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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 52 issue 5 (october 2018) : 524-529,   Doi: 10.18805/IJARe.A-316

Soil salinity mapping utilizing sentinel-2 and neural networks

R
R.S. Morgan
M
M. Abd El-Hady
I
I.S. Rahim
1Soil and Water Use deptartment, Agricultural and Biological Research Division, National Research Centre, El Behouth St., Dokki, Cairo, Egypt.
Cite article:- Morgan R.S., El-Hady Abd M., Rahim I.S. (2018). Soil salinity mapping utilizing sentinel-2 and neural networks. Indian Journal of Agricultural Research. 52(5): 524-529. doi: 10.18805/IJARe.A-316.

ABSTRACT

Soil salinity is the most important soil property that affects the agriculture productivity. Periodical monitoring of its status is considered a crucial factor in the selection of appropriate agricultural practices to attain a sustainable production. The availability of remote sensing data processed by a somewhat novel method such as artificial neural networks (ANN) offer a potential solution that could easily and affordably replace the in-site monitoring methods. The aim of this work is to use high spectral resolution Sentinel-2 (S2) data for soil salinity prediction utilizing neural networks. The study evaluated three approaches in processing the S2 data for inclusion in the artificial neural network for soil salinity prediction. These approaches included S2 spectral reflectance data, spectral indices and principal component analysis (PCA) of the S2 data. The results revealed that a combination of these approaches including the reflectance data of band 11(shortwave infrared band) of S2, the normalized differential vegetation index (NDVI) and the second PCA (dominated by the near infrared band) gave the best performance when used as input when designing the artificial neural networks to predict the soil salinity. The overall accuracy of this approach has a coefficient of determination (R2) of 0.94 between the actual and predicted soil salinity.

REFERENCES

  1. Abbas, A. and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. Proceedings of the International Congress on Modelling and Simulation (MODSIM ’07), Oxley L. and Kulasiri D., (Eds)., Modelling and Simulation Society of Australia and New Zealand, Brighton, UK. pp. 2632–2638,
  2. Al-Khaier, F. (2003). Soil salinity detection using satellite remotes sensing. Master of Science in Geo-information Science and Earth Observation, International Institute for Geo-Information Science and Earth Observation, Enscheda, The Netherlands. 
  3. Azabdaftari, A. and Sunar, F. (2016). Soil Salinity Mapping using Multitemporal LANDSAT Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
  4. Danson, F.M. and Bowyer, P. (2004). Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment. 92: 309-321.
  5. Erzin, Y., Rao, B.H., Patel, A., Gumaste, S.D. and Singh, D.N. (2010) . Artificial Neural Network Models for Predicting Electrical Resistivity of Soils from Their Thermal Resistivity. International Journal of Thermal Sciences, 49:118-130.
  6. ESA(2015). Sentinel-2 User Handbook. ESA Standard Document, Issue 1 Rev 2.
  7. Ghassemi, F., Jakeman,A.J,, and Nix, H.A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies. Sydney, N.S.W. : NSW University Press. 
  8. Gupta, R.P., Tiwari, R.K., Saini, V., Srivastava, N. (2013). A Simplified Approach for Interpreting Principal Component. Advances in Remote Sensing Images, 2:111-119. http://dx.doi.org/10.4236/ars.2013.22015.
  9. Huete, A., Justice, C., van Leeuwen, W. (1999). MODIS vegetation index (MOD13) algorithm theoretical basis document, Ver. 3.
  10. Iqbal, F. (2011). Detection of salt affected soil in rice-wheat area using satellite image African. Journal of Agricultural Research, 6(21): 4973-4982. DOI: 10.5897/AJAR11.634.
  11. Krenker, A., Bester, J., Kos, A. (2011). Introduction to the Artificial Neural Networks, Artificial Neural Networks - Mehodological Advances and Biomedical Applications,. [Kenji Suzuki (Ed.)] Available from: http://www.intechopen.com/books/artificial-    neural-networksmethodological-advances-and-biomedical-applications/introduction-to-the-artificial-neural-networks.
  12. Lakhankar, T., Ghedira, H., Temimi, M., Sengupta, M., Khanbilvardi, R., Blake, R.(2009). Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data, Remote Sensing, 1:3-21. doi:10.3390/rs1010003
  13. Metternicht, G.I. (1998). Fuzzy classification of JERS-1 SAR data: an evaluation of its performance for soil salinity mapping. Ecological Modelling, 111:61–74
  14. Metternicht, G.I., Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints, Review article. Remote Sensing of Environment, 85:1-20.
  15. Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva J, Ribeiro S. (2017). Evaluation of various interpolation techniques for estimation of selected soil properties. International Journal of GEOMATE, 13(38): 23-30.
  16. Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva, J., Berg, A. (2016). Integrating microwave and optical data for monitoring soil moisture. in Proc. ‘Living Planet Symposium 2016’, Prague, Czech Republic, 9–13 May 2016 (ESA SP-740, August 2016)
  17. Mróz, M. and Sobieraj, A. (2004). Comparison of several vegetation indices calculated on the basis of a seasonal SPOT XS time series, and their suitability for land cover and agricultural crop identification. Technical Sciences, 7:40-66.
  18. Munyati, C. (2004). Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats, Zambia, Geocarto International, 19 (3):11-22.
  19. Rock, B.N., Vogelmannm J.E., Williams, D.L., Vogelmann, A.F., Hoshizaki, T. (1986). Remote detection of forest damage. Bioscience, 36: 439-445.
  20. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.W., Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation. NASA/GSFCT Type III Final Report, Greenbelt, MD, USA. 
  21. Santiesteban, I.A.(2003). Multi-sensor image fusion applied to morphodynamics of coastal environments in Belize. Master of Science Thesis in Geo-information Science and Earth Observation, International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands.
  22. Sheldon, A., Menzies, N.W., Bing, S. H., Dalal R. (2004). The effect of salinity on plant available water. SuperSoil 2004: 3rd Australian New Zealand Soils Conference, 5 – 9 December 2004, University of Sydney, Australia. http://www.regional.org.au/au/asssi/
  23. Soil Survey Division Staff (1993). Soil survey manual. Soil Conservation Service. U.S. Department of Agriculture Handbook 18.
  24. Vajsová, B., Aastrand, P.J. (2015). New sensors benchmark report on Sentinel -2A. EUR 27674 EN. doi: 10.2788/544302. 
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Indian Journal of Agricultural Research
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    Research Article

