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\begin{document}

\title{A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches}



\author[1]{MARYAM BATOOL}%
\affil[1]{University of Sahiwal}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\selectlanguage{english}
\begin{abstract}
Current review focuses on developments during past time on RS practices
in VNIR-SWIR regions designed for estimation of soil organic carbon
(SOC) content. Soil reflectance spectroscopy finds extensive
applications such as sensors set-up on satellites, aircrafts as well as
Unmanned Aerial System (UAS). This review briefly discusses research as
well as studies on soil organic carbon analysis by employing RS
practices. It is detected that prediction correctness lessens from
Unmanned Aerial Systems (UASs) towards satellite platforms, although,
machine learning help in superior production of calibration models. For
coping with numerous challenges related to SOC observation of large
region, hyperspectral sensors set-up on forthcoming satellite missions,
airplanes as well as Unmanned Aerial System (UAS) provide special
potential. Additionally, merits as well as demerits of individual
approach are also briefly discussed here.%
\end{abstract}%



\sloppy


\textbf{A Review on Soil Organic Carbon Estimation via Remote Sensing
Approaches}

\textbf{HIGHLIGHTS:}

\begin{itemize}
\tightlist
\item
  Soil reflectance spectroscopy used for estimation of soil organic
  carbon content.
\item
  Visible near infrared-shortwave infrared sensors based on
  energy-matter interaction.
\item
  Demand of multivariate statistical procedures to study soil
  characteristics.
\end{itemize}

ABSTRACT

Current review focuses on developments during past time on RS practices
in VNIR-SWIR regions designed for estimation of soil organic carbon
(SOC) content. Soil reflectance spectroscopy finds extensive
applications such as sensors set-up on satellites, aircrafts as well as
Unmanned Aerial System (UAS). This review briefly discusses research as
well as studies on soil organic carbon analysis by employing RS
practices. It is detected that prediction correctness lessens from
Unmanned Aerial Systems (UASs) towards satellite platforms, although,
machine learning help in superior production of calibration models. For
coping with numerous challenges related to SOC observation of large
region, hyperspectral sensors set-up on forthcoming satellite missions,
airplanes as well as Unmanned Aerial System (UAS) provide special
potential. Additionally, merits as well as demerits of individual
approach are also briefly discussed here.

\textbf{Keywords:} Remote Sensing (RS), Soil Organic Carbon (SOC),
Visible near infrared-shortwave infrared (VNIR-SWIR), Spaceborne,
Airborne, Unmanned Aerial System (UAS).

\textbf{1. Introduction}

Soil is a mixture of organic as well as inorganic parts and their amount
change from place to place or within the same place (Jandl, Rodeghiero
et al. 2014). Owing to this, estimation of soil components (both
quantitative as well as qualitative) is a burdensome process (Gehl and
Rice 2007). Some logical as well as coherent datum intended to obtain
information regarding soil organic carbon content (SOC) estimation must
be required for the purpose to optimize monitoring as well as mapping
capacity (Angelopoulou, Tziolas et al. 2019). SOC holds an integral part
on carbon cycle and approximately 1500 Gt of carbon is stored in soils
at 1 m depth (Jobb\selectlanguage{ngerman}ágy and Jackson 2000), (Scharlemann, Tanner et al.
2014). Additionally, SOC is a part of Organic Matter (OM) and has
influence on physical, chemical, biological characteristics of soil
ecosystem (Ontl and Schulte 2012). Eswaran et al., (Eswaran, Van Den
Berg et al. 1993) stated some hurdles to estimate correct global carbon
content. These are because of high spatial changeability of soil organic
carbon, variableness of soil kinds which contains unpredictable
estimates, inaccessibility of valid data as well as changes in plants
and land usage.

