Recent Advances in Graphene-Based Humidity Sensors With the Focus on Structural Design: A Review
IEEE Sensors Journal,
Год журнала:
2024,
Номер
24(13), С. 20289 - 20311
Опубликована: Май 17, 2024
The
advent
of
the
5G
era
means
that
concepts
robot,
VR/AR,
UAV,
smart
home,
healthcare
based
on
IoT
(Internet
Things)
have
gradually
entered
human
life.
Since
then,
intelligent
life
has
become
dominant
direction
social
development.
Humidity
sensors,
as
humidity
detection
tools,
not
only
convey
comfort
living
environment,
but
also
display
great
significance
in
fields
meteorology,
medicine,
agriculture
and
industry.
Graphene-based
materials
exhibit
tremendous
potential
sensing
owing
to
their
ultra-high
specific
surface
area
excellent
electron
mobility
under
room
temperature
for
application
sensing.
This
review
begins
with
introduction
examples
various
synthesis
strategies
graphene,
followed
by
device
structure
working
mechanism
graphene-based
sensor.
In
addition,
several
different
structural
design
methods
graphene
are
summarized,
demonstrating
can
optimize
performance
bring
significant
advantages
Finally,
key
challenges
hindering
further
development
practical
high-performance
sensors
discussed,
presenting
future
perspectives.
Язык: Английский
Dynamic vegetation parameter retrieval algorithm for SMAP L-band radiometer observations
Remote Sensing of Environment,
Год журнала:
2025,
Номер
319, С. 114641 - 114641
Опубликована: Фев. 6, 2025
Язык: Английский
Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model
Sensors,
Год журнала:
2025,
Номер
25(8), С. 2503 - 2503
Опубликована: Апрель 16, 2025
Accurate
flood
monitoring
and
forecasting
techniques
are
important
continue
to
be
developed
for
improved
disaster
preparedness
mitigation.
Flood
estimation
using
satellite
observations
with
deep
learning
algorithms
is
effective
in
detecting
patterns
environmental
relationships
that
may
overlooked
by
conventional
methods.
Soil
Moisture
Active
Passive
(SMAP)
fractional
water
(FW)
was
used
as
a
reference
estimate
areas
long
short-term
memory
(LSTM)
model
combination
of
soil
moisture
information,
rainfall
forecasts,
floodplain
topography.
To
perform
modeling
LSTM,
datasets
different
spatial
resolutions
were
resampled
30
m
resolution
bicubic
interpolation.
The
model’s
efficacy
quantified
validating
the
LSTM-based
inundation
area
mask
from
Senti-nel-1
SAR
images
regions
topographic
characteristics.
average
under
curve
(AUC)
value
LSTM
0.93,
indicating
high
accuracy
FW.
confusion
matrix-derived
metrics
validate
had
high-performance
~0.9.
SMAP
FW
showed
optimal
performance
low-covered
vegetation,
seasonal
variations
flat
regions.
estimates
show
methodological
promise
proposed
framework
resilience.
Язык: Английский
Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation
Xuqian Bai,
Shuailong Fan,
Ruiqi Li
и другие.
Agricultural Water Management,
Год журнала:
2025,
Номер
314, С. 109544 - 109544
Опубликована: Май 6, 2025
Язык: Английский
Dynamic Vegetation Parameter Retrieval Algorithm for Smap L-Band Radiometer Observations
Опубликована: Янв. 1, 2024
Vegetation
Optical
Depth
(VOD),
obtained
from
passive
microwave
sensors,
quantifies
Water
Content
(VWC)
and
complements
conventional
vegetation
indices.
Recent
studies
on
Soil
Moisture
(SM)
VOD
retrieval
algorithms
identified
that
is
more
susceptible
to
errors
due
the
Radiative
Transfer
Model
(RTM)
its
parameterization
than
SM.
The
present
work
aims
address
this
limitation.
We
initially
characterized
error
propagation
ω
h
parameters
in
through
synthetic
experiments.
These
experiments
also
indicate
notable
of
assuming
a
temporally
constant
retrievals,
which
could
be
resolved
using
time-varying
parameter.
