Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development

Abstract

This study aims to investigate the combined use of multi-sensor datasets (Landsat 4–5 & 8 OLI satellite imagery, spatial resolution = 30 m) coupled with field studies to evaluate spatio-temporal dynamics of soil salinization along the coastal belt in West Bengal, India. This study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract (EC_e) and utilizing spectral signatures for estimating soil salinity. The SI change (%) was analyzed (2021–1995), categorizing increases in salinity levels into 5%, 10%, and 50% changes possibly due to salt encrustation on the soil layers. The land use land cover (LULC) change map (2021–1995) demonstrates that the study area is continuously evolving in terms of urbanization. Moreover, in the study area, soil salinity ranges from 0.03 ppt to 3.87 ppt, and EC_e varies from 0.35 dSm⁻¹ to 52.85 dSm⁻¹. Additionally, vulnerable saline soil locations were further identified. Classification of soil salinity based on EC_e reveals that 26% of samples fall into the non-saline category, while the rest belong to the saline category. The Spectral signatures of the soil samples (n = 19) acquired from FieldSpec hand spectrometer show significant absorption features around 1400, 1900, and 2250 nm and indicate salt minerals. The results of reflectance spectroscopy were cross-validated using X-ray fluorescence and scanning electron microscopy. This study also employed partial least square regression (PLSR) approach to predict EC_e (r² = 0.79, RMSE = 3.29) and salinity parameters (r² = 0.75, RMSE = 0.51), suggesting PLSR applicability in monitoring salt-affected soils globally. This study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil salinity. It has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil salinization. The study will help policymakers/farmers identify the salt degradation problem more effectively and adopt immediate mitigation measures.

Keywords

Hyperspectral remote sensing / Scanning electron microscopy / X-ray fluorescence / Partial least square regression / Soil salinity / Salinity index

CRediT authorship contribution statement

Prashant Kumar: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Prasoon Tiwari: Writing – review & editing, Validation, Software, Investigation, Formal analysis. Arkoprovo Biswas: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition. Prashant Kumar Srivastava: Validation, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank the Editorial Advisor Prof. M. Santosh, Associated Editor Prof. E. Shaji, and two anonymous reviewers for their insightful comments, which helped us improve the manuscript's quality. This work is a part of the Ph.D thesis of the first author (PK). PK thanks Banaras Hindu University (BHU) for providing the University Fellowship (R/Dev/Sch/UGC Research Fellow/2020-21/18340, and Credit Incentive to Research Scholars. PK expresses gratitude to Dr. Abhinav Yadav (IESD, BHU) for his assistance during the laboratory procedures for soil analysis. PK acknowledges the XRF facility at the Sophisticated Analytical & Technical Help Institute (SATHI) at central discovery centre, BHU, and Scanning Electron Microscopy Laboratory, Geology, BHU, for instrumental support. AB acknowledges the University Grant Commission for funding this work as a start-up Research Grant (No. F.30-431/2018-BSR). AB would also like to thank Banaras Hindu University for utilizing partial funds from the Bridge Grant (Development Scheme number 6031-A) under the Institution of Eminence (IoE) program to the University.

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