检索结果-南通市图书馆

Model-Based Integrated Methods for Quantitative Estimation of Soil salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

引用

Pedosphere 2012年第5期22卷 640-649页

作者： Z. E. MASHIMBYE M. A. CHO J. P. NELL W. P. DE CLERCQ A. VAN NIEKERK D. P. TURNER Department of Soil Science Stellenbosch University Private Bag X1 Matieland 7602 (South Africa) Agricultural Research Council-Institute for Soil Climate and Water Private Bag X79 Pretoria 0001 (South Africa) Department of Geography and Environmental Studies Stellenbosch University Private Bag X1 Matieland 7602 (South Africa) Council for Scientific and Industrial Research Natural Resources and the Environment P.O. Box 395 Pretoria 0001 (South Africa) School of Environmental Science University of Kwazulu-Natal Westville Campus Westville 3630 (South Africa)

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.

关键词： electrical conductivity land degradation partial least squares regression salinity index spectral reflectance

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

Geoscience Frontiers 2024年第6期15卷 22-36页

作者： Prashant Kumar Prasoon Tiwari Arkoprovo Biswas Prashant Kumar Srivastava Department of Geology Centre of Advanced StudyInstitute of ScienceBanaras Hindu UniversityVaranasi 221005U.P.India Institute of Environment and Sustainable Development Banaras Hindu UniversityVaranasi 221005U.P.India

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,*** study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract(ECe)and utilizing spectral signatures for estimating soil *** 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 *** land use land cover(LULC)change map(2021–1995)demonstrates that the study area is continuously evolving in terms of ***,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 *** of soil salinity based on ECe reveals that 26%of samples fall into the nonsaline category,while the rest belong to the saline *** 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 *** results of reflectance spectroscopy were crossvalidated using X-ray fluorescence and scanning electron *** study also employed partial least square regression(PLSR)approach to predict ECe(r^(2)=0.79,RMSE=3.29)and salinity parameters(r^(2)=0.75,RMSE=0.51),suggesting PLSR applicability in monitoring salt-affected soils *** study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil *** has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil *** study will help policymakers/farmers identify the salt degradation problem more effectivel

关键词： Hyperspectral remote sensing Scanning electron microscopy X-ray fluorescence Partial least square regression Soil salinity salinity index

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：