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\begin{document}

\title{Optimization of spectral pre-processing techniques for estimation of
surface soil properties from airborne AVIRIS-NG}



\author[1]{SAGAR TANEJA}%
\author[2]{Raj Setia}%
\author[3]{Baban K Bansod}%
\author[4]{Rahul Nigam}%
\author[2]{Sharad K Gupta}%
\author[4]{Bimal K Bhattacharya}%
\author[2]{Brijendra Pateriya}%
\affil[1]{Academy of Scientific and Innovative Research- AcSIR}%
\affil[2]{Punjab Remote Sensing Centre}%
\affil[3]{CSIR- Central Scientific Instruments Organization, Chandigarh}%
\affil[4]{Space Applications Centre, ISRO, Ahmedabad}%


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  \date{May 24, 2024}


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\begin{abstract}
Remote sensing approaches based on VIS-NIR spectroscopy can be used for
getting near real-time information about soil fertility. However, the
main challenge limiting the application of spectroscopy in soil
fertility evaluation is finding suitable data pre-processing and
calibration strategies. We have compared various pre-processing
techniques using the reflectance spectra obtained from AVIRIS-NG
hyperspectral images, for quantification of organic carbon (OC),
available phosphorus (P) and available potassium (K) in the surface
soils of Surendranagar area (Western parts of India) and Raichur
(Southern parts of India). Surface (0 - 0.15 m) soil samples were
collected from these two areas synchronously with the dates of the
AVIRIS-NG campaign. The soil samples were air dried, sieved to
\textless{}2 mm, and analyzed for OC, P, and K using standard methods.
The AVIRIS spectra (spectral range of 380-2500 nm with an interval of 5
nm) corresponding to soil sampling points were extracted. The
pre-processing steps were used in the order: Continuum Removal (Yes/No),
Moving Window Abstraction (Yes/No), No transformation or Euclidean
Normalization or Standard Normal Variate (SNV), No transformation or
Savitsky-Golay (SG) first-order smoothing, and No transformation or
first derivative OR second derivative. We have used the partial least
squares regression (PLSR) to calibrate the model from pre-processed
spectra. The PLSR with Continuum Removal, SNV, SG first-order smoothing,
and first derivative was selected as the best algorithm for estimating
soil properties from the Western parts of India, and the corresponding
R2 were 0.77 for OC, 0.79 for P and 0.83 for K (RMSE \textless{}0.3 for
all the parameters). The PLSR with Moving Window Abstraction, SG
first-order smoothing, and second derivative were selected as the best
algorithm for estimating soil properties from the Southern parts of
India, and the corresponding R2 were 0.54 for OC, 0.49 for P and 0.56
for K (RMSE \textless{}0.3 for all the parameters). These results
suggest that the optimization of AVIRIS spectra using various
pre-processing techniques and modeling approaches is required for rapid
and non-destructive assessment and monitoring of soil health for
precision agriculture.%
\end{abstract}%



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