Soil degradation monitoring by active and passive remote-sensing means: examples with two

degradation processes

Naftaly Goldshleger, *Eyal Ben-Dor,* *Ido Livne,* U. Basson***, and

Vladimir Miralis**R.Ben-Binyamin

*Soil Erosion Research Station

** Tel Aviv University

***Geosense

Background

Soil Degradation is defined as a loss of soil production by either chemical or physical processes.

Recent developments in the monitoring of soil degradation processes (Crust SalinizationIncreased Runoff) have used passive remote sensing and active remote-sensing tools such as ground-penetrating radar (GPR) and frequency domain electromagnetic induction (FDEM)

objective

To show how remote sensing (ACTIVE and PASSIVE ) methods can be used for soil degradation observation and monitoring.

If available in advance precaution can be taken

Passive remote sensing methods

Passive and active remote sensing methods

Active remote sensing methods

Passive remote sensing-ASD Soil Spectroscopy refers to reflected electromagnetic

radiation that interacts with the soil (surface) matter across the spectral range VIS-NIR-SWIR of the sun illumination radiation.

Spectral Methods Using ASD Field spectrometer for soil surface

Each sample is tested by spectral and chemical measurement, for comparison.

The calibration and spatialrepair of the data, provide avisual cross section of thesoil layers at differentdepths.

Active remote sensing -GPR

GPR (Ground Penetration

Radar) Transmits radar pulses

into the ground; and receives

wave signals reflected off of the

interfaces below.

Resolution m Depth m

Frequencies MHz

0.6 7-15 100

0.4 3-9 250

0.3 2-5 500

0.2 1-2 1000

Active remote sensing- FDEM Scanning

FDEM 96 Hz - 100 kHz

Conductivity, resistivity, magnetic susceptibility and frequency sounding measurements were acquired with a GEM-2 FDEM (frequency domain electro-magnetic) instrument at several effective frequencies.

Electromagnetic Spectrum

0.6 7-15 100 0.4 3-9 250

0.3 2-5 500

0.2 1-2 1000

Resolution m Depth m Frequencies MHz

Examples: two degradation processes

Physical Crust

Salinization

Physical Crust (Structural Crust)

Definition: A thin layer formed on the soil surface during rainstorm events. The crust is the result of a physical segregation and rearrangement of soil particles.

Origin: The outcome of the impact of the raindrops kinetic energy and the stability of the soil aggregates

Crust

Sole1

mm

Microscopic Cross Section

Problems and Solution

Soil Degradation effects : The crust significantly affects many dynamic soil properties such as : decreasing infiltration rate, surface roughness, soil water storage and capacity, increasing runoff and soil erosion

Lack of information: As a dynamic property , no information on its spatial distribution nor magnitude is available prior to the next rain event

Solution: To use reflectance spectroscopy

Crust

Erosion

Run off

:Problems

infiltration tube

runoff tube

soil tray nozzle

carousel

Rain Simulator

A facility to study the soil physical crust

Laboratory Experiment

0 joule1842 joule650 joule

Loess Soil

The crust was created by rain fall simulator

Using various energy value

Spectral Results

1.7m 2.2 m

Reflectance at 1.7m vs. Infiltration Rate

Spectral Index

AISA Airborne Image Spectrometer

182Spectral Bands 423-2400nm (FWHM 6.21-6.84nm)

Altitude of 3000m, Pixel size 2m

CrustBraking Crust

crusted

Non crusted

20 m

AB

D

C

Vegetation

high low

NBen-Dor et al., 2004

Soil Infiltration and Erosion : Physical Crust

Conclusion for the Physical Crust

For the crust analysis, passive methods - mainly soil reflectance - can be used as tools to monitor, assess and map the soil-crusting phenomenon and related properties (runoff, infiltration, etc.)

More specific conclusions are published in the following papers

Goldshlager N. Ben-Dor E, Y. Benyamini, M. Agassi and D. Blumberg 2002, Spectral properties and hydraulic conductance of crusts formed by raindrop impact. International Journal of Remote Sensing 19:3909-3920

Ben-Dor E. Goldahlager N, Benyamini M. and D.G. Blumberg 2003 The Spectral Reflectance properties of Soils structural crust in the SWIR spectral region (1.2-2.5 m), Soil Science Society of American Journal 67:289-299

Ben-Dor E. , N. Goldshalager, O. Braun , B. Kindel , A.F.H.Goetz , D. Bonfil , M. Agassi, N. Margalit , Y. Binayminy and A. Karnieli 2004 Monitoring of Infiltration Rate in Semiarid Soils using Airborne Hyperspectral Technology International Journal of Remote Sensing 25:1-18

Goldshlager N, Ben-Dor E., Chudnovsky A., and M. Agassi 2009 Soil reflectance as a generic tool for assessing infiltration rate induced by structural crust for heterogeneous soils. European Journal of Soil Science (in press)

Soil salinity

Soil degradation by salinity: Decreases soil productivity, low water infiltration to the soil profile, runoff and high soil erosion rate, infertility.