    volume 52 issue 5 (october 2018) : 524-529,   Doi: 10.18805/IJARe.A-316

    Soil salinity mapping utilizing sentinel-2 and neural networks

    R
    R.S. Morgan
    M
    M. Abd El-Hady
    I
    I.S. Rahim
    1Soil and Water Use deptartment, Agricultural and Biological Research Division, National Research Centre, El Behouth St., Dokki, Cairo, Egypt.
    Cite article:- Morgan R.S., El-Hady Abd M., Rahim I.S. (2018). Soil salinity mapping utilizing sentinel-2 and neural networks. Indian Journal of Agricultural Research. 52(5): 524-529. doi: 10.18805/IJARe.A-316.

    ABSTRACT

    Soil salinity is the most important soil property that affects the agriculture productivity. Periodical monitoring of its status is considered a crucial factor in the selection of appropriate agricultural practices to attain a sustainable production. The availability of remote sensing data processed by a somewhat novel method such as artificial neural networks (ANN) offer a potential solution that could easily and affordably replace the in-site monitoring methods. The aim of this work is to use high spectral resolution Sentinel-2 (S2) data for soil salinity prediction utilizing neural networks. The study evaluated three approaches in processing the S2 data for inclusion in the artificial neural network for soil salinity prediction. These approaches included S2 spectral reflectance data, spectral indices and principal component analysis (PCA) of the S2 data. The results revealed that a combination of these approaches including the reflectance data of band 11(shortwave infrared band) of S2, the normalized differential vegetation index (NDVI) and the second PCA (dominated by the near infrared band) gave the best performance when used as input when designing the artificial neural networks to predict the soil salinity. The overall accuracy of this approach has a coefficient of determination (R2) of 0.94 between the actual and predicted soil salinity.