Various traditional processes were used for SOC monitoring but they are
laborious as well as expensive (Omran 2017). Studies were conducted to
explore and apply other revolutionary procedures for all types of
environments and all kinds of soil (Jandl, Rodeghiero et al. 2014) i.e.,
use of Remote Sensing (RS) practice is efficient, low cost in addition
to environmentally sound aspect for analysis of various soil
characteristics (Xu, Smith et al. 2017) such as SOC estimation etc.
(Vaudour, Gilliot et al. 2016). Visible near infrared-shortwave infrared
(VNIR-SWIR) sensors (find RS usages) work on principle of energy-matter
interaction (Schwartz, Ben-Dor et al. 2012). Part of electromagnetic
radiations (falling on soil exterior) that is reflected from soil
surface is recorded as a spectrum which is enough to produce information
(both qualitative as well as quantitative) regarding soil
characteristics (Nocita, Stevens et al. 2015). In VNIR-SWIR,
characteristic vibrations take place (Mohamed, Saleh et al. 2018) in
visible region (400-700nm) i.e., electronic transitions occur which
produce absorption bands linked to chromophore while on the other hand,
in NIR-SWIR (700-2500 nm) weak overtones or such vibrations take place
owing to extending as well as bending of some bonds such as N-H, O-H, as
well as C-H bonds etc. (Rossel, Walvoort et al. 2006), (Stuart 2004).
Association amongst SOC as well as electromagnetic radiations in VNIR,
SWIR region has already been reported in laboratory conditions
(Bartholomeus, Schaepman et al. 2008), (Stevens, Nocita et al. 2013),
(Nocita, Stevens et al. 2013). In addition to laboratory trials,
numerous studies have also been conducted in real field conditions
founded on manned as well as unmanned airborne system, and satellite
platforms (Gomez, Rossel et al. 2008). However, these practices have
restrictions for direct SOC estimation including vegetation cover, soil
wetness etc. (Nocita, Stevens et al. 2013), (Bartholomeus 2009).
Furthermore, there is a demand of some multivariate statistical
procedures called Chemometrics to associate spectral signatures with
soil characteristics (Geladi 2003).

One of the most widely employed practice is application of partial least
squares regression (PLSR) for reporting direct association among
variables (Peng, Shi et al. 2014). That's the basic reason of increased
usage of machine learning algorithms for correlation procedures
(Stenberg, Rossel et al. 2010), (Liakos, Busato et al. 2018).

This review article emphasizes on current research of remote sensing
procedures, state-of- art procedures as well as instruments for current
SOC estimation.

\textbf{2. Remote sensing data sources}

For renewal as well as monitoring of SOC crossways VNIR-SWIR spectral
span, remote sensing (RS) via various sources gave statistics streams.
Various kinds of sensors (in case of imaging spectroscopy) are set up on
airborne (Castaldi, Chabrillat et al. 2018) or else spaceborne
platforms. As a result, Unmanned Aerial Systems (UASs) are widely used
to perform succeeding generation of hand-sized hyperspectral imagers
(Aldana-Jague, Heckrath et al. 2016). Brief explanation of remote
sensors requirements for SOC estimations are given below:

\emph{2.1. Spaceborne}

Spaceborne remotely sensed imagery possess the power to generate spatial
maps of higher soil horizon due to association of soil's definite
chemical bonds as well as electromagnetic radiations. With inauguration
of first satellite in 1980s, optical satellite multispectral imagery
finds extensive applications in SOC estimation (quantitative) (Frazier
and Cheng 1989). Additionally, owing to availability of Hyperion
spaceborne system, usage of hyperspectral data also increased (Castaldi,
Casa et al. 2014). However, their application for soil study was
restricted owing to essential atmospheric, geometric, radiometric data
amendment, obstacles in uncovering naked regions in one image (Demattê,
Fongaro et al. 2018), as well as hurdles associated with vegetation
cover (Barnes, Sudduth et al. 2003). Owing to these reasons studies on
SOC estimation via satellite sensors are not many (Croft, Kuhn et al.
2012). Nowadays there is a remarkable change in SOC estimation as well
as mapping founded on spaceborne data. One of the major achievements in
this regard is relevant USGS policy variation (for dispersion of Landsat
data freely) (Woodcock, Allen et al. 2008). Additionally, upcoming
hyperspectral sensors i.e., Environmental Mapping and Analysis Program
(EnMAP) shortly give unprecedental statistics for SOC estimation across
VNIR-SWIR spectral span (Stuffler, Kaufmann et al. 2007).

