一种估算土壤承载力的遥感方法

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2024-03-01 DOI:10.1016/j.inpa.2022.10.002
Italo Rômulo Mendes de Souza , Edson Eyji Sano , Renato Paiva de Lima , Anderson Rodrigo da Silva
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引用次数: 0

摘要

土壤的预固结压力(σP)可被视为承载能力(LBC)的指标,即压实前可承受的表面压力,通常由农业机械的运输造成。在这项开创性的研究中,采用了一种遥感方法,通过巴西巴伊亚州 "Rio Preto "水文流域土壤的σP来估算LBC,时间跨度为2016年至2019年每月一次。传统上,σP 是通过费时费力的实验室分析来测量的,因此无法绘制大面积地图。这项工作的创新方法包括将土壤水分的主动-被动卫星数据与粘土含量和水垫面势的植被转移函数相结合,以获得σP的地理定位估算值。对不同土壤用途、土地覆被和坡度等级下的估算值进行了分析;为每个等级的 LBC 平均值时间序列建立了 95% 的置信区间。在有一年生作物、草地和自然植被的地区,土地覆被估算值的总体季节变化相似,而平坦地区受土壤水分全年(季节间)变化的影响较小。一般来说,平坦地区的土地覆被率每年下降约 0.5%。因此,这些地区需要引起注意,因为它们占盆地面积的 86%,而且主要受到农业土壤管理和重型机械的地表压力的影响。
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A remote sensing approach to estimate the load bearing capacity of soil

Preconsolidation pressure (σP) 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 σP from soils of the “Rio Preto” Hydrographic Basin, Bahia State, Brazil, in a monthly time lapse from 2016 to 2019. Traditionally, σP 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 σP. 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.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
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
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