High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model
Remote Sensing,
Год журнала:
2025,
Номер
17(4), С. 678 - 678
Опубликована: Фев. 17, 2025
Accurate
digital
soil
organic
carbon
mapping
is
of
great
significance
for
regulating
the
global
cycle
and
addressing
climate
change.
With
advent
remote
sensing
big
data
era,
multi-source
multi-temporal
techniques
have
been
extensively
applied
in
Earth
observation.
However,
how
to
fully
mine
time-series
high-accuracy
SOC
remains
a
key
challenge.
To
address
this
challenge,
study
introduced
new
idea
mining
data.
We
used
413
topsoil
samples
from
southern
Xinjiang,
China,
as
an
example.
By
(Sentinel-1/2)
2017
2023,
we
revealed
temporal
variation
pattern
correlation
between
Sentinel-1/2
SOC,
thereby
identifying
optimal
time
window
monitoring
using
integrating
environmental
covariates
super
ensemble
model,
achieved
Southern
China.
The
results
showed
following
aspects:
(1)
windows
were
July–September
July–August,
respectively;
(2)
modeling
accuracy
sensor
integrated
with
was
superior
single-source
alone.
In
model
based
on
data,
cumulative
contribution
rate
Sentinel-2
51.71%
higher
than
that
Sentinel-1
data;
(3)
stacking
model’s
predictive
performance
outperformed
weight
average
simple
models.
Therefore,
covariates,
driven
represents
strategy
mapping.
Язык: Английский
Satellite Soil Observation (Satsoil): Extraction of Bare Soil Reflectance for Soil Organic Carbon Mapping on Google Earth Engine
Опубликована: Янв. 1, 2025
Язык: Английский
Integrating GIS and Remote Sensing for Soil Attributes Mapping in Degraded Pastures of the Brazilian Cerrado
Soil Advances,
Год журнала:
2025,
Номер
unknown, С. 100044 - 100044
Опубликована: Март 1, 2025
Язык: Английский
Comparing sentinel-2 and Landsat 8 spectral reflectance indices for predicting soil organic carbon
Environmental Earth Sciences,
Год журнала:
2025,
Номер
84(8)
Опубликована: Апрель 1, 2025
Язык: Английский
Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities
Geoderma,
Год журнала:
2025,
Номер
457, С. 117298 - 117298
Опубликована: Апрель 22, 2025
Язык: Английский
Mapping surface soil organic carbon in the coal–grain composite area: threshold and interaction effects of coal mining activities
Environmental Sciences Europe,
Год журнала:
2025,
Номер
37(1)
Опубликована: Март 26, 2025
Язык: Английский
Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China
Soil and Tillage Research,
Год журнала:
2024,
Номер
246, С. 106357 - 106357
Опубликована: Ноя. 13, 2024
Язык: Английский
Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland)
Land,
Год журнала:
2024,
Номер
13(12), С. 2229 - 2229
Опубликована: Дек. 20, 2024
This
study
presents
an
approach
for
predicting
soil
class
probabilities
by
integrating
synthetic
composite
imagery
of
bare
with
long-term
vegetation
remote
sensing
data
and
survey
data.
The
goal
is
to
develop
detailed
maps
the
agro-innovation
center
“Orlovka-AIC”
(Samara
Region),
a
focus
on
lithological
heterogeneity.
Satellite
were
sourced
from
cloud-filtered
collection
Landsat
4–5
7
images
(April–May,
1988–2010)
8–9
(June–August,
2012–2023).
Bare
surfaces
identified
using
threshold
values
NDVI
(<0.06),
NBR2
(<0.05),
BSI
(>0.10).
Synthetic
generated
calculating
median
reflectance
across
available
spectral
bands.
Following
adoption
no-till
technology
in
2012,
average
additionally
calculated
assess
condition
agricultural
lands.
Seventy-one
sampling
points
within
classified
both
Russian
WRB
classification
systems.
Logistic
regression
was
applied
pixel-based
prediction.
model
achieved
overall
accuracy
0.85
Cohen’s
Kappa
coefficient
0.67,
demonstrating
its
reliability
distinguishing
two
main
classes:
agrochernozems
agrozems.
resulting
map
provides
robust
foundation
sustainable
land
management
practices,
including
erosion
prevention
use
optimization.
Язык: Английский