利用基于卫星的光谱和热数据对浅层地下水位深度进行建模和数字测绘:浅层地下水位数字测绘框架介绍

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE Soil & Tillage Research Pub Date : 2024-10-07 DOI:10.1016/j.still.2024.106317
Mehrdad Jeihouni , Khalil Valizadeh Kamran , Lutfiye Kusak
{"title":"利用基于卫星的光谱和热数据对浅层地下水位深度进行建模和数字测绘:浅层地下水位数字测绘框架介绍","authors":"Mehrdad Jeihouni ,&nbsp;Khalil Valizadeh Kamran ,&nbsp;Lutfiye Kusak","doi":"10.1016/j.still.2024.106317","DOIUrl":null,"url":null,"abstract":"<div><div>Shallow groundwater is a key variable of the hydrological cycle and has significant impacts on the components of energy, carbon, and water balances. Moreover, shallow saline groundwater plays a critical role in secondary soil salinization. Therefore, comprehensive information on spatial distribution of shallow water table depth is fundamental for effective land management and sustainable development. But determining it by conventional methods is time-consuming and financially costly in large areas. Shallow groundwater naturally has signatures at the land surface, and it can be parameterized by properties inferred from satellite-based surface data. Against this background, this study is to introduce a novel approach and framework for Digital Shallow Water Table Mapping (DSWTM). The efficiency and performance of the proposed DSWTM was assessed by different covariate sets and employing different predictive models. In the DSWTM framework, remote sensing spectral/thermal indices, geographic and trend data were used as covariates and the PLSR, M5, Cubist, and RF algorithms were employed as predictive models under four scenarios. For two high-performance models in each scenario, the water table depth maps were generated, and associated uncertainties were quantified using the bootstrapping technique at a spatial resolution of 30 m. The results revealed that the prediction accuracies of each predictive model were constantly increasing from the first to the fourth scenario. Moreover, the Cubist and RF models had higher performance than PLSR and M5 in all scenarios. The uncertainties’ of prediction maps generated by Cubist and RF models were decreased from the first to the fourth scenarios. The RF generated maps in all scenarios had the lowest uncertainty and provided accurate prediction maps compared to Cubist. The RF as a predictive model showed the highest ability and is recommended to use in DSWTM studies. The presented DSWTM framework opened a new research window for accurate shallow water table mapping.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"245 ","pages":"Article 106317"},"PeriodicalIF":6.1000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling and digital mapping of shallow water table depth using satellite-based spectral and thermal data: Introducing a framework for digital shallow water table mapping\",\"authors\":\"Mehrdad Jeihouni ,&nbsp;Khalil Valizadeh Kamran ,&nbsp;Lutfiye Kusak\",\"doi\":\"10.1016/j.still.2024.106317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Shallow groundwater is a key variable of the hydrological cycle and has significant impacts on the components of energy, carbon, and water balances. Moreover, shallow saline groundwater plays a critical role in secondary soil salinization. Therefore, comprehensive information on spatial distribution of shallow water table depth is fundamental for effective land management and sustainable development. But determining it by conventional methods is time-consuming and financially costly in large areas. Shallow groundwater naturally has signatures at the land surface, and it can be parameterized by properties inferred from satellite-based surface data. Against this background, this study is to introduce a novel approach and framework for Digital Shallow Water Table Mapping (DSWTM). The efficiency and performance of the proposed DSWTM was assessed by different covariate sets and employing different predictive models. In the DSWTM framework, remote sensing spectral/thermal indices, geographic and trend data were used as covariates and the PLSR, M5, Cubist, and RF algorithms were employed as predictive models under four scenarios. For two high-performance models in each scenario, the water table depth maps were generated, and associated uncertainties were quantified using the bootstrapping technique at a spatial resolution of 30 m. The results revealed that the prediction accuracies of each predictive model were constantly increasing from the first to the fourth scenario. Moreover, the Cubist and RF models had higher performance than PLSR and M5 in all scenarios. The uncertainties’ of prediction maps generated by Cubist and RF models were decreased from the first to the fourth scenarios. The RF generated maps in all scenarios had the lowest uncertainty and provided accurate prediction maps compared to Cubist. The RF as a predictive model showed the highest ability and is recommended to use in DSWTM studies. The presented DSWTM framework opened a new research window for accurate shallow water table mapping.