张甘霖
, 朱阿兴
ZHANG Ganlin, ZHU A-Xing
通讯作者:
收稿日期: 2018-01-15
修回日期: 2018-01-20
网络出版日期: 2018-01-28
版权声明: 2018 地理科学进展 《地理科学进展》杂志 版权所有
基金资助:
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摘要
作为土壤学和地理学学科的分支,土壤地理学是地球表层系统科学的重要组成部分,其核心研究内容是土壤的时空变化。土壤地理学研究对象从传统的土体向地球表层系统视角下的关键带转变,研究方法上全面走向数字化。本文综述了近20年来土壤地理学分支学科包括土壤发生、土壤形态、土壤分类、土壤调查与数字土壤制图等领域的研究进展,指出其发展趋势为：基础理论研究不断拓展、调查技术正经历变革、时空演变从过程观测走向模拟,同时探讨了土壤地理学的未来发展契机与面临的挑战。
关键词：
Abstract
Soil geography is the sub-discipline of soil science and geography dealing with the spatiotemporal changes of soil, and is a part of the earth surface system science. The research topic of soil geography is gradually changing from soil body to critical zone from the perspective of the earth surface system, meanwhile the research methodology develops toward "digital". Based on an introduction of the theoretical and technical backgrounds, this article reviewed the recent progress of soil geography including on soil genesis, soil morphology, soil classification, soil survey, and digital soil mapping. Future development of soil geography needs to expand the theoretical research, innovate the investigation technology, and simulate the spatiotemporal variations of soil. Furthermore, the main opportunities, trends, and challenges in the future were discussed.
Keywords：
土壤地理学主要研究土壤与地理环境间的相关关系,涉及到土壤学、地理学、地球化学、生态学和环境科学等诸多学科(张甘霖等, 2008),研究对象包含了自然地理学涉及到的土壤圈。土壤的时空分布是土壤形成、演化、发展的综合体现,是成土因素长期综合作用的结果。传统土壤地理学研究涵盖了土壤的发生和演变、土壤分类、土壤调查、土壤分布、土壤区划和土壤资源评价等方向。随着相关学科与现代测量分析技术的快速发展,土壤地理学的研究呈现新的发展态势,从回答“土壤是什么?为什么如此?如何演变?”(张甘霖等, 2008),发展至回答“土壤如何定量化描述、分类、预测、演化?”等问题。
