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).