To
improve
characterization,
we
proposed
Dynamic
Parameter
Algorithm
(DVPA)
retrieve
simultaneously,
along
with
parameter
applied
L-band
SMAP
brightness
temperatures.
DPVA
based
Two-Stream
emission
model
(2S-EM)
RTM.
Retrievals
are
novel
multi-temporal
inversion
coupled
regularization
scheme.
Level-3
SM
supplied
as
one
critical
inputs.
DVPA,
proof-of-concept,
ten
reference
sites
varying
conditions.
retrieved
DVPA
compared
optical
indices
baseline
product
(Regularized
Dual
Channel
Algorithm-RDCA).
estimates
outperform
RDCA
terms
correlation
(R)
lagged
Regularization
ensured
optimum
filtering
noise
retrievals.
Retrieval
dynamic
helped
resolve
VOD,
resulting
improved
correspondence
growth
patterns
Given
generic
structure,
scalable
applies
other
sensors.
Язык: Английский
Assessment of the State of Plant Biomass Based on the Integration of Multispectral Sensors of Optical and Radio Ranges
E3S Web of Conferences,
Год журнала:
2024,
Номер
539, С. 02035 - 02035
Опубликована: Янв. 1, 2024
One
of
the
main
tasks
using
remote
sensing
in
agriculture
for
precision
farming
purposes
is
to
identify
management
zones
or
within
which
timing
and
parameters
agrotechnical
measures
differ
significantly.
To
clarify
boundaries
these
zones,
it
proposed
use
jointly
data
on
soil
moisture
(electrical
conductivity)
normalized
plant
index
(NDVI)
a
field
about
70
hectares.
Based
spatial
variations
humidity
obtained
bistatic
radar
system
electrical
conductivity
electromagnetic
scanning,
as
well
NDVI
indices
multispectral
cameras,
maps
distribution
are
constructed.
determine
control
fuzzy
clustering
algorithm
was
used,
three
target
classes
assessing
state
biomass
with
restrictions
percentage
were
identified.
An
analysis
813
points
surface
carried
out
reference
geographical
coordinates,
elements
array
assigned
one
corresponding
zones.
The
results
arrays
formed
by
allow
us
conclude
that
possible
conditions
significant
heterogeneity
studied
fields
terms
physico-chemical
properties
relief.
Язык: Английский
Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
Applied Sciences,
Год журнала:
2024,
Номер
14(24), С. 11875 - 11875
Опубликована: Дек. 19, 2024
Soil
moisture
is
a
key
variable
that
affects
ecosystem
carbon
and
water
cycles
can
directly
affect
climate
change.
Remote
sensing
the
best
way
to
obtain
global
soil
data.
Currently,
remote
products
have
coarse
spatial
resolution,
which
limits
their
application
in
agriculture,
ecological
environment,
urban
planning.
downscaling
methods
rely
mainly
on
optical
Affected
by
weather,
discontinuity
of
data
has
greater
impact
results.
The
synthetic
aperture
radar
(SAR)
backscatter
coefficient
strongly
correlated
with
moisture.
This
study
was
based
Google
Earth
Engine
(GEE)
platform,
integrated
Moderate-Resolution
Imaging
Spectroradiometer
(MODIS)
SAR
backscattering
coefficients
used
machine
learning
downscale
product,
reducing
original
resolution
10
km
1
100
m.
results
were
verified
using
situ
observation
from
Shandian
River
Wudaoliang.
show
two
areas,
after
adding
are
better
than
before.
In
River,
R
increases
0.28
0.42.
Wudaoliang,
value
0.54
0.70.
RMSE
0.03
(cm3/cm3).
downscaled
play
an
important
role
resource
management,
natural
disaster
monitoring,
environmental
protection,
other
fields.
monitoring
management
disasters,
such
as
droughts
floods,
it
provide
information
support
for
decision-makers
help
formulate
more
effective
emergency
response
plans.
During
droughts,
affected
areas
be
identified
timely
manner,
allocation
scheduling
resources
optimized,
thereby
agricultural
losses.
Язык: Английский