中国土壤地理研究的重要发展阶段包括：①以综合考察为主的区域土壤调查阶段(1950-1978年);②服务全国第二次土壤普查和学科恢复阶段(1978-2000年);③新思想和新技术引导下的学科全面发展阶段(2000年至今)(张甘霖等, 2008)。近20年来,随着调查方法和技术的发展,土壤地理学也得到了跨越式发展(Hartemink et al, 2008),其研究对象与内容有了更深的拓展。现代土壤地理学不仅关注土壤发生演变、系统分类、空间变异等基础理论问题的探讨,也更加关注土壤资源数据库的建立与集成、土壤质量的监测调控与数字化管理等实践主题(Grunwald, 2009; 龚子同等, 2014)。土壤圈在地球表层系统科学、环境保护及应对全球环境变化等问题方面的重要性已得到不同学科和社会的广泛认可。例如,联合国粮农组织2015年提出的17个可持续发展目标中至少有7项主题与土壤地理学密切相关：零饥饿、健康、清洁饮水、可持续城市、气候行动、水下生物与陆地生物。土壤圈对其他圈层具有重要的交互作用、界面过程及反馈影响(赵其国, 2003),这与国际上地球表层系统科学的研究热点——关键带在多要素、多过程、多尺度相互作用的研究实质上具有相似性(朱永官等, 2015)。有鉴于此,本文对近20年来现代土壤地理学在土壤发生、土壤形态、土壤分类、土壤调查与数字土壤制图等方面取得的显著进展进行系统地回顾与展望。
随着学科之间的交叉、新的研究方法和手段的运用以及认识水平的提高,现代土壤发生逐渐从自然因素发展到包括人为因素的影响研究,从静态发展到动态研究,从实验室到田间,从现象到机理探索,从定性到定量,从观测到模型模拟,从以土壤为主体走向以土壤为中心的地球表层系统、乃至水—土—气—生—岩交互作用的关键带研究。土壤发生的主要研究热点包括：
(1) 实验室模拟方法。土壤形成是一个长期而缓慢的过程,短时间内很难观察到土壤本身的变化。为在短时间内观察到土壤的发生过程,了解土壤发生过程的机理和速率,发展了一系列的室内模拟实验,采用加温、加压、高酸度或高碱度等控制条件,加快矿物的风化和土壤演变的进程,这在一定程度上促进了土壤发生学的研究。但是实验室模拟与实际差别很大,如实验室测定的矿物风化速率可比田间测定的大几个数量级(Swobada-Colberg et al, 1993),不能很好地应用到田间。实验室土柱模拟也是经常采用的方法,包括田间条件下的排水采集器研究,可在一定程度上兼顾纯粹实验室模拟与田间条件的优点,因而应用很广。
(2) 土壤时间序列研究。时间序列研究针对的是较长时间尺度(千年到百万年尺度)的土壤发生和演变,通常依据土壤发育的相对年龄研究土壤中元素的迁移与变化,将土壤剖面对比与速率联系起来。近年的研究成果包括在我国热带地区利用时间序列明确了黏粒矿物的演化特征特别是某些黏土矿物重生现象(He et al, 2008),利用水稻土系列明确了水稻土演化规律,特别是人为活动对土壤演变方向和速率的影响(Chen et al, 2011)。因此,土壤时间序列方法是理解不同土壤过程作用的时间尺度的重要手段,在定量化研究土壤发生过程中能起到重要的作用(Brantley, 2008; Chen et al, 2011; Li, Zhang et al, 2013)。
(3) 以土壤为核心的流域元素生物地球化学循环研究。土壤不是孤立的自然体,流域是研究元素循环和物质迁移的基本单元(Likens et al, 1995)。基于流域内岩石、土壤、干湿沉降、径流和植物的长期动态观测,通过物质输入、损失、迁移和转化,可研究土壤形成速率、影响因素及其演变趋势(Huang et al, 2013)。利用风化计量关系,结合野外流域元素质量平衡,可估算土壤的酸化速率(Yang et al, 2013)。因此,流域方法不但可研究现代环境条件下土壤当前的发生演变速率,还可将土壤的形成和演变与生态系统变化过程紧密联系起来。
(4) 研究对象和目标的转变。现代土壤发生研究对象正在向地球关键带研究转变。土壤是关键带的核心,土壤发生过程也是关键带演变的重要组成部分。关键带元素生物地球化学循环是土壤形成和演变的驱动力,一些重要的土壤发生过程如侵蚀—沉积、径流—入渗、沉降—挥发、吸收—归还、淋洗—毛管上升、转化—耦合、溶解—沉淀、风化—合成、同化—降解是研究关键带演化的核心(Jin et al, 2010; Zuo et al, 2016)。在关键带研究中,可采用多学科手段,从多尺度、多界面、多过程、多要素综合研究土壤演变的过程、速率、机理和驱动。近10年来开展的典型小流域矿物风化和土壤形成(Huang et al, 2013)、土壤酸化(Yang et al, 2013)、硅迁移(Yang et al, 2018)以及酸雨和植物的驱动(Zuo et al, 2016)研究是我国关键带研究的起步。国际上已发起并建立了60多个关键带观测站(Banwart et al, 2013),涵盖了基于传感器技术的微观尺度监测与基于遥感技术的区域尺度监测。关键带科学研究的热点包括关键带结构、物质迁移转化、服务功能与建立系统模型,以期描述地球表层系统中水文过程、生物地球化学过程与生态过程的耦合关系(杨建锋等, 2014; 安培浚等, 2016)。关键带研究代表了地球表层系统科学研究的国际前沿,同时也是土壤发生融入现代地球表层系统科学的重要契机(Clair et al, 2015; Arvin et al, 2017; Riebe et al, 2017)。