\emph{2.2. Airborne}

Spatial evaluation of soil environment gives correct mapping of
changeability in agricultural field via Airborne hyperspectral imaging
(Stevens, van Wesemael et al. 2008). It is also useful for data
collection to divide a place in agreement with soil heterogeneity
(Mulder, De Bruin et al. 2011). Airborne set-up sensors exhibit
flexibleness for a specific measurement time window giving power to
choose optimal flight condition and work under an extraordinary-cloud
coverage (Usha and Singh 2013).

\emph{2.3. Unmanned Aerial System (UAS)}

From past few years, UAS is an efficient as well as low price platform
for environmental monitoring (Whitehead and Hugenholtz 2014). Owing to
usage of innovative sensors, low cost, development in sensor's size as
well as spectral resolution UASs have wide range of uses. Both
spaceborne characteristics (i.e., brief revisit period) as well as
airborne platforms (including an extraordinary resolution) are combined
in UAS, and it is the distinctive characteristic of UAS to give
resolution required on the way to protect diversity of
agri-environmental landscapes (Pádua, Vanko et al. 2017). Inspite of
these merits, research related to soil characteristics analysis is
restricted owing to restricted payload, restricted flight duration as
well as issues of image processing (Zhang and Kovacs 2012).

\textbf{3. Review}

\emph{3.1. Uses of remote sensing statistics in SOC estimation}

For mapping soil characteristics, statistics via satellite sensors find
its uses as auxiliary variables. On behalf of this purpose, forecasting
of SOC spatial changeability as well as progress of high-quality maps
via combination of geostatistical methods with variety of remote sensed
variables is more correct as compared to simple Kriging (Mirzaee,
Ghorbani-Dashtaki et al. 2016), (Wang, Waters et al. 2018). Schillaci et
al., (Schillaci, Lombardo et al. 2017) customized set of topographical
as well as environmental covariates with a Stochastic Gradient Treeboost
for assessment of SOC stocks-Landsat 7ETM+ was used to get RS data and
results showed that panchromatic Band 8 resulted in superior forecasting
as compared to NDVI. Modal et al., (Mondal, Khare et al. 2017) founded
from RS data that variables such as radiance, moisture, as well as plant
vegetation condition indication affect SOC dispersal to a great extent.
Castaldi et al., (Castaldi, Palombo et al. 2016) studied power of 3
upcoming satellite hyperspectral imagers (EnMAP, PRISMA (Labate,
Ceccherini et al. 2009) as well as HyspIRI (Roberts, Quattrochi et al.
2012)) in comparison with ALI besides Hyperion (EO-1) for SOC
estimation. For stimulation of spectral statistics via upcoming
satellite imagers, spectra in laboratory set-up were resampled in
accordance with sensor's spectral as well as radiometric requirements.
Owing to this a regional soil spectral library having 160 samples and
datum from LUCAS soil database were used. On behalf of result analysis,
PLSR was employed intended for model standardization Ratio of
Performance to Interquartile Range (RPIQ) (Bellon-Maurel,
Fernandez-Ahumada et al. 2010). For local database results from
resampled data are superior varied as of \(R^{2}\) = 0.36 for
Sentinel-2 and \(R^{2}\) = 0.51 for PRISMA. Outcomes obtained
from LUCAS database have lesser \(R^{2}\) i.e., varied from
0.06 to 0.26 for Hyperion as well as PRISMA correspondingly
(Angelopoulou, Tziolas et al. 2019).

