of
soil
organic
carbon
(SOC)
at
scales
relevant
for
implementing
targeted
conservation
measures.
Spectra-based
remote
sensing
SOC
in
croplands
is
promising,
but
it
extraction
high-quality
bare
pixels
that
enable
spatially
continuous
coverage.
Here,
we
aim
to
compare
predictive
capability
single-date
versus
multitemporal
compositing
images
an
intensively
cultivated
region
(4,700
km2)
northeast
China.
A
series
12
within
2017–2022
were
processed
passed
onto
three
approaches
(geometric
median,
univariate
mean
median)
create
mosaics.
Both
spectral
images,
together
with
laboratory-simulated
Sentinel-2
benchmark
data,
used
develop
partial
least
squares
regression,
Cubist
random
forest
models
via
100
bootstrapped
validations.
With
consistently
being
best
performing
algorithm
all
data
sources,
results
show
exhibited
temporally
unstable
performance
(R2:
0.30–0.67).
Among
approaches,
high-dimensional
geometric
median
composite
was
most
suitable
because
(i)
its
close
resemblance
laboratory
reference
robustness
outliers,
which
yielded
a
model
0.64;
RMSE:
2.24
g/kg)
outperforming
11
out
models;
(ii)
ability
retain
between-band
relationships
allowed
further
incorporation
SOC-relevant
indexes,
led
6.5
%
increase
prediction
accuracy.
The
resultant
map
highlighted
imaging
reveal
field-scale
degradation
patterns.
Future
work
should
explore
possibility
extending
purely
spectra-based
framework
integrated
mapping
monitoring
additional
biophysical
information.
