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CHARACTERIZATION OF SOIL AND RETRIEVAL OF ITS PARAMETERS THROUGH HYPERSPECTRAL REMOTE SENSING

KrishiKosh

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Title CHARACTERIZATION OF SOIL AND RETRIEVAL OF ITS PARAMETERS THROUGH HYPERSPECTRAL REMOTE SENSING
 
Creator HARSHA KUMARA KADUPITIYA
 
Contributor R. N. Sahoo
 
Subject economic resources, soil properties, land resources, biological phenomena, sampling, remote sensing, area, soil sampling, sets, clay
 
Description t-8155
Soil is the most valuable natural resource on the earth and provides the base
for all life processes. Information of soil variability at required time and place is
vital for wise resource use. In the present study, as an alternate to conventional
laboratory soil analysis, hyperspectral remote sensing which is non-destructive, cost
effective and capable of spatial prediction has been investigated for surface soil
characterization in 7.36 x 106 km2
strip covering Jalandhar and partly Hoshiarpur
and Ludhiana district in Punjab Sate, India. The objectives were to characterize soils
using hyperspectral reflectance, to predict soil parameters from hyperspectral remote
sensing data both from ground and space based platforms and to evaluate accuracy
and predictability of soil parameters derived by hyperspectral remote sensing. Soil
parameters evaluated were mineralizable nitrogen(N), available phosphorous (P) and
potassium (K), DTPA extractable manganese (Mn), iron (Fe), copper (Cu) and zinc
(Zn), calcium carbonate (CaCO3, soil organic carbon (SOC), pH (1:2.5), EC (1:2.5),
bulk density (BD), particle density (PD), hydraulic conductivity (Ks) and soil
texture. Laboratory- and field-measured reflectance from Spectroradiometer (ASD,
FS3, 350-2500 nm) and space-borne Hyperion sensor reflectance data of EO-1
satellite have been evaluated for soil characterization. Statistical methods used were
correlation analysis, stepwise regression approach (SRA) and principal component
analysis (PCA). Two third of 85 soil samples were used for development of model
and rest used for validation. Spectral correlation analysis revealed that chemical soil
parameters have higher correlations with reflectance than physical soil parameters.
SRA approach resulted 10 nm interval as optimum band width for soil parameter
assessment. Reflectance (R), absorbance (A) and their first and second derivatives
(R′, R″, A′ and A″) were used to develop prediction models and found that derivative
spectra are preferred for better prediction. Validation has been done with standard
error of prediction (SEP), ratio prediction deviation (RPD) and range error ratio
(RER). Values of RPD and RER suggests that except for pH, BD, PD and Ks
, all the
other soil parameters can be reliably predicted using derivative spectra of laboratory
measured reflectance while PCA is not convincing for soil parameter assessment.
Evaluation of soil parameter predictability of laboratory, field, and Hyperion
reflectance, using wavelength regions common to all the three platforms, disclosed
that model predictability of Hyperion reflectance data is lowest. But for some
parameters like SOC and CaCO3, Hyperion space-borne sensor data could still be
resulted reasonably good predictability even with higher noise inherent to spaceborne
sensors due to atmospheric effects and large area averaging (moderate spatial
resolution).
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Date 2016-12-23T09:42:56Z
2016-12-23T09:42:56Z
2009
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/92332
 
Format application/pdf
 
Publisher DIVISION OF AGRICULTURAL PHYSICS