1. Space-Based Imaging Spectroscopy of Soil Contamination
Authors: Asa Gholizadeh, Mohammadmehdi Saberioon, Eyal Ben-Dor, Luboš Borůvka
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Space-Based Imaging Spectroscopy of Soil Contamination
Proximal Sensing of Soil Contamination
Soil Contamination
Natural Gas
Petroleum Hydrocarbons
Potentially Toxic Elements (PTEs)
Table 1: PTEs content (mg/kg)
Location As Mn Zn Cu Cr Ni Pb Cd
Douro, Portugal
38.3
139 57
36.5
92.3
21.4
30.8
0.2
Pokrok, Czech Republic
4.48
599
25.2
13.7 -
18.4
0.27
Smolnica, Poland
142 18
39.9
1.65
Sonepur, India
3.96
947 27 98 34
27.3
0.012
Figure 1: Correlation between reflectance of VIS-NIR-SWIR and PTEs
Gholizadeh et al. (2015)
PTEs
As: Arsenic, Mn: Manganese, Zn: Zinc, Cu: Copper, Cr: Chromium, Ni: Nickel, Pb: Lead, Cd: Cadmium
Remote Sensing of Soil Contamination
Unmanned Aerial Vehicles (UAVs)
Airborne Sensors
Spaceborne Sensors
Unmanned Aerial Vehicles (UAVs)
Aerial Sensors
Table 2: UAVs Advantages and Disadvantages
Table 3: Technical specifications of some airborne sensors
Advantages
Disadvantages
Safety in operation
Lack of collision avoidance technology
High temporal and spatial resolution data
Specialist expertise required
Programmatic flexibility
Small scale of operation
Economic cost
Legal constraints
Environmental friendly
Sensor
Spectral range
(nm)
Channels
(no.)
Spectral bandwidth (nm)
Spatial resolution (nm)
Swath
(km)
SNR
HyMap
128
10-20
2-10
> 500:1
AISA
807
3.3-12 1
> :1
AVIRIS
227 10 30
7.5
CASI
288
1-4
1.6
> 200:1
Rossi and Brunelli (2015)
Mochammad et al. (2016)
Chinchu et a l. (2017)
Choe et al. (2008)
Van der Werff et al. (2008)
PTEs
Petroleum hydrocarbons
Natural gas leakage
Spaceborne Sensors
Advantages: Comprehensive monitoring of large-scale sites, Availability of high quality temporal images, Data reduction, SNR improvements, Better classification of results
Kopackova et al. (2012) used ASTER to monitor PTEs
Arellano et al. (2015) used Hyperion to monitor oil and metal
Figure 4: SHALOM coverage area
Table 4: HyspIRI TIR bands
Figure 3: EnMAP operating principles
Figure 2: Sentinel-2 bands
TIR Bands
λ (nm)
Δλ (nm) 1
3980 80 2
7350
320 3
8280
340 4
8630
350 5
9070
360 6
10530
540 7
11330 8
12050
520
Spectral range: VIS-NIR-SWIR
Spectral bands: 241
GSD: 10 m
Swath: 10 km
Revisiting time: 4 days
Spectral range: VIS-NIR-SWIR
Spectral bands: 214
GSD: 60 m
Swath: 145 km
Revisiting time: 19 days
Spectral range: VIS-NIR-SWIR
Spectral bands: 242
GSD: 30 m
Swath: 30 km
Revisiting time: 27 days
Spectral range: VIS-NIR-SWIR
Spectral bands: 13
GSD: 10 m, 20 m, 60 m
Swath: 290 km
Revisiting time: 5 days
Kokaly et al. (2013) used synthesized image for mapping hydrocarbons.
Feingersh and Ben-Dor (2016) introduced it suitable for soil contamination assessment.
Mielke et al. (2014) used synthesized image for mapping of U, Pb, and Cr.
Rogge et al. (2014) used synthesized image for mapping of Ni, Cu, and Pt.
Mielke et al. (2014) used synthesized image for mapping of U, Pb, and Cr.
References
Conclusions
Arellano, P., Tansey, K., Balzter, H., Boyd, D.S., Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environ. Pollu. 205,
Chinchu, V.J., Sheela, K.G., Manu, A., Implementation of gas detection system using unmanned moving vehicle. Int. Adv. Res. J. Sci. Eng. Technol. 4(2),
Feingersh, T., Ben-Dor, E., SHALOM- A commercial hyperspectral space mission, in: Qian, S.E. (Ed.), Optical Payloads for Space Missions, J. Wiley & Sons Inc., New York.
Gholizadeh, A., Boruvka, L., Vasat, R., Saberioon, M.M., Klement, A., Kratina, J., Tejnecky, V., Drabek, O., Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: A case study. PLoS ONE. 10(2), e
Mielke, C., Boesche, N.K., Rogass, C., Kaufmann, H., Gauert, C., de Wit, M., Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern Push-Broom missions: Hyperion/OLI and EnMAP/Sentinel-2. Remote Sens. 6(8),
Forthcoming innovative remote sensing space sensors provides:
Precision mapping and monitoring of soil contaminants.
Larger spatial coverage, short revisit time, and more frequent area coverage.
A major step towards global soil surface mapping from space.
Better timing of the acquisition and improvement of the results classification.
Dense time series.
*Background Image: Dasht-e-Kavir Mineralogy, Iran, Modified Copernicus Sentinel Data