Deep Learning-Enhanced Insar for Spatiotemporal Groundwater Monitoring at Persepolis and Naqsh-E Rostam UNESCO Sites
Published: Jan. 1, 2025
Language: Английский
D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2025,
Volume and Issue:
139, P. 104496 - 104496
Published: April 3, 2025
Language: Английский
High-resolution anthropogenic emission inventories with deep learning in northern South America
Remote Sensing of Environment,
Journal Year:
2025,
Volume and Issue:
324, P. 114761 - 114761
Published: April 17, 2025
Language: Английский
Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan
Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi,
Journal Year:
2025,
Volume and Issue:
14(2), P. 615 - 629
Published: April 15, 2025
Water
is
essential
for
the
sustainability
of
life
and
healthy
functioning
ecosystems.
Increasing
pollution
poses
a
serious
threat
to
world's
waters,
making
monitoring
protection
water
quality
strategic
imperative.
Chlorophyll-a
one
most
important
indicators
ecosystem
health,
as
it
measure
photosynthetic
activity
phytoplankton
density,
lifeblood
aquatic
Remote
sensed
data
provide
unique
opportunity
analyse
chlorophyll-a
changes
in
lake
In
this
study,
concentration
was
modelled
by
machine
deep
learning
techniques
using
measurements,
Landsat-8
surface
reflectance
values
spectral
indices
Lake
Mogan
between
2018
2024.
The
RF,
ANN,
CNN
models
achieved
R²
0.84,
0.85,
0.92,
respectively.
With
its
ability
learn
relationships,
identify
patterns
complex
datasets,
superior
process
remote
sensing
imagery,
thematic
maps
were
generated
model,
which
performed
best
study.
results
study
demonstrate
potential
sensing-based
approaches
chlorophyll-a.
produce
highly
accurate
results,
provides
literature
with
an
effective
tool
future
studies.
Language: Английский
A review of studies on assessing water quality parameters based on the Google Earth Engine imagery
Remote Sensing Applications Society and Environment,
Journal Year:
2025,
Volume and Issue:
unknown, P. 101581 - 101581
Published: May 1, 2025
Language: Английский
A Synergistic Framework for Coupling Crop Growth, Radiative Transfer, and Machine Learning to Estimate Wheat Crop Traits in Pakistan
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(23), P. 4386 - 4386
Published: Nov. 24, 2024
The
integration
of
the
Crop
Growth
Model
(CGM),
Radiative
Transfer
(RTM),
and
Machine
Learning
Algorithm
(MLA)
for
estimating
crop
traits
represents
a
cutting-edge
area
research.
This
requires
in-depth
study
to
address
RTM
limitations,
particularly
similar
spectral
responses
from
multiple
input
combinations.
proposes
CGM
trait
retrieval
evaluates
performance
output-based
spectra
generation
estimation
without
biased
sampling
using
machine
learning
models.
Moreover,
PROSAIL
as
training
against
Harmonized
Landsat
Sentinel-2
(HLS)
testing
was
also
compared
with
HLS
data
only
an
alternative.
It
found
that
satellite
(HLS,
80:20)
not
consistently
performed
better,
but
(train)
(test)
had
satisfactory
results
uniform
samples
in
spite
differences
simulated
real
data.
PROSAIL-HLS
has
RMSE
0.67
leaf
index
(LAI),
5.66
µg/cm2
chlorophyll
ab
(Cab),
0.0003
g/cm2
dry
matter
content
(Cm),
0.002
water
(Cw)
only,
0.40
LAI,
3.28
Cab,
0.0002
Cm,
0.001
Cw.
Optimized
models,
namely
Extreme
Gradient
Boost
(XGBoost)
Support
Vector
(SVM)
Random
Forest
(RF)
Cm
Cw,
were
deployed
temporal
mapping
be
used
wheat
productivity
enhancement.
Language: Английский