Soil salinity

Owned by Maiwenn Herpe
Last updated: May 31, 2021 by Frederika Mourot
4 min read

Satellite(s)

Landsat, Sentinel-2, MODIS, Hyperspectral.

Monitoring element

Land surface reflectance.

Satellite(s)

Landsat, Sentinel-2, MODIS, Hyperspectral.

Monitoring element

Land surface reflectance.

Description technique

Several techniques have been proposed for determining soil salinity from remote-sensing data.
Salinity may be estimated via spectral reflectance related to soil surface, or use of halophytic plants or crop performance as indirect proxies for soil salinity. These include reflectance modelling approaches, modelling the relationship between physical measures of soil salinity (Electrical Conductivity - EC) and spectral reflectance (e.g. Sudiero et al, 2015, Fourati et al, 2015 and Bouaziz et al, 2011) or use of machine learning models to produce classifications.
Sudiero et al (2014) proposed a technique in which a dimensionless index combining the NIR and visible band of Landsat was used to score the soil layer reflectance as a proxy for salinity. Scudiero et al (2015) took this further by relating this Canopy Response Salinity Index (CRSI) to a number of ground measurements of EC to develop a predictive surface a the regional level.

Accuracy / Resolution

Variable spatial and temporal resolution according to sensors.

Case study

The regional scale soil salinity assessment for San Joaquin Valley, California by Scudiero et al (2015) showed relationship (linear regression) between CRSI with seven years of Landsat 7 ETM+ images and a ground truth dataset of 22 electromagnetic induction readings. Multi-year results showed a better correlation than single observations.

Fan et al. (2016) showed how inter-calibrated Landsat data could be applied to monitoring soil salinity over the Yellow River Delta, China, from 1985 to 2015. By using a previously developed relationship between spectral reflectance and soil salinity for the Hyperion hyperspectral sensor, the Landsat sensors where then calibrated to the same values as Hyperion ALI, allowing predictions at much larger scales and across different date observations than what could be achieved with the hyperspectral sensor.

Benefits

  • Offers region scale overview of soil salinity, with a quicker updates, greater coverage and lower costs when compared to traditional ground based surveying.

  • In case of multitemporal dataset, can be used to derive temporal as well as spatial trends in salinity.

Limitations

  • While salinity mapping via multispectral data such as Landsat has been shown to be effective (Fourati et al., 2015; Scudiero et al., 2015), the wide bandwidths relative to hyperspectral imagery can limit accuracy (Allbed et al., 2013, Fourati et al., 2015). However, the capture of global scale satellite hyperspectral data has only recently begun with the launch of the PRIMSA sensor by the Italian Space Agency in 2019, and availability is still limited. Airborne capture is also common, but represents a significant cost relative to the use of commercial satellite data, let alone open access multispectral.

  • Studies suggest multispectral sensors such as Landsat 8 and Sentinel-2 are able to resolve broad classes of salinity across semi-arid regions, but the confounding impact of vegetation cover was noted (Fourati et al., 2015, Liu et al., 2019). Liu et al. (2019) used non-matrix factorisation to spectrally unmix a Landsat 5 scene, allowing the soil signal to be better separated from vegetation and improving the predictive power of the resulting model, though the vegetation was controlled and consistent rather than natural.

  • Thermal bands may be required to separate saline impacted soils in sparsely vegetated regions (Allbed et al., 2013).

  • NaCL does not have any distinguishing spectral features within the visible and NIR part of the spectrum, which makes it difficult to identify directly. Wavelengths of this spectrum do not penetrate the soil layer, meaning results are only relevant for the upper soil layer (Goldshleger, 2010).

Applicability for Northland

  • Soil salinity monitoring using satellite data relies on significant ground expression of saltification to allow spectral detection, and has mostly been applied to arid environments to date. While proxy monitoring is possible, it would likely to difficult to separate the impact of soil salinity from other possible causes of vegetative stress, such as drought.

  • Vegetation cover might limit applicability.

  • 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

Allbed, A. and Kumar, L., 2013. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing, 2013.
https://www.scirp.org/journal/paperinformation.aspx?paperid=41262

Fourati, H.T., Bouaziz, M., Benzina, M. and Bouaziz, S., 2015. Modeling of soil salinity within a semi-arid region using spectral analysis. Arabian Journal of Geosciences, 8(12), pp.11175-11182.
https://link.springer.com/article/10.1007/s12517-015-2004-3

Metternicht, G.I. and Zinck, J.A., 2003. Remote sensing of soil salinity: potentials and constraints. Remote sensing of Environment, 85(1), pp.1-20.
https://www.sciencedirect.com/science/article/pii/S0034425702001888

Gorji, T., Tanik, A. and Sertel, E., 2015. Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science, 15, pp.507-512.
https://www.sciencedirect.com/science/article/pii/S1878522015003252

Scudiero, E., Skaggs, T.H. and Corwin, D.L., 2015. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment, 169, pp.335-343.
https://www.sciencedirect.com/science/article/pii/S0034425715301127?via%3Dihub

Scudiero, E., Skaggs, T.H. and Corwin, D.L., 2014. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2, pp.82-90.
https://www.sciencedirect.com/science/article/pii/S2352009414000285?via%3Dihub

Liu, Y., Zhang, F., Wang, C., Wu, S., Liu, J., Xu, A., Pan, K. and Pan, X., 2019. Estimating the soil salinity over partially vegetated surfaces from multispectral remote sensing image using non-negative matrix factorization. Geoderma, 354, p.113887.
https://www.mdpi.com/2072-4292/12/24/4118

Goldshleger, N., Ben-Dor, E., Lugassi, R. and Eshel, G., 2010. Soil degradation monitoring by remote sensing: examples with three degradation processes. Soil Science Society of America Journal, 74(5), pp.1433-1445.
https://acsess.onlinelibrary.wiley.com/doi/abs/10.2136/sssaj2009.0351

Bouaziz, M., Matschullat, J. and Gloaguen, R., 2011. Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil. Comptes Rendus Geoscience, 343(11-12), pp.795-803.

https://www.sciencedirect.com/science/article/pii/S1631071311001945

Fan, X., Weng, Y. and Tao, J., 2016. Towards decadal soil salinity mapping using Landsat time series data. International journal of applied earth observation and geoinformation, 52, pp.32-41.

https://www.sciencedirect.com/science/article/pii/S0303243416300757?via%3Dihub

Yong-Ling, W.E.N.G., Peng, G. and Zhi-Liang, Z., 2010. A spectral index for estimating soil salinity in the Yellow River Delta Region of China using EO-1 Hyperion data. Pedosphere, 20(3), pp.378-388.

https://www.sciencedirect.com/science/article/pii/S1002016010600276

Other comments or information

Use of repeat pass hyperspectral dataset e.g. PRISMA.