The
2015–2016
El
Niño-induced
drought
caused
biomass
loss
in
global
tropical
forests,
yet
the
recovery
duration
of
different
vegetation
components
(woody
components,
upper
canopies,
and
leaves)
remains
unknown.
Here,
we
use
satellite
remote
sensing
data
optical
depth
leaf
area
index,
with
varying
sensitivity
to
examine
during
event.
We
find
that
woody
component
had
slowest
compared
canopy
leaves,
displayed
greater
spatial
variability
between
continents.
Key
factors
influencing
include
severity,
moisture-related
climatic
conditions
(i.e.,
vapor
pressure
deficit,
precipitation,
soil
moisture),
seasonal
variations
temperature
precipitation.
Our
study
highlights
importance
for
maintaining
ecosystem
balance
under
disturbances
indicates
need
further
research
explore
mechanisms
long-term
impacts
on
forest
dynamics.
Woody
forests
have
a
slower
rate
from
severe
canopies
according
multiple
observations
across
tropics
2015-2016
Frontiers in Remote Sensing,
Journal Year:
2024,
Volume and Issue:
5
Published: July 30, 2024
Two
L-band
passive
microwave
satellite
sensors,
onboard
the
Soil
Moisture
and
Ocean
Salinity
(SMOS)
launched
in
2009
Active
Passive
(SMAP)
2015,
are
specifically
designed
for
surface
soil
moisture
(SM)
monitoring.
The
first
global
continuous
fused
SM
product
based
on
SMOS
SMAP
observations
(SMOS-SMAP-INRAE-BORDEAUX,
so-called
Fused-IB)
was
recently
released
to
public.
Currently,
performance
of
Fused-IB
has
only
been
evaluated
collectively
over
entire
data
records
study
period,
without
specific
evaluation
individual
seasons.
To
fill
this
gap,
intercompared
enhanced
SMAP-L3
version
6
(SMAP-E)
products
against
situ
from
International
Network
(ISMN)
2016
2020
regarding
whole
period
different
We
aim
investigate
these
two
at
time
scales
explore
potential
eco-hydrological
factors
(i.e.,
precipitation
vegetation)
driving
their
seasonal
variations.
Results
show
that
both
good
agreement
with
measurements,
demonstrating
high
median
correlation
(
R
)
low
ub
RMSD
(median
=
0.70
0.058
m
3
/m
vs.
0.68
0.059
SMAP-E)
during
2016–2020.
For
most
land
use
cover
(LULC)
types,
outperformed
SMAP-E
higher
accuracy
lower
errors,
particularly
forests,
partly
due
advantage
robust
SMAP-IB
(SMAP-INRAE-BORDEAUX)
algorithm
used
generate
which
avoids
pronounced
saturation
effects
vegetation
optical
depth
caused
by
relying
information.
Besides,
had
superior
performances
across
LULC
types
summer
(JJA)
autumn
(SON),
yet
increased
uncertainties
were
observed
grasslands,
croplands
spring
(MAM)
winter
(DJF).
These
could
be
mainly
attributed
growth
grasslands
croplands,
interception
water
rainfall
events
grasslands.
results
can
serve
as
a
reference
developers
enhance
thus
promote
hydro-meteorological
applications
benefit
radiometer
products.
Scientific Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Aug. 28, 2024
The
L-band
vegetation
optical
depth
data
garners
significant
interest
for
its
ability
to
effectively
monitor
vegetation,
thanks
minimal
saturation
within
this
frequency
range.
However,
the
existing
datasets
have
limited
temporal
coverage,
constrained
by
start
of
respective
satellite
missions.
Global
equivalent
AI-Based
Vegetation
Optical
Depth
or
GLAB-VOD
is
a
global
long-term
consistent
microwave
dataset
created
using
machine
learning
expand
SMAP-IB
VOD
coverage
from
2015-2020
2002-2020.
has
an
18-day
resolution
and
25
km
spatial
on
EASE2
grid
covers
An
auxiliary
daily
brightness
temperature
product,
called
GLAB-TB,
developed
in
parallel
ensures
consistency
product
across
time
periods
with
different
satellites.
As
result
consistency,
can
be
used
study
regional
trends
biomass
utilized
any
other
applications
where
necessary.
shows
excellent
correlation
globally
when
compared
(up
R
=
0.92)
canopy
height
(R
0.93),
outperforming
target
dataset,
VOD.
The
Soil
Moisture
Ocean
Salinity
(SMOS)
mission
carries
on-board
the
first
L-band
radiometer
launched
in
2010
to
retrieve
global-scale
soil
moisture
(SM)
and
Vegetation
Optical
Depth
(L-VOD).
