Soil contamination

Owned by Maiwenn Herpe
Last updated: Jun 24, 2021 by Frederika Mourot
3 min read

Satellite(s)

Landsat, Sentinel-2, MODIS, Hyperspectral.

Monitoring element

Soil reflectance.

Satellite(s)

Landsat, Sentinel-2, MODIS, Hyperspectral.

Monitoring element

Soil reflectance.

Description technique

Soil contamination is difficult to detect through remote sensing, which is by definition indirect. To date there have been limited applications of spaceborne sensors to soil contamination and monitoring due to a lack of appropriate sensors, though future hyperspectral systems are potentially applicable to this topic (Gholizadeh et al, 2018).
Field based proximal and airborne hyperspectral sensors have been shown to successfully detect a range of different contaminants via indirect influence of soil spectral characteristics (Gholizadeh et al, 2018). However, the confounding nature of vegetation coverage for satellite remote sensing means that contamination detection has largely been achieved via the use of vegetation as a bioindicator of soil state (Gholizadeh et al, 2019).
By looking at the relationship between the visible (red) and the NIR-SWIR ends of the spectrum, vegetative stress can be detected, with a range of responses noted to different soil contaminants, though there was variation between plant types and regions confounding the derivations of simple relationships (Gholizadeh et al, 2019).
These values can be mapped as spectral indices such as NDVI, allowing the extraction of a single value or score, but there will be significant variations caused by a large range of possible factors beyond soil contamination, so care must be taken to ensure relative consistency between vegetation used as a control and and as an indicator within possible contaminated sites. Regardless, it may be difficult to determine the nature of soil contamination via vegetative stress.

Accuracy / Resolution

Variable spatial and temporal resolution according to sensors.

Case study

Arellano et al (2015) used the Hyperion hyperspectral sensor to detect hydrocarbon soil contamination via mapping vegetative stress. This process demonstrated that hyperspectral imagery could detect spectral response related to a reduction in chlorophyll content resulting from soil contamination.

Benefits

  • More cost effective than traditional techniques, which require large scale field sampling and laboratory testing (Gholizadeh et al, 2018).

  • Offers greater responsiveness, scale, temporal granularity and simplicity than field methods (Gholizadeh et al, 2018).

Limitations

  • Multispectral sensors struggle to provide the spectral resolution required to extract useful soil indices for evaluating contamination (Gholizadeh et al, 2018).

  • Soil remote sensing is confounded by vegetation coverage (Gholizadeh et al, 2018).

Applicability for Northland

Maybe not at the moment but likely in a few years as soil contamination is still an area of development for RS applications. The recent (e.g. PRISMA) and forthcoming (e.g. EnMAP) spaceborne hyperspectral sensors should facilitate a growth in research and application in this subject area, so it may be worth waiting until more mature approaches are available.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication references

Gholizadeh, A., Saberioon, M., Ben-Dor, E. and Borůvka, L., 2018. Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives. Critical Reviews in Environmental Science and Technology, 48(3), pp.243-278.
https://www.tandfonline.com/doi/full/10.1080/10643389.2018.1447717

Gholizadeh, A. and Kopačková, V., 2019. Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques. International Journal of Environmental Science and Technology, 16(5), pp.2511-2524.
https://link.springer.com/article/10.1007/s13762-019-02310-w

Arellano, P., Tansey, K., Balzter, H. and Boyd, D.S., 2015. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205, pp.225-239.

https://www.sciencedirect.com/science/article/pii/S0269749115002754