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,
Pätzold 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.