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 (Jobbágy 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.