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

土壤盐分 盐度 环境科学 遥感 土工试验 土壤科学 偏最小二乘回归 土壤水分 地质学 海洋学 数学 统计
作者
Prashant Kumar,Prasoon Tiwari,Arkoprovo Biswas,Prashant K. Srivastava
出处
期刊:Geoscience frontiers [Elsevier BV]
卷期号:15 (6): 101881-101881 被引量:3
标识
DOI:10.1016/j.gsf.2024.101881
摘要

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 (ECe) 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 ECe 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 ECe 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 ECe (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.

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