International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2024,
Номер
132, С. 104081 - 104081
Опубликована: Авг. 1, 2024
Understanding
soil
moisture
dynamics
is
crucial
for
crop
growth.
The
digital
mapping
of
field
distribution
provides
valuable
information
agricultural
water
management.
optical
satellite
data
fine
scale
a
region.
However,
these
are
greatly
limited
due
to
cloud
contamination
and
revisit
period.
Despite
the
reported
beneficial
effects
spatiotemporal
fusion
methods,
accurate
estimates
high-resolution
through
still
unclear,
particularly
when
using
Sentinel-2/3
images.
This
study
introduces
new
estimation
framework
integrating
spatio-temporal
spectral
from
images
machine
learning
algorithm,and
thus
provide
spatiotemporally
continuous
estimation.
includes
four
methods
(ESTARRFM,
Fit-FC,
FSDAF
STFMF)
models
(PLSR,
SVM,
RF
GBRT).
feasibility
was
validated
in
Hetao
Irrigation
Area
Inner
Mongolia,
China.
results
showed
that
fused
image
generated
by
Fit-FC
visually
closest
true
image,
followed
ESTARFM,
FSDAF,
STFMF.
fusion-machine
provided
reliable
multi-layer
(0
∼
20,
20
40
60
cm)
irrigation
area.
dense
time
series
facilitated
detection
events
irrigated
farmland.
Our
findings
highlighted
effectiveness
providing
daily
monitoring
farmland
on
large
scale.
These
high
spatial–temporal
resolution
growth
resource
management,
contributing
further
expanding
application
remote
sensing
precision
agriculture.
Remote Sensing,
Год журнала:
2025,
Номер
17(5), С. 837 - 837
Опубликована: Фев. 27, 2025
Soil
moisture
(SM)
monitoring
in
farmland
at
a
regional
scale
is
crucial
for
precision
irrigation
management
and
ensuring
food
security.
However,
existing
methods
SM
estimation
encounter
significant
challenges
related
to
accuracy,
generalizability,
automation.
This
study
proposes
an
integrated
data
fusion
method
systematically
assess
the
potential
of
three
automated
machine
learning
(AutoML)
frameworks—tree-based
pipeline
optimization
tool
(TPOT),
AutoGluon,
H2O
AutoML—in
retrieving
SM.
To
evaluate
impact
input
variables
on
six
scenarios
were
designed:
multispectral
(MS),
thermal
infrared
(TIR),
MS
combined
with
TIR,
auxiliary
data,
TIR
comprehensive
combination
MS,
data.
The
research
was
conducted
winter
wheat
cultivation
area
within
People’s
Victory
Canal
Irrigation
Area,
focusing
0–40
cm
soil
layer.
results
revealed
that
scenario
incorporating
all
types
(MS
+
auxiliary)
achieved
highest
retrieval
accuracy.
Under
this
scenario,
AutoML
frameworks
demonstrated
optimal
performance.
AutoGluon
superior
performance
most
scenarios,
particularly
excelling
scenario.
It
accuracy
Pearson
correlation
coefficient
(R)
value
0.822,
root
mean
square
error
(RMSE)
0.038
cm3/cm3,
relative
(RRMSE)
16.46%.
underscores
critical
role
strategies
enhancing
highlights
advantages
regional-scale
retrieval.
findings
offer
robust
technical
foundation
theoretical
guidance
advancing
efficient
monitoring.
Agronomy,
Год журнала:
2023,
Номер
13(9), С. 2302 - 2302
Опубликована: Авг. 31, 2023
In
India,
agriculture
serves
as
the
backbone
of
economy,
and
is
a
primary
source
employment.
Despite
setbacks
caused
by
COVID-19
pandemic,
allied
sectors
in
India
exhibited
resilience,
registered
growth
3.4%
during
2020–2121,
even
overall
economic
declined
7.2%
same
period.
The
improvement
sector
holds
paramount
importance
sustaining
increasing
population
safeguarding
food
security.
Consequently,
researchers
worldwide
have
been
concentrating
on
digitally
transforming
leveraging
advanced
technologies
to
establish
smart,
sustainable,
lucrative
farming
systems.
advancement
remote
sensing
(RS)
machine
learning
(ML)
has
proven
beneficial
for
farmers
policymakers
minimizing
crop
losses
optimizing
resource
utilization
through
valuable
insights.
this
paper,
we
present
comprehensive
review
studies
dedicated
application
RS
ML
addressing
agriculture-related
challenges
India.
We
conducted
systematic
literature
following
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analysis
(PRISMA)
guidelines
evaluated
research
articles
published
from
2015
2022.
objective
study
shed
light
both
technique
across
key
agricultural
domains,
encompassing
“crop
management”,
“soil
“water
management,
ultimately
leading
their
improvement.
This
primarily
focuses
assessing
current
status
using
intelligent
geospatial
data
analytics
Indian
agriculture.
Majority
were
carried
out
management
category,
where
deployment
various
sensors
led
yielded
substantial
improvements
monitoring.
integration
technology
techniques
can
enable
an
approach
monitoring,
thereby
providing
recommendations
insights
effective
management.
