International Journal of Remote Sensing,
Journal Year:
2022,
Volume and Issue:
43(9), P. 3429 - 3449
Published: May 3, 2022
Soil
organic
carbon
(SOC)
is
one
of
the
key
soil
components
for
cultivated
soils.
SOC
regularly
monitored
and
mapped
to
improve
quality,
health,
productivity
soil.
However,
traditional
SOC-level
monitoring
expensive
land
managers
farmers.
Estimating
using
satellite
imagery
provides
an
easy,
efficient,
cost-effective
way
monitor
surface
levels.
The
objective
this
study
was
estimate
distribution
in
selected
soils
Major
Land
Resource
Areas
(MLRA),
102A
(Rolling
Till
Plain,
Brookings
County,
SD),
103
(Central
Iowa
Minnesota
Prairies,
Lac
qui
Parle
MN),
with
different
resolutions
(Landsat
8
PlanetScope).
dominant
area
are
Haplustolls,
Calciustolls,
Endoaquolls,
which
formed
silty
sediments,
local
alluvium,
till.
Landsat
PlanetScope
spectral
bands
were
used
develop
prediction
models.
Parametric
data-driven
methods
employed
predict
SOC.
Multiple
linear
regression
Linear
Spatial
Mixed
Model
(LSMM)
on
data.
In
addition
parametric
models,
Regression
Trees
Random
Forest
also
both
results
showed
that
reduced
LSMM
provided
lowest
RMSE,
0.401
0.367
PlanetScope,
respectively.
Furthermore,
random
forest
has
highest
RPD
RPIQ
(RPD
2.67
2.49)
2.85
3.7).
all
cases,
models
obtained
from
better
than
those
8.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(12), P. 2917 - 2917
Published: June 18, 2022
There
is
a
need
to
update
soil
maps
and
monitor
organic
carbon
(SOC)
in
the
upper
horizons
or
plough
layer
for
enabling
decision
support
land
management,
while
complying
with
several
policies,
especially
those
favoring
storage.
This
review
paper
dedicated
satellite-based
spectral
approaches
SOC
assessment
that
have
been
achieved
from
satellite
sensors,
study
scales
geographical
contexts
past
decade.
Most
relying
on
pure
models
carried
out
since
2019
dealt
temperate
croplands
Europe,
China
North
America
at
scale
of
small
regions,
some
hundreds
km2:
dry
combustion
wet
oxidation
were
analytical
determination
methods
used
50%
35%
satellite-derived
studies,
which
measured
topsoil
contents
mainly
referred
mineral
soils,
typically
cambisols
luvisols
lesser
extent,
regosols,
leptosols,
stagnosols
chernozems,
annual
cropping
systems
value
~15
g·kg−1
range
30
median.
prediction
limited
preprocessing
based
bare
pixel
retrieval
after
Normalized
Difference
Vegetation
Index
(NDVI)
thresholding.
About
one
third
these
partial
least
squares
regression
(PLSR),
another
random
forest
(RF),
remaining
included
machine
learning
such
as
vector
(SVM).
We
did
not
find
any
studies
either
deep
all-performance
evaluations
uncertainty
analysis
spatial
model
predictions.
Nevertheless,
literature
examined
here
identifies
information,
derived
under
conditions,
an
interesting
approach
deserves
further
investigations.
Future
research
includes
considering
simultaneous
imagery
acquired
dates
i.e.,
temporal
mosaicking,
testing
influence
possible
disturbing
factors
mitigating
their
effects
fusing
mixed
incorporating
non-spectral
ancillary
information.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(7), P. 1223 - 1223
Published: March 30, 2024
Mineral
mapping
from
satellite
images
provides
valuable
insights
into
subsurface
mineral
alteration
for
geothermal
exploration.
In
previous
studies,
eight
fundamental
algorithms
were
used
utilizing
USGS
spectra,
a
collection
of
reflectance
spectra
containing
samples
minerals,
rocks,
and
soils
created
by
the
USGS.
We
an
ASD
FieldSpec
4
Hi-RES
NG
portable
spectrometer
to
collect
analyzing
ASTER
Coso
Geothermal
Field.
Then,
we
established
ground-truth
information
spectral
library
97
samples.
Samples
collected
field
analyzed
using
CSIRO
TSG
(The
Spectral
Geologist
Commonwealth
Scientific
Industrial
Research
Organization).
Based
on
mineralogy
study,
multiple
high-purity
minerals
selected
data,
including
alunite,
chalcedony,
hematite,
kaolinite,
opal.
Eight
target
detection
applied
preprocessed
data
with
proposed
local
library.
measured
highest
overall
accuracy
87%
95%
opal,
83%
60%
96%
kaolinite
out
these
algorithms.
Three,
four,
five,
fused
extract
obtained
results.
The
results
prove
that
fusion
gives
better
than
individual
ones.
conclusion,
this
paper
discusses
significance
evaluating
different
It
proposes
robust
approach
maps
as
indicator
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(13), P. 3316 - 3316
Published: June 28, 2023
Remote
sensing
(RS)
has
revolutionized
field
data
collection
processes
and
provided
timely
spatially
consistent
acquisition
of
on
the
terrestrial
landscape
properties.
This
research
paper
investigates
relationship
between
Wind
Erosion
(WE)
Sensing
techniques.
By
examining,
analyzing,
reviewing
recent
studies
utilizing
RS,
we
underscore
importance
wind
erosion
by
exploring
indicators
that
influence
detection,
evaluation,
modeling
erosion.
Furthermore,
it
identifies
gaps
particularly
in
soil
erodibility
estimation,
moisture
monitoring,
surface
roughness
assessment
using
RS.
Overall,
this
enhances
our
understanding
WE
RS
offers
insights
into
future
directions.
To
conduct
study,
employed
a
two-fold
approach.
First,
utilized
non-systematic
review
approach
accessing
Global
Applications
Soil
Modelling
Tracker
(GASEMT)
database.
Subsequently,
conducted
systematic
relevant
literature
remote
core
Web
Science
(WoS)
Additionally,
VOSviewer
bibliometric
software
to
generate
cooperative
keyword
network
analysis,
facilitating
advancements
identifying
emerging
areas
research.
With
review,
focused
examining
current
state
potential
for
mapping
analyzing
following
modelling:
(1)
erodibility;
(2)
moisture;
(3)
roughness;
(4)
vegetation
cover;
(5)
barriers;
(6)
mapping.
Our
study
highlights
widespread
utilization
freely
available
data,
such
as
MODIS
Landsat,
modeling.
However,
also
acknowledge
limitations
high
resolution
sensors
due
their
costs.
techniques
offer
an
efficient
cost-effective
at
various
scales
call
more
comprehensive
detailed
regional
scales.
These
findings
provide
valuable
guidance
endeavors
domain.