Steinberg et al., (Steinberg, Chabrillat et al. 2016) worked on
estimation and study of prediction correctness via simulated statistics
of forthcoming satellite sensor EnMAP in comparison with airborne
AHS-160. Results confirmed similarity among soil spectral reflectivity
obtained via sensors beside satellite sensor. To make progress in
simulated EnMAP statistics, resolution of sampling strategy is crucial.
For forecasting soil as well as soil organic matter characteristics,
Gallo et al., (Gallo, Demattê et al. 2018) employed PLSR algorithm on
datum obtained from naked soil composite image. Gholizadeh et al.,
(Gholizadeh, \selectlanguage{polish}Ž\selectlanguage{english}i\selectlanguage{polish}ž\selectlanguage{english}ala et al. 2018) stated that for obtaining an
extraordinary-quality information on fluctuations in SOC, Sentinel-2 is
more reliable as compared to airborne sensors. For that reason, they
used modest SVM model to direct prediction models over spectral
signature of Sentinel-2 as well as a set of spectral marks. B4 as well
as B5 after B11 and B12 gave superior SOC as well as Sentinel-2 spectral
band association. Additionally, some other spectral marks i.e., BI, BI2,
GNDVI as well as SATVI also resulted in strong association with SOC.
Castaldi et al., (Castaldi, Hueni et al. 2019) observed that to express
SOC changeability at both inside area as well as at geographical level
spectral resolution as well as spectral properties are sufficient. For
variety of pilot sites, they established Partial Least Square Regression
(PLSR) as well as Random Forest (RF) models by making use of Sentinel-2
RPD values obtained by this way varied from 1.0-2.6. Vaudour et al.,
(Vaudour, Gomez et al. 2019) also reported identical results. Table 1
summarizes SOC analysis by employing spaceborne platforms.

\emph{3.2. Airborne}

Power of airborne hyperspectral sensor 160 (AHS, Caravan International
Corporation, USA) for SOC content estimation for naked place of soil
kinds was assessed by Stevens et al., (Stevens, Udelhoven et al. 2010).
Spectral span changes from 430 nm to 2540 nm. Received spectra were
linked through 325 soil samples having SOC content varied from 7-61 gC/
kg. Owing to heterogeneousness in mineralogy as well as soil humid
content, there was decline in reflectance from sandy to
colluvial-alluvial soils. When findings of PLSR, Penalized Spline
Regression (PSR) as well as SVM for worldwide calibration were matched,
it became clear that SVM was the most efficient as well as suitable
approach (\(R^{2}=0.74)\) owing to large datum.

There is a demand of atmospheric amendments as well as suitable weather
circumstances in case of airborne data since large pixel size as well as
changing standard of sensor's strength in addition to sensitivity may
cause numerous problems (Brook and Dor 2011). Hbirkou et al., (Hbirkou,
P\selectlanguage{ngerman}ätzold et al. 2012) worked on assessing power of airborne hyperspectral
sensor HyMap (Integrated Spectronics, Sydney, Australia) in addition to
studied consequences of soil raggedness as well as vegetation cover
towards SOC projection models at field level. Complete experiment is
carried out in dry weather. PLSR models from thorough datum (n = 204)
produced results with \(R^{2}=0.83\) which in specific places varied
from 0.34-0.73. Soil raggedness greatly affect model's correctness as
most negative conditions giving \(R^{2}=0.34\). Comparable findings
were reported by Lagacherie et al., (Lagacherie, Baret et al. 2008).

