Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: S
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ORIGINAL ARTICLE
Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: Sharif Abad Plain, Central Iran Vahid Habibi1 · Hasan Ahmadi2 · Mohammad Jafari2 · Abolfazl Moeini1 Received: 20 July 2020 / Accepted: 17 October 2020 © Springer Nature Switzerland AG 2020
Abstract Land salinization is one of the most important factors in reducing the soil quality of agricultural land. Accordingly, these regions affected agricultural production and ecological development. Therefore, it is important to assess soil salinity driving factors. However, it is difficult to characterize soil salinity using single-factor and linear models. This research was carried out for soil digital mapping using remote sensing to classify saline lands and their spatial distribution in SharifAbad plain. The methodology is based on the differentiation of saline soils by combining the Landsat 8 data, fieldwork, and neural network model for prediction of soil salinity. The Latin hypercube method is based on the stratified sampling method. Based on this technique, 63 samples were selected from 0 to 30 cm of the soil surface. In the ANN model, considered 30% of the soil EC as the validation set and the rest (70%) for the testing set. To model soil salinity, auxiliary variables such as Landsat 8 satellite image of 2016 including 2–5 main bands and band 7, topographic auxiliary data includes DEM, TWI, TCI, and spectral parameters were extracted. The result revealed that the GFF algorithm which, according to R2 and MSE statistics, the best way to prepare a soil salinity map in Sharif Abad plain. The ANN model in most cases satisfies the EC amount less than the real value. We found that the average values of SI5, TCI, and TWI values in the non-saline, the class were lower than the saline class, and the average values of DEM and NDVI indices in the non-saline class were higher than the saline class and showed a statistically significant difference. Keywords Artificial neural network · Salinization · Hazard · Landsat 8 · Latin hypercube
Introduction The health and food security of a society is directly dependent on agricultural production, and any disturbance in the production process of this sector can directly threaten the community food security. Soil salinity, as a limiting factor, directly affects plant growth (Piekut et al. 2018; El-Hamid and Hong 2020). Due to soil salinity which accumulates soluble salts within the range of root development or soil profile and the ecological potential and biomass production will be reduced, * Mohammad Jafari [email protected] 1
Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
Faculty of Natural Resource, University of Tehran, Karaj, Iran
2
therefore, soil cannot support forests, pastures, rangelands, and eventually deserts expand (Abbas et al. 2013; Alipur et al. 2016; Al-Jubouri and Wheib 2020; Mirzaee et al. 2020). Today, soil salinity along with other natural disasters, su
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