This slide is all about proximal sensing of soil properties including lab techniques and proximal remote sensing. Hope it will help soil science scholars and acade
2. Sumanta Chatterjee
Ph.D Research Scholar
Division of Agricultural Physics
Indian Agricultural Research Institute
New Delhi
Sensing of Soil Properties : Recent
Development
3. Conventional System Of Soil
Characterization
Time consuming
More laborious
Low area coverage
Costly
Less precision
Less repeativity
Remote sensing is a high throughout method
4. 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
6. 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
7. Soil Sensors
Reflectance based soil sensors
Conductivity, resistivity, and permittivity based soil
sensors
Passive radiometric based soil sensors
Strength based soil sensors
8. 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).
9. 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
10. 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)
11. 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
12. Summary of measurement accuracy of soil fundamental properties by laboratory
visible and near infrared (vis–NIR) spectroscopy
Chang et al., 2001
14. Heavy Metal
Moron and Cozzolino (2003) explored the use of NIR reflectance spectroscopy to
study microelements in surface soils from 332 sites across Uruguay.
15. 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)
16. Summary of accuracy of soil properties measured by mid-infrared (MIR) spectroscopy
Chang et al., 2001
17. 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)
18. Electromagnetic induction
Numerous authors claim to quantitatively map different soil properties such as
salinity, clay content , and MC with ECa measured by EMI devices
Boyan Kuang et al., 2012
22. Fig : A typical Fusion soil sensor which measure EC, MC, PR etc
23. 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
24. Soil properties measured in situ with ground penetrating radar
(GPR) techniques
Boyan Kuang et al., 2012
26. 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.
27. Figure : A capacitance soil water content sensor prototype
28. Soil moisture content measured in laboratory and in situ using
FDR and TDR :
Boyan Kuang et al., 2012
29. 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).
30. 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
32. 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)
33. 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
34. 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
35. 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
36. 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
37. Place : Maryland, USA
Sample : 315
Area : 65 ha
Used : Imaging Spectrometer, PLSR, GPS, ENVI 4.7, LIDAR
DEM data
40. 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
……………………………………………………………………………………………………………………………………………………
41. 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
…………… ……………………… ……………
42. 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)
44. Map of predicted values for selected analytes (C, Silt, Fe, Al),
overlaid on a high-resolution digital elevation map
45. 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
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.
R2 value don’t just represent the particular studies enlisted, but they are also based on other studies not listed in this table.
Total soil C varied from 5 to 187 g/kg, and total N from 0.25 to 7.52 g/kg (Figure 1a). Soil C to N ratio generally varied with land-use: lower (median = 16) for soils with a current or recent agricultural history (including E. globulus established on pasture) and higher (median = 21) for soils from forest / remnant vegetation land-uses (including P. radiata plantations). The overall correlation, between total N and total C was r = 0.86 (P < 0.001), and within land-uses correlations varied from 0.91 to 0.97 (P < 0.001).
For simple illustrative purposes here, pasture, native forest / regenerated woodlands and remnant vegetation were used as a reference land-use (RLU) to present the effects of a subject land-use (SLU, i.e. plantation or crop, Figs 2a, b, c).
Back-transformed predictions for total C (Figure 3a) were excellent, both for calibration (R2 = 0.96) and validation (R2 = 0.94) data subsets. Predictions for total N (Figure 3b) were respectively very good (R2 = 0.97) and good (R2 = 0.86). The calibration prediction for MBC (Figure 3c) was good (R2 = 0.73), despite MBC not being very well correlated with total C. MBC predictions for the validation subset had considerable scatter (R2 = 0.53), although fair for microbial biomass (Stenberg and Viscarra Rossel 2008).