WELCOME

Sumanta ChatterjeePh.D Research ScholarDivision 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

Contd…

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

Boyan Kuang et al., 2012

Geonics EM-38 EC meter

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

Electrical resistivity

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

Boyan Kuang et al., 2012

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 MC2. Soil texture3. Soil compaction4. Water table5. Soil color and OC content6. Delineate hard pans7. Hydraulic parameters

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

Boyan Kuang et al., 2012

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 :

Boyan Kuang et al., 2012

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

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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

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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

thank you