</div></div>\",\"PeriodicalId\":49503,\"journal\":{\"name\":\"Soil & Tillage Research\",\"volume\":\"245 \",\"pages\":\"Article 106317\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soil & Tillage Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167198724003180\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOIL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil & Tillage Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167198724003180","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

浅层地下水是水文循环中的一个关键变量,对能量、碳和水的平衡有重大影响。此外,浅层含盐地下水在土壤次生盐碱化中起着至关重要的作用。因此,有关浅层地下水位深度空间分布的综合信息对于有效的土地管理和可持续发展至关重要。但在大面积地区,用传统方法确定浅层地下水位既费时又费钱。浅层地下水天然具有地表特征,可通过卫星地表数据推断出其属性参数。在此背景下,本研究提出了一种新的数字浅层地下水位测绘(DSWTM)方法和框架。通过不同的协变量集和采用不同的预测模型,对所提出的 DSWTM 的效率和性能进行了评估。在 DSWTM 框架中,遥感光谱/热指数、地理和趋势数据被用作协变量,PLSR、M5、Cubist 和 RF 算法被用作四种情况下的预测模型。针对每个方案中的两个高性能模型,生成了地下水位深度图,并在空间分辨率为 30 米的条件下使用引导技术对相关的不确定性进行了量化。结果表明,从第一种情况到第四种情况,每种预测模型的预测精度都在不断提高。此外,在所有情况下,Cubist 和 RF 模型的性能均高于 PLSR 和 M5。从第一个场景到第四个场景,Cubist 和 RF 模型生成的预测图的不确定性都有所下降。与 Cubist 相比,RF 在所有场景中生成的地图的不确定性最低,并能提供准确的预测地图。射频作为一种预测模型显示出最高的能力,建议在 DSWTM 研究中使用。所提出的 DSWTM 框架为准确绘制浅层地下水位图打开了一扇新的研究之窗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modeling and digital mapping of shallow water table depth using satellite-based spectral and thermal data: Introducing a framework for digital shallow water table mapping
Shallow groundwater is a key variable of the hydrological cycle and has significant impacts on the components of energy, carbon, and water balances. Moreover, shallow saline groundwater plays a critical role in secondary soil salinization. Therefore, comprehensive information on spatial distribution of shallow water table depth is fundamental for effective land management and sustainable development. But determining it by conventional methods is time-consuming and financially costly in large areas. Shallow groundwater naturally has signatures at the land surface, and it can be parameterized by properties inferred from satellite-based surface data. Against this background, this study is to introduce a novel approach and framework for Digital Shallow Water Table Mapping (DSWTM). The efficiency and performance of the proposed DSWTM was assessed by different covariate sets and employing different predictive models. In the DSWTM framework, remote sensing spectral/thermal indices, geographic and trend data were used as covariates and the PLSR, M5, Cubist, and RF algorithms were employed as predictive models under four scenarios. For two high-performance models in each scenario, the water table depth maps were generated, and associated uncertainties were quantified using the bootstrapping technique at a spatial resolution of 30 m. The results revealed that the prediction accuracies of each predictive model were constantly increasing from the first to the fourth scenario. Moreover, the Cubist and RF models had higher performance than PLSR and M5 in all scenarios. The uncertainties’ of prediction maps generated by Cubist and RF models were decreased from the first to the fourth scenarios. The RF generated maps in all scenarios had the lowest uncertainty and provided accurate prediction maps compared to Cubist. The RF as a predictive model showed the highest ability and is recommended to use in DSWTM studies. The presented DSWTM framework opened a new research window for accurate shallow water table mapping.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
期刊最新文献
Drivers of soil quality and maize yield under long-term tillage and straw incorporation in Mollisols Improving the accuracy of soil organic matter mapping in typical Planosol areas based on prior knowledge and probability hybrid model Straw incorporating in shallow soil layer improves field productivity by impacting soil hydrothermal conditions and maize reproductive allocation in semiarid east African Plateau Significant increases in nitrous oxide emissions under simulated extreme rainfall events and straw amendments from agricultural soil Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1