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

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1 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 R.Ben-Binyamin*Vladimir Miralis* *Soil Erosion Research Station ** Tel Aviv University ***Geosense

2 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 Salinization Increased Runoff) have used passive remote sensing and active remote-sensing tools such as ground-penetrating radar (GPR) and frequency domain electromagnetic induction (FDEM)

3 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

4 Active remote sensing methods Passive and active remote sensing methods Passive remote sensing methods

5 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.

6 Active remote sensing -GPR GPR (Ground Penetration Radar) Transmits radar pulses into the ground; and receives wave signals reflected off of the interfaces below. The calibration and spatial repair of the data, provide a visual cross section of the soil layers at different depths. Frequencies MHz Resolution m Depth m

7 Active remote sensing- FDEM Scanning FDEM 96Hz khz Conductivity, resistivity, magnetic susceptibility and frequency sounding measurements were acquired with a GEM-2 FDEM (frequency domain electromagnetic) instrument at several effective frequencies.

8 Electromagnetic Spectrum Resolution m Depth m Frequencies MHz

9 Examples: two degradation processes Physical Crust Salinization

10 1mm 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 Microscopic Cross Section Sole

11 Problems: 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

12 Rain Simulator A facility to study the soil physical crust soil tray nozzle infiltration tube carousel runoff tube

13 Laboratory Experiment Loess Soil 0 joule 650 joule 1842 joule The crust was created by rain fall simulator Using various energy value

14 Spectral Results 1.7µm 2.2 µm

15 Spectral Index Reflectance at 1.7µm vs. Infiltration Rate

16 AISA Airborne Image Spectrometer 182Spectral Bands nm (FWHM nm) Altitude of 3000m, Pixel size 2m

17 Crust Braking Crust

18 crusted Non crusted 20 m

19 Soil Infiltration and Erosion : Physical Crust Ben-Dor et al., 2004 N C Vegetation D B A high low

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

21 Soil salinity This phenomenon is related to a high water table and low water quality 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)

22 The Working Scheme Sampling Points Test Soil Spectral measurement in the field : 0cm, 30cm, 60cm Soil measurement (EC) in the Laboratory

23 Comparison of our spectrum data to the laboratory spectrum data for Gypsum Important absorption in relation to air-bone sensor 1550nm 1750nm 1480nm 1450nm 2200nm Absorption Halite Spectrum 1950nm

24 60 cm depth EC (ds/m) Surface Gypsum Correlated with 60 cm depth EC y = x R 2 = Surface CaSO4 (m eq/l) Ec A model between Spectral and Lab information: -

25 Spectral changes along the scanning line In Genigar field Genigar field 15 מטרים 15 m Shift Sampling point

26 FDEM

27 The use of an air-photo from 1962, and of GPR, for characterization of the buried layer. Eastern scan line content Lateral changes in the soil layer, point to the existence of a buried layer.

28 ASD based Model for AISA spectral configuration (Test Results) Measured EC Predicted and measured Electrical conductivity (test samples) R² = RPD= רר the Predicted EC Wavelength (nm) Factor (for continuum removed spectrum) b

29 A comparison between measured EC results and predicted EC results from the AISA-ES image. ds/m

30 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.

31 Uzbekistan Syr-Darya Salinity Map (UN) Non-saline Slightly saline Moderately Severely saline saline

32 Percentage of area affected by salinity (by severity levels) 0 11

33 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) that the 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 domains field, airborne and space-borne are feasible for this application.

34 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 can indicate the severity of soil salinity. 7. Novel method: mm wave instrument, and

35 b Novel method a Handle bar Fiber optic Handle bar Stabilizer bar Lamp holder (1.2 cm f) Halogen Lamp Mirror at 45 angle Fiber Holder (0.8cm f ) ASD Catherization: optic fibrous -Dor et al., 2008).

36 Spectra & Video

37 Measurement setup Millimetre-wave backscattering

38 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.

39 Thank You

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