Journal of Biomedical Research & Environmental Sciences,
Год журнала:
2023,
Номер
4(11), С. 1618 - 1623
Опубликована: Ноя. 1, 2023
Infrared
spectroscopy
has
emerged
as
a
powerful
tool
to
assess
soil
properties
for
both
environmental
science
and
agriculture.
Here,
we
explore
its
recent
trends
developments
assessment.
This
technique
is
an
alternative
that
counters
the
limitations
of
traditional
laboratory
methods,
offering
cost-effective
non-destructive
approach.
latest
in
innovation
landscape
infrared
assessment
are
explored,
providing
insights
on
broad
range
applications
into
future
trajectory
this
technology.
Firstly,
delve
agriculture,
highlighting
potential
prediction
many
attributes.
Next,
carbon
assessment,
emphasizing
importance
estimating
organic
stock
quality.
Soil
pollution
elemental
contents
addressed,
focusing
potentially
toxic
elements
concentrations
soil,
strongly
relevant
monitoring.
emerges
valuable
rapid
non-hazardous
content
physical
prediction,
traditionally
limited
texture
analysis,
extended
through
application
novel
approaches,
shedding
light
broader
technology
quality
The
ongoing
statistical
modeling
technological
also
showcased,
mainly
focused
machine
learning
methods.
Lastly,
spectral
libraries
emphasized,
such
Global
Spectral
Calibration
Library
Estimation
Service,
Brazilian
Library.
In
conclusion,
become
important
multitude
across
agricultural
contexts.
review
underscores
growing
advancing
standardization
reproducibility
sustainable
procedures,
ensuring
brighter
science.
Land Degradation and Development,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 3, 2025
ABSTRACT
Given
that
Sentinel‐2
(S2)
multispectral
images
provide
extensive
spatial
information
and
ground‐based
hyperspectral
data
capture
refined
spectral
characteristics,
their
integration
can
enhance
both
the
comprehensiveness
precision
of
surface
acquisition.
This
study
seeks
to
leverage
these
sources
develop
an
optimized
estimation
model
for
accurately
monitoring
large‐scale
soil
organic
carbon
(SOC)
content,
thereby
addressing
current
limitations
in
multi‐source
fusion
research.
In
this
study,
using
mathematical
transformation
discrete
wavelet
transform
process
ground
delta
oasis
Weigan
Kuqa
rivers
Xinjiang,
China,
combination
with
S2
image,
machine
learning
algorithms
were
employed
construct
models
SOC
content
total
variables
characteristic
variables,
inversion
oases
was
carried
out.
We
found
R
‐DWT‐H9
significantly
correlation
between
(
p
<
0.001).
The
accuracy
constructed
based
on
feature
selected
by
SPA
IRIV
generally
higher
than
variable
models.
IRIV‐RFR
had
highest
stable
capability.
values
2
training
validation
sets
0.66
0.64,
respectively.
RMSE
1.5
g∙kg
−1
,
RPD
>
1.4.
interior
oasis,
mainly
deficient
(61.35%)
or
relatively
(8.17%),
while
periphery
it
extremely
(30.48%).
Combine
providing
a
reference
evaluating
fertility
arid
regions.
Remote Sensing,
Год журнала:
2025,
Номер
17(8), С. 1363 - 1363
Опубликована: Апрель 11, 2025
Accurate
mapping
of
soil
organic
carbon
(SOC)
supports
sustainable
land
management
practices
and
accounting
initiatives
for
mitigating
climate
change
impacts.
This
study
presents
a
novel
meta-learner
framework
that
combines
multiple
machine
learning
algorithms
spectra
processing
to
optimize
SOC
prediction
using
the
PRISMA
hyperspectral
satellite
imagery
in
Doukkala
plain
Morocco.
The
employs
two-layer
structure
models.
first
layer
consists
Random
Forest
(RF),
Support
Vector
Regression
(SVR),
Partial
Least
Squares
(PLSR).
These
base
models
were
configured
data
smoothing,
transformation,
spectral
feature
selection
techniques,
based
on
70/30%
split.
second
utilizes
ridge
regression
model
as
integrate
predictions
from
Results
indicated
RF
SVR
performance
improved
primarily
with
selection,
while
PLSR
was
most
influenced
by
smoothing.
approach
outperformed
individual
models,
achieving
an
average
relative
improvement
48.8%
over
single
R2
0.65,
RMSE
0.194%,
RPIQ
2.247.
contributes
development
methodologies
predicting
properties
data.
