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Developing geographic weighted regression (GWR) technique for monitoring soil salinity using sentinel-2 multispectral imagery by Mohammad Mahdi Taghadosi; Mahdi Hasanlou is a Environmental Science article available to read on EtoBox.

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Soil salinity is a widespread natural hazard that negatively influences soil fertility and crop productivity. Using the potential of earth observation data and remote sensing technologies provides an opportunity to address this environmental issue and makes it possible to identify salt-affected regions accurately. While most of the utilized methods and model development techniques for monitoring soil salinity to date have been globally considered and tried to detect salinity and create predictive maps with a single regression algorithm, fewer studies have investigated the potential of local models and weighted regression techniques for estimating soil salinity. Accordingly, this research deals with monitoring surface soil salinity by the potential of Sentinel-2 multispectral imagery using the geographic weighted regression (GWR) technique. The field study was conducted in an area that has suffered from salinization, and the salinity of several soil samples was measured to be used as a source of ground truth data. The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used

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Author

Mohammad Mahdi Taghadosi; Mahdi Hasanlou

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)