Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Oct. 12, 2023
Abstract
Air
temperature
holds
significant
importance
in
microclimate
and
environmental
health
studies,
playing
a
crucial
role
weather
regulation.
There
is
need
to
develop
reliable
model
capable
of
accurately
capturing
air
variations.
In
this
study,
we
focused
on
the
Amazon-Cerrado
transitional
forest,
constructing
robust
predictive
for
hourly
fluctuations.
This
situated
approximately
50
km
northwest
Sinop,
Mato
Grosso,
Brazil,
area,
making
it
important
investigate
its
climatic
behavior
ecosystems.
We
estimated
using
machine
learning
techniques
such
as
Random
Forest,
Gradient
Boosting,
Multilayer
Perceptron,
Support
Vector
Regressor,
aiming
evaluate
most
effective
models
based
relevant
metrics.
Performance
assessments
were
conducted
during
both
dry
rainy
seasons
verify
their
adaptability.
The
top-performing
Forest
demonstrated
Willmott
Spearman
indexes
above
0.97.
relative
humidity,
solar
radiation,
volumetric
soil
water
content
identified
features,
evaluated
with
0.95
dimensionality
reduction.
These
results
underscore
efficacy
estimating
temperature.
Vegetation
phenology
plays
a
pivotal
role
in
ecological
processes
on
terrestrial
surfaces
and
the
interactions
between
biosphere
atmospheric
feedback.
Current
attempts
to
retrieve
vegetation
have
primarily
depended
indices
extracted
from
satellite
remote
sensing
imagery.
These
approaches
often
neglect
underlying
physical
mechanisms
associated
with
climatic
factors,
there
is
notable
absence
of
evaluations
comparisons
field-observed
inventory
data.
To
address
these
limitations,
this
paper
proposes
an
innovative
constraint
neural
networks
(PCNNs)
model
that
combines
machine
learning
techniques
enhance
accuracy
predictions.
By
incorporating
meteorological
variables
into
by
using
Moderate-Resolution
Imaging
Spectroradiometer
(MODIS)
dataset
identify
four
types
North
America,
study
delved
relationship
climate
factors
as
well
its
impacts
ecosystems.
Our
demonstrated
high
compared
methods
without
when
validated
field
observations
PhenoCam
USA
National
Phenology
Network
(USA-NPN)
spanning
2001
2021.
The
results
show
overall
root
mean
square
error
(RMSE)
constraints
reduced
12.37
days,
higher
2.6
days
than
method
constraints.
We
different
traditional
rule-based
methods,
deciduous
(DV)
exhibited
most
favorable
prediction
results,
RMSE
bias
(MBE)
low
5.71
4.06
PCNNs
model,
respectively.
This
was
followed
evergreen
needle-leaved
forests
mixed
12.32
13.28
stressed
type
had
worst
result
19.86
(RMSE),
weighted
index
agreement
(WIA)
attained
value
0.68.
findings
suggest
embedded
significantly
boosted
for
common
types,
particularly
DV,
unconstrained
ML
model.
It
offers
valuable
insights
incorporation
within
models.
research
paves
way
substantial
advancements
land
surface
phenology,
enabling
more
accurate
reliable
predictions
various
contexts.
Frontiers in Ecology and Evolution,
Journal Year:
2022,
Volume and Issue:
10
Published: Oct. 6, 2022
Ecological
processes
are
complex,
often
exhibiting
non-linear,
interactive,
or
hierarchical
relationships.
Furthermore,
models
identifying
drivers
of
phenology
constrained
by
uncertainty
regarding
predictors,
interactions
across
scales,
and
legacy
impacts
prior
climate
conditions.
Nonetheless,
measuring
modeling
ecosystem
such
as
remains
critical
for
management
ecological
systems
the
social
they
support.
We
used
random
forest
to
assess
which
combination
climate,
location,
edaphic,
vegetation
composition,
disturbance
variables
best
predict
several
phenological
responses
in
three
dominant
land
cover
types
U.S.
Northwestern
Great
Plains
(NWP).
derived
measures
from
25-year
series
AVHRR
satellite
data
characterized
climatic
predictors
(i.e.,
multiple
moisture
and/or
temperature
based
variables)
over
seasonal
annual
timeframes
within
current
year
up
4
years
prior.
found
that
antecedent
conditions,
seasons
before
current,
were
strongly
associated
with
measures,
apparently
mediating
communities
current-year
For
example,
at
least
one
measure
antecedent-moisture
availability
[precipitation
vapor
pressure
deficit
(VPD)]
was
a
key
predictor
all
productivity
measures.
Variables
including
longer-term
lags
sums,
multi-year-cumulative
conditions
maximum
VPD,
top
start
season.
Productivity
also
contextual
soil
characteristics
composition.
Phenology
is
process
profoundly
affects
organism-environment
relationships,
spatio-temporal
patterns
structure
function,
other
dynamics.
Phenology,
however,
mediated
lagged
effects,
interactions,
diversity
potential
drivers;
nonetheless,
incorporation
can
improve
phenology.
Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Oct. 12, 2023
Abstract
Air
temperature
holds
significant
importance
in
microclimate
and
environmental
health
studies,
playing
a
crucial
role
weather
regulation.
There
is
need
to
develop
reliable
model
capable
of
accurately
capturing
air
variations.
In
this
study,
we
focused
on
the
Amazon-Cerrado
transitional
forest,
constructing
robust
predictive
for
hourly
fluctuations.
This
situated
approximately
50
km
northwest
Sinop,
Mato
Grosso,
Brazil,
area,
making
it
important
investigate
its
climatic
behavior
ecosystems.
We
estimated
using
machine
learning
techniques
such
as
Random
Forest,
Gradient
Boosting,
Multilayer
Perceptron,
Support
Vector
Regressor,
aiming
evaluate
most
effective
models
based
relevant
metrics.
Performance
assessments
were
conducted
during
both
dry
rainy
seasons
verify
their
adaptability.
The
top-performing
Forest
demonstrated
Willmott
Spearman
indexes
above
0.97.
relative
humidity,
solar
radiation,
volumetric
soil
water
content
identified
features,
evaluated
with
0.95
dimensionality
reduction.
These
results
underscore
efficacy
estimating
temperature.
