Abstract.
Melt
ponds
on
the
Arctic
sea
ice
affect
radiative
balance
of
region
as
they
introduce
darkening
during
summer.
Temporal
and
spatial
extent
ponding
well
its
amplitude
reflect
state
are
important
for
our
understanding
change.
Remote
sensing
retrievals
melt
pond
fraction
(MPF)
provide
information
both
present
development
change
throughout
years,
which
is
a
valuable
in
context
climate
amplification.
In
this
work,
we
transfer
earlier
published
Pond
Detector
remote
retrieval
(MPD)
to
Ocean
Land
Colour
Instrument
(OLCI)
data
onboard
Sentinel-3
satellite
so
complement
existing
Medium
Resolution
Imaging
Spectrometer
(MERIS)
MPF
dataset
(2002–2011)
from
Environmental
Satellite
(ENVISAT)
with
recent
(2017–present).
To
evaluate
bias
product,
comparisons
Sentinel-2
MultiSpectral
(MSI)
high
resolution
imagery
presented,
addition
validation
studies.
Both
MERIS
OLCI
MPD
tend
overestimate
small
MPFs,
can
be
attributed
presence
water
saturated
snow
before
onset.
Good
agreement
middle
range
observed,
areas
exceptionally
=
100
%
recognized
well.
The
MPFs
were
reprocessed
using
an
improved
cloud
clearing
routine
together
internally
consistent
dataset,
allows
analyse
past
20
years.
Although
total
summer
hemispheric
trend
moderate
+0.75
per
decade,
regional
weekly
trends
display
pronounced
dynamic
−10
+20
depending
region.
We
conclude
following
effects:
global
onset
shifted
towards
spring
by
at
least
2
weeks,
happening
late
May
years
compared
early-mid
June
beginning
dataset.
there
regime
East
Siberian
Laptev
Sea
dominating
not
Beaufort
Gyre
before.
Central
Arctic,
North
Greenland
CAA
show
signs
increasing
first
year
(FYI)
daily
gridded
averages
available
webpage
Institute
Physics,
University
Bremen,
historic
ENVISAT
data,
ongoing
operational
processing
data.
Geographies,
Journal Year:
2024,
Volume and Issue:
4(3), P. 441 - 461
Published: July 26, 2024
This
comprehensive
review
explores
the
ecological
significance
of
Argane
stands
(Argania
spinosa)
in
southwestern
Morocco
and
pivotal
role
remote
sensing
technology
monitoring
forest
ecosystems.
stands,
known
for
their
resilience
semi-arid
arid
conditions,
serve
as
a
keystone
species,
preventing
soil
erosion,
maintaining
balance,
providing
habitat
sustenance
to
diverse
wildlife
species.
Additionally,
they
produce
an
extremely
valuable
oil,
offering
economic
opportunities
cultural
local
communities.
Remote
tools,
including
satellite
imagery,
LiDAR,
drones,
radar,
GPS
precision,
have
revolutionized
our
capacity
remotely
gather
data
on
health,
cover,
responses
environmental
changes.
These
technologies
provide
precise
insights
into
canopy
structure,
density,
individual
tree
enabling
assessments
stand
populations
detection
abiotic
stresses,
biodiversity,
conservation
evaluations.
Furthermore,
plays
crucial
vegetation
productivity,
drought
stress,
contributing
sustainable
land
management
practices.
underscores
transformative
impact
safeguarding
ecosystems,
particularly
highlights
its
potential
continued
advancements
research
efforts.
Abstract.
The
revolutionary
advances
of
Artificial
Intelligence
(AI)
in
the
past
decade
have
brought
transformative
innovation
across
science
and
engineering
disciplines.
Also
field
Arctic
science,
we
witnessed
an
increasing
trend
adoption
AI,
especially
deep
learning,
to
support
analysis
big
data
facilitate
new
discoveries.
In
this
paper,
provide
a
comprehensive
review
applications
learning
sea
ice
remote
sensing
domains,
focusing
on
problems
such
as
lead
detection,
thickness
estimation,
concentration,
extent
forecasting
motion
detection
well
type
classification.
addition
discussing
these
applications,
also
summarize
technological
that
customized
solutions,
including
loss
functions
strategies
better
understand
dynamics.
To
promote
growth
exciting
interdisciplinary
field,
further
explore
several
research
areas
where
community
can
benefit
from
cutting-edge
AI
technology.
These
include
improving
multi-modal
capabilities,
enhancing
model
accuracy
measuring
prediction
uncertainty,
leveraging
foundation
models,
deepening
integration
with
physics-based
models.