Applications of RS techniques have numerous restrictions (Ben-Dor,
Chabrillat et al. 2009) however, research on naked soil is recommend for
data achieved via airborne mounted sensors (Denis, Stevens et al. 2014).
Franceschini et al., (Franceschini, Demattê et al. 2015) worked on
spectral combination of naked soil having photosynthetic as well as
non-photosynthetic vegetation. Statistics was achieved via ProSpecTIR
V-S sensor (SpecTIR LLC, Reno, N). Guerschman et al., (Guerschman, Hill
et al. 2009) suggested linear unmixing methodology for naked soil
fractional cover analysis. There were 89 collected samples which were
split into four categories in accordance with naked soil fractional
cover quartile. PLSR models were employed for individual category. From
results it became obvious that soil spectral albedo reduces by upsurging
in organic matter (OM) as well as clay content. However, forecasting of
OM content in laboratory environment having \(R^{2}=0.70\) was more
correct in contrast to airborne hyperspectral sensors
having\(R^{2}=0.33\). Residual Spectral Unmixing (RSU), a spectral
unmixing practice was established by Bartholomeus et al., (Bartholomeus,
Kooistra et al. 2011) for elimination of vegetation effect of mixed
pixels as well as enhancing SOC changeability analysis in enclosed maize
fields.

Diek et al., (Diek, Schaepman et al. 2016) produced multi-temporal
composites via Airborne Prism Experiment (APEX) in addition to making
use of crop rotation to escalate naked soil regions. For hiding of green
vegetation as well as for non-agricultural places, various spectral
evidence in addition to renovated agricultural field block map was
employed consecutively. Nevertheless, for XOM analysis,
\(R^{2}\) value was 0.39 ± 0.04 which showed that besides
vegetation cover, some other elements need to be considered such as soil
wetness as well as raggedness. Bayer et al., (Bayer, Bachmann et al.
2016) suggested a feature-founded forecasting model for SOC estimation
founded on naked soil field spectra in HyMap's spectra resolution.
Iterative Spectral Mixture Approach was employed for resolving problem
of mixed pixels giving 45.4 \% upsurge in model range. Little
predictions were due to various kinds of vegetation, low spatial
resolution as well as decreased correctness of geo-correction
applications. Castaldi et al., (Castaldi, Chabrillat et al. 2018)
suggested bottom practice for SOC estimation purposes by making use of
existing advanced great soil spectral collections. Owing to this LUCAS
topsoil datum (Toth, Jones et al. 2013) was merged along with APEX
sensor statistics. Correctness of model was examined via entirely
independent verified datum producing comparable RMSE of 4.3 gC/ kg to
conventional procedures (RMSE = 3.6 gC/ kg).

Vohland et al., (Vohland, Ludwig et al. 2017) examined various spectral
variable selection procedures such as Competitive Adaptive Reweighted
Sampling (CARS) (an approach that ``iteratively retains informative
variables'') as well as genetic algorithm (GA) to enhance predictions.
Results showed that PLSR models based on fuel spectrum produced inferior
findings when compared with spectral variable selection i.e., GA
provided \(R^{2}=0.85\) for airborne dimensions in case of SOC
estimation. Peón et al., (Peón, Recondo et al. 2017) correlated
predictions obtained via Hyperion as well as AHS consecutively. They
concluded that both sensors have comparable spectral associations in red
region chiefly at 610 as well as 679-681 nm. Main findings regarding SOC
estimation employing usage of airborne platforms are summarized in table
2.

\emph{3.3. Unmanned Aerial Systems (UASs)}

For variety of environmental as well as climate variable estimation
founded on UAS applications development was made (McGwire, Weltz et al.
2012), (Li, Niu et al. 2016), (Capolupo, Kooistra et al. 2015) but these
platforms are not employed for soil environment observing. As far as
there is just 1 finding for SOC estimation (Aldana-Jague, Heckrath et
al. 2016) by employing multispectral Mini-MCA6 from Tetracam Inc.
(450-1050 nm) (Chatsworth, CA, USA) on-board a UAS platform on the way
to assess its effectiveness for SOC predictions. Optimum conditions for
flight compaign were cloudless sky, little vegetation shelter, as well
as dehydrated soil. Suggested procedure has excellent power for SOC
monitoring employing an algorithm producing mean SVM coefficient of
determination of 0.95 as well as a RMSE of 0.21 \% in cross
authentication in comparison with dry combustion laboratory procedures.
Table 3 showed SOC estimation study via UAS.