土壤形态是认识土壤形成和演化历史的关键,也是土壤发生的基础,是土壤发生发展历史的集中反映。尽管随着科技的发展,无须借助传统土壤剖面直接观测的土壤调查手段相继出现,但土壤剖面(soil profile)调查仍然是研究土壤最直观和详实的有效手段(Hartemink et al, 2016)。由于土壤发生层整合了土壤发生性质和土壤形成过程信息,因此土壤发生层划分和形态描述决定了土壤剖面描述的准确性和客观性。土壤形态特征是划分土壤发生层的主要依据,也是野外土壤描述的主要内容(Soil Survey Division Staff, 1993)。土壤形态是土壤调查的基础,结合土壤理化性质、矿物性质、微形态特征等,能帮助人们认识、理解土壤发生过程,科学划分土壤类型。
为便于同一或不同地域间研究成果的交流,标准的剖面描述规范显得尤为重要。随着对土壤认识的不断深入,剖面描述规范也相继得到了更新完善,例如Munsell比色卡、《土壤调查手册》(Soil Survey Division Staff, 1993; FAO, 2006),使得土壤剖面描述结果从技术规范上能越来越接近土壤的真实面貌。
传统的土壤形态调查存在诸多问题：①信息采集设备简陋,方法手段落后,形态特征获取困难,劳动强度大,成本高昂;②样品带回实验室分析后,只能分析获取到每层土壤样品性质的平均值,不能揭示土壤空间(水平方向和垂直方向)的变异,很少体现土壤属性在剖面的连续性分布;③一些土壤形态描述为定性的,没有定量表达,描述结果受描述者个人经验所限制。由于上述问题的存在,使土壤调查结果的适用性和实用性受到了限制,过去已经获取的大量土壤形态描述资料未能发挥应有的作用,造成极大的资源浪费。
自2003年以来,全波谱包括从长波到短波电磁感应中的X射线、γ辐射测量开始在土壤制图领域得到了较好的应用(McBratney et al, 2003)。此后,电传感器、电磁传感器、光学传感器、声学传感器、电气化学传感器及其他地球物理测量工具等普遍应用于农业和环境土壤学研究领域(Viscarra Rossel et al, 2010)。与此同时,计量土壤学也在国际上得到迅速发展,为数字化土壤形态计量学(Digital soil morphometrics,简写为DSMorph)的形成和发展奠定了坚实的基础。2014年,Hartemink和Minasny首先提出DSMorph的概念。他们认为,DSMorph是通过不同的工具和技术手段,定量获取土壤剖面属性、剖面属性图及其深度函数(Hartemink et al, 2014)。DSMorph自问世以来,得到众多土壤学家的高度关注,其快速传播与发展,为土壤调查与制图提供了新产品,为土壤地理学提供了一种新工具,给土壤分类带来一场新革命(Demattê, 2016; Hempel et al, 2016)。
国际土壤联合会(IUSS)2014年设立了DSMorph工作组,并于次年6月在美国威斯康星大学举行了第一次DSMorph国际专题研讨会。此次专题研讨会的成功举办促进了DSMorph体系的进一步完善和这一新兴领域的蓬勃发展。2016年《Digital Soil Morphometrics》一书的面世,表明DSMorph体系已趋于成熟。之后又有多篇论文问世,将DSMorph应用于不同的领域(Wang et al, 2017)。
与传统土壤剖面形态描述相比,DSMorph能更精确地定量再现土壤形态属性,并以一种相对客观的方式定量反映土壤时空变异。DSMorph主要包括4个方面内容(Jones et al, 2016)：①实现土壤形态属性信息数字化;②实现土体空间变异辨别(捕获)信息数字化;③集成与整合土壤形态信息,进行多变量统计分析;④与土壤深度函数等土壤经典模型集成,解译土壤形态信息,并应用于不同尺度、不同专业领域。