    REFERENCES

    1. Abbas, A. and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. Proceedings of the International Congress on Modelling and Simulation (MODSIM ’07), Oxley L. and Kulasiri D., (Eds)., Modelling and Simulation Society of Australia and New Zealand, Brighton, UK. pp. 2632–2638,
    2. Al-Khaier, F. (2003). Soil salinity detection using satellite remotes sensing. Master of Science in Geo-information Science and Earth Observation, International Institute for Geo-Information Science and Earth Observation, Enscheda, The Netherlands. 
    3. Azabdaftari, A. and Sunar, F. (2016). Soil Salinity Mapping using Multitemporal LANDSAT Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
    4. Danson, F.M. and Bowyer, P. (2004). Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment. 92: 309-321.
    5. Erzin, Y., Rao, B.H., Patel, A., Gumaste, S.D. and Singh, D.N. (2010) . Artificial Neural Network Models for Predicting Electrical Resistivity of Soils from Their Thermal Resistivity. International Journal of Thermal Sciences, 49:118-130.
    6. ESA(2015). Sentinel-2 User Handbook. ESA Standard Document, Issue 1 Rev 2.
    7. Ghassemi, F., Jakeman,A.J,, and Nix, H.A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies. Sydney, N.S.W. : NSW University Press. 
    8. Gupta, R.P., Tiwari, R.K., Saini, V., Srivastava, N. (2013). A Simplified Approach for Interpreting Principal Component. Advances in Remote Sensing Images, 2:111-119. http://dx.doi.org/10.4236/ars.2013.22015.
    9. Huete, A., Justice, C., van Leeuwen, W. (1999). MODIS vegetation index (MOD13) algorithm theoretical basis document, Ver. 3.
    10. Iqbal, F. (2011). Detection of salt affected soil in rice-wheat area using satellite image African. Journal of Agricultural Research, 6(21): 4973-4982. DOI: 10.5897/AJAR11.634.
    11. Krenker, A., Bester, J., Kos, A. (2011). Introduction to the Artificial Neural Networks, Artificial Neural Networks - Mehodological Advances and Biomedical Applications,. [Kenji Suzuki (Ed.)] Available from: http://www.intechopen.com/books/artificial-    neural-networksmethodological-advances-and-biomedical-applications/introduction-to-the-artificial-neural-networks.
    12. Lakhankar, T., Ghedira, H., Temimi, M., Sengupta, M., Khanbilvardi, R., Blake, R.(2009). Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data, Remote Sensing, 1:3-21. doi:10.3390/rs1010003
    13. Metternicht, G.I. (1998). Fuzzy classification of JERS-1 SAR data: an evaluation of its performance for soil salinity mapping. Ecological Modelling, 111:61–74
    14. Metternicht, G.I., Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints, Review article. Remote Sensing of Environment, 85:1-20.
    15. Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva J, Ribeiro S. (2017). Evaluation of various interpolation techniques for estimation of selected soil properties. International Journal of GEOMATE, 13(38): 23-30.
    16. Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva, J., Berg, A. (2016). Integrating microwave and optical data for monitoring soil moisture. in Proc. ‘Living Planet Symposium 2016’, Prague, Czech Republic, 9–13 May 2016 (ESA SP-740, August 2016)
    17. Mróz, M. and Sobieraj, A. (2004). Comparison of several vegetation indices calculated on the basis of a seasonal SPOT XS time series, and their suitability for land cover and agricultural crop identification. Technical Sciences, 7:40-66.
    18. Munyati, C. (2004). Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats, Zambia, Geocarto International, 19 (3):11-22.
    19. Rock, B.N., Vogelmannm J.E., Williams, D.L., Vogelmann, A.F., Hoshizaki, T. (1986). Remote detection of forest damage. Bioscience, 36: 439-445.
    20. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.W., Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation. NASA/GSFCT Type III Final Report, Greenbelt, MD, USA. 
    21. Santiesteban, I.A.(2003). Multi-sensor image fusion applied to morphodynamics of coastal environments in Belize. Master of Science Thesis in Geo-information Science and Earth Observation, International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands.
    22. Sheldon, A., Menzies, N.W., Bing, S. H., Dalal R. (2004). The effect of salinity on plant available water. SuperSoil 2004: 3rd Australian New Zealand Soils Conference, 5 – 9 December 2004, University of Sydney, Australia. http://www.regional.org.au/au/asssi/
    23. Soil Survey Division Staff (1993). Soil survey manual. Soil Conservation Service. U.S. Department of Agriculture Handbook 18.
    24. Vajsová, B., Aastrand, P.J. (2015). New sensors benchmark report on Sentinel -2A. EUR 27674 EN. doi: 10.2788/544302. 
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