\textbf{4. Discussion}

\emph{4.1. Summary of remote sensing technique}

There are various kinds of RS practices founded on their
three-dimensional, spectral, chronological as well as radiometric
resolution and platforms on where they are set-up (shown in Fig. 1).
Based on type of use, property to be quantified, as well as correctness
of results, suitable approach is selected.

Rapid as well as extensive uses of these applications are due to
developments in sensors requirements. Sensors set-up on satellite
platforms have upgraded from panchromatic towards multispectral as well
as upcoming hyperspectral i.e., EnMAP, HyspIRI as well as PRISMA. Owning
to availability of such hyperspectral sensors, essential information
regarding soil's condition, SOC estimation can be obtained via RS
applications. Additionally, to meet present as well as upcoming demands
for soil monitoring essential datum for correct up-to-date soil maps can
also be obtained via RS practice. RS practices have merits that these
are environmentally sound practices to obtain data related to soil
properties, provide data of those sites/ places that are inaccessible,
provide concise information and lower the chances of laborious soil
sampling work (Angelopoulou, Tziolas et al. 2019).

RS practices have issues that they possess little signal to noise
proportion (Minu, Shetty et al. 2016), little spectral resolution
(Gomez, Rossel et al. 2008), as well as undergo geometric and
atmospheric manipulations (Jakob, Zimmermann et al. 2017). There exists
another problem i.e., scale effects, for example, variations take place
when retrieval models as well as algorithms are derived at small and
large levels (Wu and Li 2009). Additionally, external parameters like
soil wetness, structure, raggedness, vegetation greatly affect correct
quantitative estimation via RS practices (Wu and Li 2009). Table 4
summarized some merits as well as demerits of RS platforms for SOC
monitoring (Angelopoulou, Tziolas et al. 2019).

Multivariate statistical practices find applications for model
calibration employing PLSR, and pre-processing practices changes in each
study. However, for generating prediction models for soil
characteristics, there is significant interest in connection with
machine learning approaches having power to outperform PLSR as shown in
Fig. 2 (Angelopoulou, Tziolas et al. 2019).

\emph{4.2. Future of soil spectroscopy in SOC estimation}

To reduce global as well as local level threats, capability of all
stakeholders in agricultural sections must be enhanced resulting in
elimination of negative environmental pressures (Lipper, Thornton et al.
2014). Research is continued for establishing SSLs. Production of SSLs
take place at low price and with little effort in comparison to
analytical wet chemical procedures (accountable for environmental issues
owning to chemicals used in them) (Demattê, Dotto et al. 2019), however,
usage of SSLs was restricted on behalf of confined estimations. Nowadays
confined regression procedures involving application of spectral sources
as well as geographical proximity were established for improving SOC
estimation (Lobsey, Viscarra Rossel et al. 2017), (Tziolas, Tsakiridis
et al. 2019). Additionally, lack in comparability among various studies
is another challenge. This is because of reason that similar procedures
are not employed for model assessment as well as correctness and
particular data required for making comparison between them is also
missing in studies (Romero, Ben-Dor et al. 2018).

Importance of RS platforms cannot be exaggerated for establishing new
observational modalities and enhancing computing, observation, and
description practices at different levels in context of manageable
progress objectives set by United Nations (Anderson, Ryan et al. 2017).
Hence, purpose of RS data is as a proxy designed for SOC estimation
resulting in large level maps within frame of soil related indicators
such as SDG indicator 15.3 (Tóth, Hermann et al. 2018), (Keesstra, Bouma
et al. 2016). Possibly, SSLs proved to be excellent foundation for
upcoming hyperspectral remote sensing of soils from space (Guanter,
Kaufmann et al. 2015) because they could find uses in support of
Copernicus program as well as for collaborative usage with mobile
proximal soil (Kühnel and Bogner 2017) besides airborne sensors
(Castaldi, Chabrillat et al. 2018).

