Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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Indian Journal of Agricultural Research, volume 52 issue 5 (october 2018) : 524-529

Soil salinity mapping utilizing sentinel-2 and neural networks

R.S. Morgan, M. Abd El-Hady, 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.
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.
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