Sumanta's slideshare

Sumanta Chatterjee
Ph.D Research Scholar
Division of Agricultural Physics
Indian Agricultural Research Institute
New Delhi
Sensing of Soil Properties : Recent
Development

Conventional System Of Soil
Characterization
 Time consuming
 More laborious
 Low area coverage
 Costly
 Less precision
 Less repeativity
Remote sensing is a high throughout method

Visible and Near IR Systems
 Panchromatic imaging system : IKONOS
PAN, SPOT,HRV-PAN
 Multispectral imaging system: LANDSAT MSS,
LANDSAT TM , SPOT HRV-XS , IKONOS MS
 Superspectral Imaging Systems : MODIS ,
MERIS
 Hyperspectral Imaging Systems : Hyperion on
EO1 satellite

Spectral Signature Curves of Soil, Water and Vegetation :

Soil constituent influencing spectral reflectance:
 Soil moisture content
 Particle size distribution
 Soil mineralogy
 Parent materials
 Organic matter
 Presence of iron oxides
 Effect of salinity
 Soil color
 Order of soil taxonomy
 Management practices : Tillage

Soil Sensors
 Reflectance based soil sensors
 Conductivity, resistivity, and permittivity based soil
sensors
 Passive radiometric based soil sensors
 Strength based soil sensors

A.Reflectance sensors
 The fundamental vibrations in the mid-infrared (MIR) region result
in overtones and/or combinations in the near infrared (NIR)
region.
 In the visible range (400–780 nm), absorption bands related to soil
color are due to soil organic matter content (SOM) and moisture
content (MC)
 In the NIR range, the overtones of OH and overtones and/or
combinations of C-H + C-H, C-H + C-C, OH+ minerals, and N-H are
important for the detection of SOM, MC, clay minerals, and
nitrogen (Mouazen et al., 2010).

1.Visible–near infrared sensors
Earlier vis–NIR (400–2500 nm) spectroscopy, along with multiple
linear regression calibration technique, was used to determine soil
properties, such as soil MC, SOM, total carbon (TC), inorganic
carbon (Cin), OC, pH, CEC, and total nitrogen (TN)
 Bowers and Hanks (1965) used a NIR spectrophotometer to
evaluate the influences of MC, SOM, and particle size on energy
reflectance
With the emerging of commercial NIR spectrophotometers and
multivariate calibration software packages, the vis–NIR
spectroscopy has been adopted much widely for analysis of key
soil properties (MC, pH, SOM, TN, and OC) with high accuracy

Soil properties with direct spectral responses in near
infrared range
C and N have both direct spectral responses in the NIR
region, which can be attributed to overtones and
combinations of N-H, C-H + C-H and C-H + C-C
 Chang et al. (2001) found TC, TN, and MC to be readily
and accurately estimated (R2 > 0.84; ratio of prediction
deviation (RPD > 2.47)

Boyan Kuang et al., 2012
Fig : Histogram of no. of studies reported on different R2 categories for the
laboratory measurement of SOC with Vis-NIR spectroscopy taken as an example

Summary of measurement accuracy of soil fundamental properties by laboratory
visible and near infrared (vis–NIR) spectroscopy
Chang et al., 2001

Heavy Metal
Moron and Cozzolino (2003) explored the use of NIR reflectance spectroscopy to
study microelements in surface soils from 332 sites across Uruguay.

Mid-infrared spectroscopy
 When subjected to light, the fundamental molecular vibrations occur at
frequencies in the MIR range of 2500–25000 nm.
 Among different MIR spectroscopy techniques, the MIR diffuse reflectance and
infrared attenuated total reflectance spectroscopy are important.
 In the diffuse reflectance (infrared) technique, commonly called DRIFT, the
DRIFT cell reflects radiation to the powder/ soil and collects the energy reflected
back over a large angle.
 The attenuated total reflectance (ATR) spectroscopy utilizes the phenomenon
of total internal reflection.
 Literature confirms that DRIFTS can outperform vis–NIR for the quantification
of soil carbon (McCarty and Reeves, 2006)

Summary of accuracy of soil properties measured by mid-infrared (MIR) spectroscopy
Chang et al., 2001

B. Conductivity, resistivity, and permittivity based soil sensors
This class includes measurement of -
 Electromagnetic induction (EMI).
 Electrical resistivity (ER) or Conductivity (EC)
 Time domain reflectance (TDR)
 Frequency domain reflectance (FDR)
 Ground penetrating radar (GPR)

Electromagnetic induction
Numerous authors claim to quantitatively map different soil properties such as
salinity, clay content , and MC with ECa measured by EMI devices

Huth et al., 2007
Fig : calibration of EC using EM 38 sensor

Electrical resistivity
Soil properties measured by contact type electrical resistivity
(ER/ECa) sensors :

