Science of Remote Sensing,
Год журнала:
2024,
Номер
10, С. 100146 - 100146
Опубликована: Июль 2, 2024
Combining
the
advantages
of
crop
growth
models
and
remote
sensing
observations,
data
assimilation
(DA)
has
emerged
as
a
vital
tool
for
monitoring
early-season
yield
forecasting.
As
an
increasing
number
related
studies
have
been
conducted,
systems
grown
increasingly
sophisticated.
However,
within
this
context,
research
on
algorithms,
core
component
system,
highly
need
investigating
potential.
In
review,
we
discuss
essential
differences
inherent
connections
various
algorithms
based
Bayes's
Theorem.
Building
upon
foundation,
review
application
progress
different
DA
models.
Additionally,
identify
challenges
limitations
faced
by
current
in
practical
applications
propose
potential
directions
future
study.
summary
entire
paper,
provide
recommendations
algorithm
choice
strategy
conjunction
with
specific
scenarios.
Remote Sensing,
Год журнала:
2023,
Номер
15(18), С. 4527 - 4527
Опубликована: Сен. 14, 2023
Due
to
the
scarcity
of
observational
data
and
intricate
precipitation–runoff
relationship,
individually
applying
physically
based
hydrological
models
machine
learning
(ML)
techniques
presents
challenges
in
accurately
predicting
floods
within
data-scarce
glacial
river
basins.
To
address
this
challenge,
study
introduces
an
innovative
hybrid
model
that
synergistically
harnesses
strengths
multi-source
remote
sensing
data,
a
(i.e.,
Spatial
Processes
Hydrology
(SPHY)),
ML
techniques.
This
novel
approach
employs
MODIS
snow
cover
sensing-derived
glacier
mass
balance
calibrate
SPHY
model.
The
primarily
generates
baseflow,
rain
runoff,
snowmelt
melt
runoff.
These
outputs
are
then
utilized
as
extra
inputs
for
models,
which
consist
Random
Forest
(RF),
Gradient
Boosting
(GDBT),
Long
Short-Term
Memory
(LSTM),
Deep
Neural
Network
(DNN),
Support
Vector
Machine
(SVM)
Transformer
(TF).
reconstruct
relationship
between
streamflow.
performance
these
six
is
comprehensively
explored
Manas
River
basin
Central
Asia.
findings
underscore
SPHY-RF
performs
better
simulating
daily
streamflow
flood
events
than
other
five
models.
Compared
model,
significantly
reduces
RMSE
(55.6%)
PBIAS
(62.5%)
streamflow,
well
(65.8%)
(73.51%)
floods.
By
utilizing
bootstrap
sampling,
95%
uncertainty
interval
established,
effectively
covering
87.65%
events.
Significantly,
substantially
improves
simulation
struggles
capture,
indicating
its
potential
enhance
accuracy
prediction
offers
framework
robust
forecasting
basins,
offering
opportunities
explore
extreme
warming
climate.
Science of Remote Sensing,
Год журнала:
2024,
Номер
10, С. 100146 - 100146
Опубликована: Июль 2, 2024
Combining
the
advantages
of
crop
growth
models
and
remote
sensing
observations,
data
assimilation
(DA)
has
emerged
as
a
vital
tool
for
monitoring
early-season
yield
forecasting.
As
an
increasing
number
related
studies
have
been
conducted,
systems
grown
increasingly
sophisticated.
However,
within
this
context,
research
on
algorithms,
core
component
system,
highly
need
investigating
potential.
In
review,
we
discuss
essential
differences
inherent
connections
various
algorithms
based
Bayes's
Theorem.
Building
upon
foundation,
review
application
progress
different
DA
models.
Additionally,
identify
challenges
limitations
faced
by
current
in
practical
applications
propose
potential
directions
future
study.
summary
entire
paper,
provide
recommendations
algorithm
choice
strategy
conjunction
with
specific
scenarios.
