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Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model by Justin George Kalambukattu; Suresh Kumar; R. Arya Raj is a Environmental Science article available to read on EtoBox.

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Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0-15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Lev

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Author

Justin George Kalambukattu; Suresh Kumar; R. Arya Raj

Publisher

Springer Science and Business Media LLC

Published

2018

Language

EN

Field

Environmental Science (Physical Sciences)