\textbf{5. Conclusions}

Current review focuses on development during past time on RS practices
in VNIR-SWIR regions designed for estimation of soil organic carbon
(SOC) content. From this review paper, we summarized that for coping
with numerous challenges related to SOC observation of large region,
hyperspectral sensors set-up on forthcoming satellite missions,
airplanes as well as Unmanned Aerial System (UAS) provide special
potential. Numerous studies have been conducted regarding development in
machine learning as well as exploring effectiveness of soil spectroscopy
application for studying soil characteristics. However, some parameters
like roughness, soil wetness, vegetation cover etc. affect satellite
imagery to a great extent. For this reason, a combination of both remote
as well as proximal sensing technologies should be taken into
consideration for establishing low price as well as effective monitoring
solutions having high spatial resolution.

\textbf{Acknowledgements}

First, all praise to Almighty Allah for blessing us with strength and
enabling us to successfully accomplish this work. We would like to
manifest special thanks to nearest and dearest parents, family, as well
as teachers for their endless love, guidance, support, besides
encouragement.

\textbf{Author's contribution}

All authors contributed equally to manuscript.

\textbf{Conflicts of interests}

Authors state no competing financial concerns to influence the work
described in this research.

\textbf{Funding}

Not applicable as this review article did not receive any specific grant
from funding agencies in the public, commercial, or not-for-profit
sectors.

\textbf{Data transparency}

Authors will ensure transparency of data.

\textbf{Ethical approval}

Not applicable.

\textbf{Consent to participate}

Not applicable.

\textbf{Consent for publication}

Not applicable.

\textbf{Data availability}

The data that support this study are available in the article and
accompanying online supplementary material.

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\textbf{Table 1}

Summary of SOC analysis by employing spaceborne platforms.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Reference} & \textbf{Sensor} & \textbf{Spectral series (nm)} &
\textbf{Algorithm/ multivariate approach} & \(\mathbf{R}^{\mathbf{2}}\) &
\textbf{RMSE (g/ kg)} & \textbf{RPD}\tabularnewline
\midrule
\endhead
(Gomez, Rossel et al. 2008) & Hyperion & 400-2500 & PLSR & 0.51 & 0.73 &
1.43\tabularnewline
(Mirzaee, Ghorbani-Dashtaki et al. 2016) & Landsat ETM+ & 450-2350 &
ANNSK & 0.63 & 0.27 & -\tabularnewline
(Castaldi, Palombo et al. 2016) & EnMAP & 420-2500 & PLSR & 0.25-0.67 &
0.20-0.48 & 1.17-1.80\tabularnewline
(Castaldi, Palombo et al. 2016) & PRISMA & 400-2500 & PLSR & 0.26-0.65 &
0.21-0.48 & 1.17-1.65\tabularnewline
(Castaldi, Palombo et al. 2016) & HyspIRI & 380-2510 & PLSR & 0.23-0.60
& 0.22-0.48 & 1.15-1.45\tabularnewline
(Steinberg, Chabrillat et al. 2016) & EnMAP & 420-2500 & AutoPLSR & 0.67
& 2.8 & 1.7\tabularnewline
(Castaldi, Hueni et al. 2019) & Sentinel-2 & 440-2200 & PLSR/ RF & - &
1.9-25.2/ 2.0-18.6 & 1.1-2.6/ 1.0-2.2\tabularnewline
(Vaudour, Gomez et al. 2019) & Sentinel-2 & 440-2200 & PLSR & 0.56 &
1.23 & 1.51\tabularnewline
(Gholizadeh, \selectlanguage{polish}Ž\selectlanguage{english}i\selectlanguage{polish}ž\selectlanguage{english}ala et al. 2018) & Sentinel-2 & 440-2200 & SVM & - &
0.0.8-0.24 & 1.60-1.92\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2}

