Geoderma,
Год журнала:
2023,
Номер
439, С. 116697 - 116697
Опубликована: Окт. 24, 2023
Optical
remote
sensing
satellites
provide
rapid
access
to
regional
topsoil
salinization
mapping.
However,
mapping
based
on
spectral
reflectance
is
always
affected
by
background
material
like
vegetation
cover,
straw
mulching
and
soil
types.
In
light
of
these
challenges,
this
study
investigates
the
potential
image
fusion,
where
images
original
bare
pixels
were
combined,
minimize
impact
cover
salinity
A
case
was
presented
for
typical
area
using
synchronized
Sentinel-2
MSI
(named
image)
255
ground-truth
data
collected
in
October
2020,
aligning
with
periods
salt
return.
Furthermore,
obtain
novel
pixels,
multi-temporal
acquired
during
two
distinct
intervals:
March
May
September
November,
spanning
years
from
2018
2021.
The
synthetic
(SYSI)
obtained
extracting
images.
Two
(original,
SYSI)
fused
non-negative
matrix
factorization
(NMF)
method,
named
SYSIfused.
Then,
stacking
machine
algorithm
used
under
different
types,
evaluating
SYSIfused
accuracy
prediction.
results
showed
outperformed
(the
R2
best
models
increased
0.054–0.242,
RMSE
MAE
decreased
0.049–0.780
0.012–0.546,
respectively).
Based
SYSIfused,
order
effect
types
coastal
bog
solonchaks
>
alluvial
cinnamon
coral
saline
overall
samples,
their
roles
improving
model
0.141,
0.085,
0.022,
0.012,
respectively.
Besides,
provided
prediction
performances
(R2
=
0.742,
0.377,
0.362).
This
introduces
concept
merging
SYSI,
resulting
a
significant
improvement
areas
covered
vegetation.
Sensors,
Год журнала:
2025,
Номер
25(7), С. 2184 - 2184
Опубликована: Март 30, 2025
Despite
extensive
use
of
Sentinel-2
(S-2)
data
for
mapping
soil
organic
carbon
(SOC),
how
to
fully
mine
the
potential
time-series
S-2
still
remains
unclear.
To
fill
this
gap,
study
introduced
an
innovative
approach
mining
data.
Using
200
top
samples
as
example,
we
revealed
temporal
variation
patterns
in
correlation
between
SOC
and
subsequently
identified
optimal
monitoring
time
window
SOC.
The
integration
environmental
covariates
with
multiple
ensemble
models
enabled
precise
arid
region
southern
Xinjiang,
China
(6109
km2).
Our
results
indicated
following:
(a)
exhibited
both
interannual
monthly
variations,
while
July
August
is
SOC;
(b)
adding
properties
texture
information
could
greatly
improve
accuracy
prediction
models.
Soil
contribute
8.85%
61.78%
best
model,
respectively;
(c)
among
different
models,
stacking
model
outperformed
weight
averaging
sample
terms
performance.
Therefore,
our
proved
that
spectral
from
window,
integrated
has
a
high
accurate
mapping.
Geoderma,
Год журнала:
2023,
Номер
439, С. 116697 - 116697
Опубликована: Окт. 24, 2023
Optical
remote
sensing
satellites
provide
rapid
access
to
regional
topsoil
salinization
mapping.
However,
mapping
based
on
spectral
reflectance
is
always
affected
by
background
material
like
vegetation
cover,
straw
mulching
and
soil
types.
In
light
of
these
challenges,
this
study
investigates
the
potential
image
fusion,
where
images
original
bare
pixels
were
combined,
minimize
impact
cover
salinity
A
case
was
presented
for
typical
area
using
synchronized
Sentinel-2
MSI
(named
image)
255
ground-truth
data
collected
in
October
2020,
aligning
with
periods
salt
return.
Furthermore,
obtain
novel
pixels,
multi-temporal
acquired
during
two
distinct
intervals:
March
May
September
November,
spanning
years
from
2018
2021.
The
synthetic
(SYSI)
obtained
extracting
images.
Two
(original,
SYSI)
fused
non-negative
matrix
factorization
(NMF)
method,
named
SYSIfused.
Then,
stacking
machine
algorithm
used
under
different
types,
evaluating
SYSIfused
accuracy
prediction.
results
showed
outperformed
(the
R2
best
models
increased
0.054–0.242,
RMSE
MAE
decreased
0.049–0.780
0.012–0.546,
respectively).
Based
SYSIfused,
order
effect
types
coastal
bog
solonchaks
>
alluvial
cinnamon
coral
saline
overall
samples,
their
roles
improving
model
0.141,
0.085,
0.022,
0.012,
respectively.
Besides,
provided
prediction
performances
(R2
=
0.742,
0.377,
0.362).
This
introduces
concept
merging
SYSI,
resulting
a
significant
improvement
areas
covered
vegetation.