Fig : A typical Fusion soil sensor which measure EC, MC, PR etc

Ground penetrating radar
 The working principle of GPR is similar to reflection seismic and
sonar techniques
 GPR systems work in a frequency range of 10–5000 MHz (e.g.,
VHF-UHF)
 GPR is used to determine
1. Soil MC
2. Soil texture
3. Soil compaction
4. Water table
5. Soil color and OC content
6. Delineate hard pans
7. Hydraulic parameters

Soil properties measured in situ with ground penetrating radar
(GPR) techniques

Fig : Working Principle of a GPR device

Permittivity based sensors
 Permittivity based soil sensors measure changes in
dielectric properties of soils by transmitting an EM
wave into the soil matrix.
 These sensors are categorized as time domain
reflectometry or reflectometers (TDR) and
frequency domain reflectometry or reflectometers
(FDR).
 Dielectric sensors are mostly used for determining
MC.

Figure : A capacitance soil water content sensor prototype

Soil moisture content measured in laboratory and in situ using
FDR and TDR :

C. Passive radiometric sensing
Gamma-ray spectrometers :
 Widely used in mineral exploration and environmental and
geological mapping
 Gamma-ray spectra are typically recorded at a frequency of up to
1 Hz.
 The gamma spectrometers can be used by mounting on an
aircraft or on ground vehicles to scan the fields.
 These ground-based gamma spectrometers were used to
estimate soil texture, plant available K , and other minerals
(Viscarra Rossel et al., 2007).

Fig : (A) A proximal passive γ radiometric sensor mounted on a
multisensor platform, (B) a γ-ray spectrum showing the energies of
the potassium (K),uranium (U), and thorium (Th) bands.
Viscarra Rossel et al., 2011

Soil properties measured with on-line proximal gamma-ray
spectrometry :
Chang et al., 2001

Place : Southern Australia
Samples : 812 , 0-10 cm depth
Land Use : Agriculture (Crop and Pasture)
Forest plantation
Native Vegetation
Used : Mid-infrared spectroscopy(MIRS)
Partial Least Square Regression(PLSR)

Results and discussion
Fig : Scatter plots of (a) total N and total C, and (b) microbial biomass
carbon (MBC) and total C. Values are means across replicate samples

Fig : Scatter plots of (a) total C, (b) total N, and (c) microbial biomass carbon
(MBC) in subject land-use (SLU) and that in reference land-use (RLU). SLU-
RLU comparisons are: E. globulus plantation – pasture (WA), Pinus radiata
plantation – native forest / regenerated woodland (Vic. / NSW), and crop –
remnant vegetation (Vic.). Values are means across replicate samples

Statistics for MIRS-PLSR calibration and validation for total C, total
N and microbial biomass carbon (MBC)
ANumber of latent variables
BRoot mean square error of calibration
CRoot mean square error of crossvalidation
DRoot mean square error of prediction

Fig : Relationships between the MIRS-PLSR predicted and the measured values for the
calibration and validation (bold text) data subsets of (a) total C, (b) total N and (c) MBC

Place : Maryland, USA
Sample : 315
Area : 65 ha
Used : Imaging Spectrometer, PLSR, GPS, ENVI 4.7, LIDAR
DEM data

Results and Discussion
Observed laboratory analyte concentrations for the six bare soil fields

Partial least squares (PLS) prediction model goodness of fit (R2) associated with each of 15
math treatments, for the 13 analytes that predicted with R2 > 0.5
……………………………………………………………………………………………………………………………………………………

Partial least squares (PLS) model accuracy in predicting soil analyte concentrations1 for (a)
the 269 calibration samples and (b) 46 validation samples from the field
…………… ……………………… ……………

Maps of predicted soil carbon content calculated from (a) unsmoothed imagery (1-
pixel data extraction) and (b) spatially smoothed imagery (9-pixel data extraction).
Predicted values ranged from 0.4% (black) to 2.5% (white)

Unsmoothed (1-pixel) imagery with 2.5m resolution vs spatially smoothed (9-pixel) imagery

Map of predicted values for selected analytes (C, Silt, Fe, Al),
overlaid on a high-resolution digital elevation map

conclusion
 Accurate mapping of soil properties is made difficult due to high spatial variability
observed within agricultural fields, however, advances in remote sensing
technology are now providing tools to support geospatial mapping of soil
properties.
 Diffuse reflectance spectroscopy offers a rapid and nondestructive means for
measurement of soil properties based on the reflectance spectra of illuminated
soil.
 As hyperspectral imagery becomes more readily available and at lower cost, the
application of partial least squares (PLS) regression to soil spectral reflectance data
can provide an effective method for calculating high-resolution raster maps of
important soil properties including texture, pH, and carbon and nutrient content.
 Mid infrared reflectance spectroscopy (MIRS) has been demonstrated to be useful
for the analysis of soils, including for total and various fractions of carbon, some
nutrients, and soil texture

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