Summary of main findings regarding SOC estimation employing usage of
airborne platforms.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Sensor}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Spectral series (nm)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Algorithm/ multivariate approach}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\(\mathbf{R}^{\mathbf{2}}\)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{RMSE (g/ kg)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{RPD}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Stevens, Udelhoven et al. 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AHS-160\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
430--2540\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR, PSR, SVMR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.53--0.89\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.13--6.22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.47-3.15\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Stevens, van Wesemael et al. 2008)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
AHS-160\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
430--2540\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.47\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Hbirkou, P\selectlanguage{ngerman}ätzold et al. 2012)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
HyMap\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
450--2500\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.34--0.83\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.76--1.10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.14-2.32\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Franceschini, Dematt\selectlanguage{ngerman}ê et al. 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ProSpec TIR V-S\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
400--2500\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.33\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.82\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.25\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Bartholomeus, Kooistra et al. 2011)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AHS-160\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
430--2540\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.62\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.34\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Vaudour, Gilliot et al. 2016)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AISA-Eagle\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
400--1000\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0. 44\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Pe\selectlanguage{ngerman}ón, Recondo et al. 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AHS-160\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
430--2540\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.27--0.60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.44--8.70\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.18-1.60\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Homolov\selectlanguage{ngerman}á, Schaepman et al. 2014)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
AISA Dual system\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
400--2450\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SLR, SMLR, PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.73\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Castaldi, Chabrillat et al. 2018)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
APEX\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
400--2500\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4.3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Vohland, Ludwig et al. 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
HyMap\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
450--2500\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PLSR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.73--0.85\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.19--0.25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.94-2.62\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3}

SOC estimation study via UAS.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Reference} & \textbf{Sensor} & \textbf{Spectral series (nm)} &
\textbf{Algorithm/ multivariate approach} & \(\mathbf{R}^{\mathbf{2}}\) &
\textbf{RMSE (g/ kg)} & \textbf{RPD}\tabularnewline
\midrule
\endhead
(Aldana-Jague, Heckrath et al. 2016) & Mini-MCA6 & 450--1050 & SVM &
0.95 & 0.21 & -\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4}

Summary of merits as well as demerits of RS platforms for SOC monitoring
(Angelopoulou, Tziolas et al. 2019).\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Platform} & \textbf{Merits} & \textbf{Demerits}\tabularnewline
\midrule
\endhead
Satellites & Short revisit time, produced auxiliary records, gave free
of cost information besides data of unreachable places & Low signal to
noise proportion, demand for geometric as well as atmospheric
amendments, atmospheric absorption affecting with spectral
quantities\tabularnewline
Airborne & Extraordinary spatial resolution, gave data of unreachable
places, tools available in the range of (1000--2500 nm) & Extraordinary
working complication, expensive, demand for meteorological environment
for remote sensing usage, official limitations for flight\tabularnewline
Unmanned Aerial Systems (UASs) & Extraordinary spatial resolution,
designing of flight strategy by taking weather situations into
consideration & Restricted payloads, restricted flight period, demand
for geometric as well as atmospheric amendments\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1.} An overview of various RS practices founded on their
three-dimensional, spectral, chronological as well as radiometric
resolution for SOC estimation. Black bordered signs represent upcoming
remote sensing systems whereas x symbol represent that Hyperion is not
in working position. Reproduced with permission from (Angelopoulou,
Tziolas et al. 2019).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2.} Number of articles involving use of multivariate
calibration technique in each EO domain (PLSR stands for Partial Least
Squares Regression, SMLR stands for Stepwise Multiple Linear Regression,
SVM stands for Support Vector Machines, SLR stands for Simple Linear
Regression, ANN stands for Artificial Neural Networks, PSR stands for
Penalized-spline Signal Regression and MLR stands for Multiple Linear
Regression). Reproduced with permission from (Angelopoulou, Tziolas et
al. 2019)

\selectlanguage{english}
\FloatBarrier
\end{document}

