Italo Rômulo Mendes de Souza , Edson Eyji Sano , Renato Paiva de Lima , Anderson Rodrigo da Silva
{"title":"一种估算土壤承载力的遥感方法","authors":"Italo Rômulo Mendes de Souza , Edson Eyji Sano , Renato Paiva de Lima , Anderson Rodrigo da Silva","doi":"10.1016/j.inpa.2022.10.002","DOIUrl":null,"url":null,"abstract":"<div><p>Preconsolidation pressure (<span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span>) of soil can be considered as an indicator of the Load Bearing Capacity (LBC), which is the tolerated surface pressure before compaction, often caused by the traffic of agricultural machinery. In this pioneering study, a remote sensing approach was introduced to estimate LBC through <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span> from soils of the “Rio Preto” Hydrographic Basin, Bahia State, Brazil, in a monthly time lapse from 2016 to 2019. Traditionally, <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span> is measured by a laborious and time demanding laboratory analysis, making it unfeasible to map large areas. The innovative methodology of this work consists of combining active–passive satellite data on soil moisture and pedotransfer functions of clay content and water matric potential to obtain geo-located estimates of <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span>. Estimates were analysed under different classes of soil use, land cover and slope; 95% confidence intervals were built for the time series of mean values of LBC for each class. The overall seasonal variation in LBC estimates is similar in areas with annual crops, grasslands and natural vegetation, and flat areas are less affected by soil moisture variations over the year (between seasons). LBC decreased, in general, at about 0.5% a year in flat areas. Therefore, these areas demand attention, since they occupy 86% of the Basin and are mostly subjected to agricultural soil management and surface pressure by heavy machinery.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 109-116"},"PeriodicalIF":7.7000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000828/pdfft?md5=e916dbebf760cf045391dcc6229a89a1&pid=1-s2.0-S2214317322000828-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A remote sensing approach to estimate the load bearing capacity of soil\",\"authors\":\"Italo Rômulo Mendes de Souza , Edson Eyji Sano , Renato Paiva de Lima , Anderson Rodrigo da Silva\",\"doi\":\"10.1016/j.inpa.2022.10.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Preconsolidation pressure (<span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span>) of soil can be considered as an indicator of the Load Bearing Capacity (LBC), which is the tolerated surface pressure before compaction, often caused by the traffic of agricultural machinery. In this pioneering study, a remote sensing approach was introduced to estimate LBC through <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span> from soils of the “Rio Preto” Hydrographic Basin, Bahia State, Brazil, in a monthly time lapse from 2016 to 2019. Traditionally, <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span> is measured by a laborious and time demanding laboratory analysis, making it unfeasible to map large areas. The innovative methodology of this work consists of combining active–passive satellite data on soil moisture and pedotransfer functions of clay content and water matric potential to obtain geo-located estimates of <span><math><msub><mi>σ</mi><mi>P</mi></msub></math></span>. Estimates were analysed under different classes of soil use, land cover and slope; 95% confidence intervals were built for the time series of mean values of LBC for each class. The overall seasonal variation in LBC estimates is similar in areas with annual crops, grasslands and natural vegetation, and flat areas are less affected by soil moisture variations over the year (between seasons). LBC decreased, in general, at about 0.5% a year in flat areas. Therefore, these areas demand attention, since they occupy 86% of the Basin and are mostly subjected to agricultural soil management and surface pressure by heavy machinery.</p></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"11 1\",\"pages\":\"Pages 109-116\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214317322000828/pdfft?md5=e916dbebf760cf045391dcc6229a89a1&pid=1-s2.0-S2214317322000828-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317322000828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317322000828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
A remote sensing approach to estimate the load bearing capacity of soil
Preconsolidation pressure () of soil can be considered as an indicator of the Load Bearing Capacity (LBC), which is the tolerated surface pressure before compaction, often caused by the traffic of agricultural machinery. In this pioneering study, a remote sensing approach was introduced to estimate LBC through from soils of the “Rio Preto” Hydrographic Basin, Bahia State, Brazil, in a monthly time lapse from 2016 to 2019. Traditionally, is measured by a laborious and time demanding laboratory analysis, making it unfeasible to map large areas. The innovative methodology of this work consists of combining active–passive satellite data on soil moisture and pedotransfer functions of clay content and water matric potential to obtain geo-located estimates of . Estimates were analysed under different classes of soil use, land cover and slope; 95% confidence intervals were built for the time series of mean values of LBC for each class. The overall seasonal variation in LBC estimates is similar in areas with annual crops, grasslands and natural vegetation, and flat areas are less affected by soil moisture variations over the year (between seasons). LBC decreased, in general, at about 0.5% a year in flat areas. Therefore, these areas demand attention, since they occupy 86% of the Basin and are mostly subjected to agricultural soil management and surface pressure by heavy machinery.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining