利用遥感衍生物和预测模型对土壤盐碱化进行时空评估:对可持续发展的影响

IF 8.5 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Prashant Kumar , Prasoon Tiwari , Arkoprovo Biswas , Prashant Kumar Srivastava
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引用次数: 0

摘要

本研究旨在综合利用多传感器数据集(Landsat 4-5 & 8 OLI 卫星图像,空间分辨率 = 30 米)和实地研究,评估印度西孟加拉邦沿海地带土壤盐碱化的时空动态。这项研究通过绘制饱和提取物的盐度和导电率(ECe)图,并利用光谱特征来估算土壤盐度,从而对土壤盐碱化进行评估。对 SI 变化(%)进行了分析(2021-1995 年),将盐度水平的增加分为 5%、10% 和 50%,这可能是由于土壤层上的盐分结壳造成的。土地利用土地覆被变化图(2021-1995 年)显示,研究区域的城市化进程在不断发展。此外,研究区域的土壤盐度介于 0.03 ppt 至 3.87 ppt 之间,ECe 介于 0.35 dSm-1 至 52.85 dSm-1 之间。此外,还进一步确定了易受盐碱土壤影响的地点。根据导电率对土壤盐度进行分类后发现,26% 的样本属于非盐碱类,其余属于盐碱类。从 FieldSpec 手动光谱仪获取的土壤样本(n = 19)的光谱特征显示,在 1400、1900 和 2250 纳米附近有明显的吸收特征,表明存在盐矿物。利用 X 射线荧光和扫描电子显微镜对反射光谱的结果进行了交叉验证。本研究还采用偏最小二乘回归(PLSR)方法预测了ECe(r2 = 0.79,RMSE = 3.29)和盐度参数(r2 = 0.75,RMSE = 0.51),表明偏最小二乘回归方法适用于全球受盐影响土壤的监测。这项研究的结论强调,遥感数据和多元分析是绘制空间变化图和预测土壤盐度的重要工具。研究还得出结论,用于灌溉和水产养殖活动的含盐地下水会加剧土壤盐碱化。这项研究将有助于决策者/农民更有效地识别盐分退化问题,并立即采取缓解措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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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来源期刊
Geoscience frontiers
Geoscience frontiers Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
17.80
自引率
3.40%
发文量
147
审稿时长
35 days
期刊介绍: Geoscience Frontiers (GSF) is the Journal of China University of Geosciences (Beijing) and Peking University. It publishes peer-reviewed research articles and reviews in interdisciplinary fields of Earth and Planetary Sciences. GSF covers various research areas including petrology and geochemistry, lithospheric architecture and mantle dynamics, global tectonics, economic geology and fuel exploration, geophysics, stratigraphy and paleontology, environmental and engineering geology, astrogeology, and the nexus of resources-energy-emissions-climate under Sustainable Development Goals. The journal aims to bridge innovative, provocative, and challenging concepts and models in these fields, providing insights on correlations and evolution.
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