Early Detection of Soil Salinization by Means of Spaceborne Hyperspectral Imagery

Soil salinization is increasingly affecting agricultural areas worldwide, reducing soil quality and crop yields. Surface salinization evidences present complex spectral features, increasing in depth with increasing salt concentrations. For this reason, low salinization detection provides a complex challenge to test the capabilities of new-generation hyperspectral satellites. The aim of this study is to test the capability of the new generation of hyperspectral satellites (EnMAP) in detecting early stages and low levels of topsoil salinization and to investigate the differences between laboratory and image spectra to take into account their influence on model performance. The area of study, the Grosseto plain, located in central Italy, presented heterogeneous salinity levels (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>C</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>= 11.7 dS/m, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>C</mi><mrow><mi>m</mi><mi>e</mi><mi>a</mi><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula>= 0.99 dS/m). We investigated the salt-affected soil spectral behaviour with both laboratory-acquired spectra (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mrow><mi>o</mi><mi>b</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula>= 60) and EnMAP-derived spectra (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>n</mi><mrow><mi>o</mi><mi>b</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula>= 20). Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. Two-dimensional correlograms resulted in an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.88 for laboratory data and 0.63 for EnMAP data. PLSR proved to have the worst performance. The Random Forest regressor proved its capability in detecting complex spectral features, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> scores of 0.72 for laboratory data and 0.60 for EnMAP. The Random Forest model provides very satisfactory mapping capabilities when tested on the whole study area. The results highlight that the EnMAP-derived dataset produces similar results to those of ASD laboratory spectra, providing evidences regarding EnMAP’s predictive capability to detect early stages of topsoil salinization.

Abstract licence: CC BY

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