SMOS-IC
version-2
is
latest
retrieval
algorithm
of
SMOS
over
land
surfaces,
it
outperforms
other
existing
algorithms.
Research
underway
improve
by
refining
surface
roughness
parameters
which
significantly
affect
performance
SM
L-VOD.
In
this
study,
we
present
a
new
version
(Version
2.1)
L-VOD
retrievals
featuring
novel
calibrations
global
roughness.
For
purpose,
retrieved
(through
Hr
parameter)
bare
soils
using
algorithm.
A
Random
Forest
(RF)
model
was
then
developed
predict
soils,
data
(soil
textural
properties
temperature)
terrain
as
explanatory
variables,
ultimately
facilitated
extrapolation
these
values
scale.The
predicted
from
RF
demonstrated
very
good
correspondence
with
(R2
=
0.89).
observed
dominant
influence
properties,
particularly
organic
content
(SOC),
indicated
litter
on
modeling
Hr.
newly
map
V2.1
high
spatial
variability
within
each
IGBP
cover
type.
Intercomparison
in-situ
ISMN
revealed
improved
vs
V2
CCI,
higher
correlation
R
(R_SMV2.1
0.69,
R_SMV2
0.67,
R_SMCCI
0.62)
lower
ubRMSE
(ubRMSE_SMV2.1
0.057
m3/m3,
ubRMSE_SMV2
0.060
m3/m3
ubRMSE_SMCCI
0.059
m3/m3).
Moreover,
Triple
Collocation
Analysis
(TCA)
evaluations
ECMWF-modelled
reference
dataset
yielded
SMV2.1
SMV2
most
regions
globe,
especially
for
ubRMSE.
terms
L-VOD,
VODV2.1
product
outperformed
VODV2
showing:
(i)
above-ground
biomass
products
(ii)
temporal
MODIS
NDVI
low
moderate
vegetated
regions.
approach
presented
here
offers
framework
calibrating
both
current
future
microwave
remote
sensing
missions
such
Active
Passive
(SMAP)
Copernicus
Imaging
Microwave
Radiometer
(CIMR).
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 11, 2024
Abstract
Tropical
rainforests
are
crucial
for
Earth's
health,
but
climate
change
is
making
severe
droughts
more
frequent.
The
2015–2016
El
Niño-induced
drought
caused
significant
biomass
loss,
yet
the
recovery
duration
of
different
vegetation
components
(woody
parts,
upper
canopies,
and
leaves)
remains
unknown.
This
study
employed
satellite
remote
sensing
data
L-band
Vegetation
Optical
Depth
(L-VOD),
X-band
VOD
(X-VOD),
Enhanced
Index
(EVI)
from
2010
to
2022,
characterized
by
having
sensitivities
components,
examine
these
in
tropical
evergreen
broadleaf
forest
(EBF)
regions
during
drought.
Results
showed
that
woody
component
had
slowest
recovery,
particularly
Africa,
which
took
longer
return
pre-drought
conditions
than
South
America.
Key
factors
influencing
included
severity,
moisture-related
climatic
(i.e.,
VPD,
precipitation,
soil
moisture),
seasonal
variations.
Moreover,
EBF
America
less
impact
drought,
benefitted
favorable
(e.g.,
precipitation
lower
VPD),
experienced
higher
variation
monthly
temperature
resulting
a
faster
observed
Africa.
Earth s Future,
Journal Year:
2024,
Volume and Issue:
12(10)
Published: Oct. 1, 2024
Abstract
The
carbon
sink
in
pantropical
biomes
play
a
crucial
role
modulating
the
inter‐annual
variations
of
global
terrestrial
balance
and
is
threatened
by
extreme
climate
events.
However,
it
has
not
been
carefully
examined
whether
an
increase
tropical
gross
primary
productivity
(GPP)
can
compensate
decrease
during
precipitation
anomalies.
Using
asymmetry
index
(AI)
multiple
GPP
products,
we
assessed
responses
to
anomalies
2001–2022.
Positive
AI
indicates
that
increases
are
greater
than
decreases
anomalies,
vice
versa.
Our
results
showed
average
negative
asymmetry,
is,
exceeded
In
addition,
positive
was
found
hyper‐arid
arid
regions,
which
opposite
observed
semi‐arid,
sub‐humid,
humid
regions.
This
suggest
changes
from
as
moisture
increases.
Notably,
significant
decreasing
trend
over
entire
region,
indicating
effect
on
vegetation
enhanced.
Considering
model
predicted
increasing
variability
extremes,
impact
cycle
may
continue
intensify.
Lastly,
divergence
estimates
among
products
highlight
need
further
improve
our
understanding
response
changes,
especially
for