We
hope
paper
serve
cornerstone
progress
using
inspire
field.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(20), P. 3764 - 3764
Published: Oct. 10, 2024
Revolutionary
advances
in
artificial
intelligence
(AI)
the
past
decade
have
brought
transformative
innovation
across
science
and
engineering
disciplines.
In
field
of
Arctic
science,
we
witnessed
an
increasing
trend
adoption
AI,
especially
deep
learning,
to
support
analysis
big
data
facilitate
new
discoveries.
this
paper,
provide
a
comprehensive
review
applications
learning
sea
ice
remote
sensing
domains,
focusing
on
problems
such
as
lead
detection,
thickness
estimation,
concentration
extent
forecasting,
motion
type
classification.
addition
discussing
these
applications,
also
summarize
technological
that
customized
solutions,
including
loss
functions
strategies
better
understand
dynamics.
To
promote
growth
exciting
interdisciplinary
field,
further
explore
several
research
areas
where
community
can
benefit
from
cutting-edge
AI
technology.
These
include
improving
multimodal
capabilities,
enhancing
model
accuracy
measuring
prediction
uncertainty,
leveraging
foundation
models,
deepening
integration
with
physics-based
models.
We
hope
paper
serve
cornerstone
progress
using
inspire
field.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(20), P. 8889 - 8889
Published: Oct. 14, 2024
As
the
global
climate
changes,
there
is
an
increasing
focus
on
oceans
and
their
protection
exploitation.
However,
exploration
of
necessitates
construction
marine
equipment,
siting
such
equipment
has
become
a
significant
challenge.
With
ongoing
development
computers,
machine
learning
using
remote
sensing
data
proven
to
be
effective
solution
this
problem.
This
paper
reviews
history
technology,
introduces
conditions
required
for
site
selection
through
measurement
analysis,
uses
cluster
analysis
methods
identify
areas
as
research
hotspot
ocean
sensing.
The
aims
integrate
into
Through
review
discussion
article,
limitations
shortcomings
current
stage
are
identified,
relevant
proposals
put
forward.
The cryosphere,
Journal Year:
2025,
Volume and Issue:
19(1), P. 83 - 105
Published: Jan. 10, 2025
Abstract.
Melt
ponds
on
Arctic
sea
ice
affect
the
radiative
balance
of
region
as
they
introduce
darkening
during
summer.
The
temporal
extent
and
spatial
ponding,
well
its
amplitude,
reflect
state
are
important
for
our
understanding
change.
Remote
sensing
retrievals
melt
pond
fraction
(MPF)
provide
information
both
present
development
change
throughout
years,
which
is
valuable
in
context
climate
amplification.
In
this
work,
we
transfer
earlier
published
Pond
Detector
(MPD)
remote
retrieval
to
Ocean
Land
Colour
Instrument
(OLCI)
data
board
Sentinel-3
satellite
so
complement
existing
Medium
Resolution
Imaging
Spectrometer
(MERIS)
MPF
dataset
(2002–2011)
from
Environmental
Satellite
(ENVISAT)
with
recent
(2017–present).
To
evaluate
bias
product,
comparisons
Sentinel-2
MultiSpectral
(MSI)
high-resolution
imagery
presented,
addition
validation
studies.
Both
MERIS
OLCI
MPD
tend
overestimate
small
MPFs
(ranging
0
0.2),
can
be
attributed
presence
water-saturated
snow
before
onset
ponding.
Good
agreement
middle-range
(0.2–0.8)
observed,
areas
exceptionally
high
=
100
%
recognized
well.
were
reprocessed
using
an
improved
cloud
clearing
routine
together
internally
consistent
dataset,
allows
past
20
years
analyzed.
Although
total
summer
hemispheric
trend
moderate,
at
+0.75
per
decade,
regional
weekly
trends
display
a
pronounced
dynamic
range
−10
+20
depending
region.
We
conclude
following
effects:
global
shifted
towards
spring
by
least
2
weeks,
happening
late
May
compared
early
June
mid-June
beginning
dataset.
There
has
been
regime
East
Siberian
Laptev
Sea
dominating
not
Beaufort
Gyre
before.
central
Arctic,
north
Greenland
Canadian
Archipelago
(CAA)
have
shown
signs
increasing
first-year
(FYI)
years.
daily
gridded
averages
available
web
page
Institute
Physics,
University
Bremen,
historic
ENVISAT
ongoing
operational
processing
data.