Lack of Information: As a dynamic property no information on its spatial distribution nor magnitude is available . If such information were available in advance, precautions could be taken.

Solution: To use active and passive remote sensing means (HSR, GPR, FDEM) Estimates extent of salt affected areas are in general close to one billion hectares

which represents about 7 percent of the earth's continent extent (Ghassemi et al, 1995)

This phenomenon is related to a high water table and low water quality

The Working Scheme

Soil Spectral measurement in the field : 0cm, 30cm, 60cm

Soil measurement (EC) in the Laboratory

Sampling Points

Test

Comparison of our spectrum data to the laboratory spectrum data for Gypsum

1450nm

1480nm

1550nm1750nm

2200nm

1950nm

Important

absorption in

relation to air-bone

sensor

Halite

Spectrum

Absorption

Surface Gypsum Correlated with 60 cm depth EC

y = 0.3568x - 1.98

R2 = 0.9566

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200 250

Surface CaSO4 (meq/l)

60 c

m d

ep

th E

C (

ds/m

)

:A model between Spectral and Lab information-

Ec

Spectral changes along the scanning line In Genigar field

15

Genigar field

Sampling point

15 m

Shift

FDEM

The use of an air-photo from 1962, and of GPR, for characterization of the buried layer.

Eastern scan

line

Lateral changes in the soil

layer, point to the

existence of a buried layer .

content

ASD based Model for AISA spectral configuration (Test Results)

R = 0.9012

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Measu

red

EC

Predicted EC

Predicted and measured Electrical conductivity (test samples)

RPD=2.54

Factor (for continuum removed spectrum)Wavelength (nm)

634.109192b0

166.6197357540.74

-918.89599611503.06

-1519.5865481989.99

2564.2802732036.36

-919.46112062175.48

-951.84606932187.08

874.77886962221.86

the

ds/m

A comparison between measured EC results and predicted EC results

from the AISA-ES image.

The results indicate that chemical methods which are correlated with remote sensing

methods give a correct picture of soil salinity.

A spectroscopy based EC prediction model can be built using relatively low spectral

resolution and excluding water vapor absorption bands.

A model of this kind can be applied to air-borne hyperspectral imagery in order to map soil salinity in a fast, accurate and cost effective way.

FDEM data will be added to the model in the future and will contribute to increase its accuracy.

Non-salineSlightly salineModerately salineSeverely saline

Salinity Map (UN) Syr-Darya

Uzbekistan

110

Percentage of area affected by salinity (by severity levels)

Conclusion for the Soil Salinity

1. Spectroscopy is a sensitive field method that can be used to locate saline-affected areas.It can be clearly seen (Uzbekistan results) thatthe soil salinity property can be effectively predicted from the reflectance information across specific wavelengths (1750nm, 1940nm, and 1980nm).

2. Using soil surface information, Halite and Gypsum correlations can help in the assessment of salinity, to a depth of 60 cm

3. All domainsfield, airborne and space-borneare feasible for this application.

4. The GPR system can be used to map the subsurface regional structure, and to point out anomalies which could indicate saline problems.

5. The FDEM results were dove-tailed with the multi- sensor approach, for precise detection and a geo-referenced database of soil salinity changes, enables mapping and prediction of the salinization process.

6. Comparing the airborne and satellite images with field-collected spectral data by using hyper- spectral imagery canindicate the severity of soil salinity.

7. Novel method: mm wave instrument, and

.

Novel method

Fiber optic

Mirror at 45 angle

ASD

Halogen Lamp

Stabilizer bar

Handle bar

Fiber Holder

(0.8cmf )

Lamp holder

(1.2 cm f)

Handle bar

ab

Catherization: optic fibrous-Dor et al., 2008).

Spectra & Video

Measurement setup Millimetre-wave backscattering

General Conclusions

The two soil degradation factors can potentially be

monitored by passive and active remote sensing.

The main advantages of the passive spectral remote-sensing

method are its availability and ability to cover large areas; on

the other hand, it only senses the soil surface.

For salinity, an electromagnetically based approach (an active

remote-sensing technique) could provide additional information

on the salinization process. In the case of soil salinization,

monitoring the underground layers is crucial, and the active

remote-sensing methods (e.g., GPR and FDEM) and mm

waves in the future can be used for this purpose but are limited

by the size of the area covered.

Thank You

Naftalig@moag.gov.il