土壤分类体系构筑了关于土壤的系统知识,在一定程度上厘清了土壤之间在属性和空间上的距离关系,是土壤调查制图、资源评价、农业精准化管理及学术交流的基础和依据(龚子同等, 2007)。土壤分类体系伴随土壤知识的更新、土壤信息技术的发展也在不断更新、发展。中国土壤系统分类的建立,标志着土壤分类从定性(马伯特分类、发生分类)向定量(系统分类)的跨越(龚子同等, 2014)。现有的系统分类还属于“半定量化”体系,以土壤形态为主导并结合部分土壤理化属性。在土壤信息全面数字化的浪潮中,土壤分类发展呈现如下特点：
(1) 从高级单元走向基层分类(土族、土系化),构建与土壤综合功能密切相关的土壤基层单元分类标准。目前,传统土壤分类已基本发展成熟,虽然各个分类体系之间仍然存在一定的分歧,以诊断层和诊断特性为基础的定量土壤分类已经是普遍共识。中国土壤系统分类建立了高级单元(土纲、亚纲、土类、亚类)的分类标准和检索,但基层分类(土族、土系)体系还在持续建立与完善中(张甘霖等, 2013)。基层分类单元能更精准地解译土壤类型,与土壤综合功能密切相关,在应用上直接联系生产实际,可为农业生产、土地评价、土地利用规划、生态环境建设等提供重要的基础数据(张甘霖等, 2013)。历经10年多的中国土系调查,目前已建立了近5000个典型土系,完成了我国第一部基于定量标准和统一分类原则的《土系志》,朝着系统建立基于定量标准的基层分类体系迈出了重要的一步。土壤分类从高级分类走向基层分类,是对土壤变异精细认识的体现。
(2) 从传统分类走向数值土壤分类,实现土壤分类的定量化、数字化、信息化。伴随土壤信息获取技术的革新,土壤分类的体系与内涵也在不断发展。星地遥感技术的蓬勃发展实现了土壤信息的快速获取。传统的土壤分类体系是基于“有限的”数据样点和专家经验知识凝练而成,而在土壤信息的“大数据”时代,未来的土壤分类可藉由“数据”驱动完成。通过数据挖掘、分类距离、机器学习等,可实现土壤分类的数值化。数值土壤分类最早起源于20世纪60年代(Hole et al, 1960),基于有限的土壤理化属性及形态特征(转化为0/1值),其发展又伴随土壤信息的全面数字化而重新受到关注。相比于传统分类,数值土壤分类的优势在于可以最大限度地消除分类中的主观或人为因素,尽量客观地呈现土壤类型在时空中的差异。此外,数值分类还可实现分类的“多项选择(Multiple Allocation)”,解决现有体系中“每一种土壤在分类体系中仅有一个位置”的问题。全球的土壤学家致力于建立一个统一的土壤分类系统,而这一系统的核心思想即基于数值土壤分类。在全球统一的分类系统建立之前,数值土壤分类可以辅助评估现有的分类体系,也可为不同分类系统间的参比提供基准及参考信息(Hughes et al, 2017)。在数值分类体系中,土壤光谱是很好的分类指标。土壤光谱获取快速、便捷,是诸多土壤属性指标的综合反映(Viscarra Rossel et al, 2006)。基于光谱技术的优势,可辅助解决传统土壤分类调查所遭遇的难题(数据获取及分析成本高),而基于光谱差异所建立的数值土壤分类可为分类研究提供新的思路和维度。
(3) 从传统分类走向功能分类,建立面向土壤功能和服务生产实践的分类体系(Carré et al, 2007)。土壤分类的实质和理论基础,是区分地球表面三维土壤覆被这一连续体发生重要变化的边界,并试图将这种变化与土壤的功能相联系。无论是古代朴素分类体系所使用的颜色或土壤质地,还是现代分类采用的理化属性或光谱,都携带或者代表了土壤的某种潜在功能信息。土壤分类发展应更加直接、显性、深入地表达土壤的功能属性,土壤分类发展应致力于服务生产实践,为使用者提供土壤功能指标,如土壤肥力、固碳能力、抗侵蚀能力、土壤荷载等。
土壤调查是土壤属性特征和时空演变信息获取的第一步。传统意义上的土壤调查是在土壤地理学的理论指导下,对土壤剖面形态及其周围地理环境进行观察与描述记载,通过理化性质分析、分类与评价,对土壤的发生演变、分类、分布和功能进行对比分析。
传统土壤信息的获取具有周期长、成本高、过程复杂、复杂区域不可达、现势性差等显著缺点,难以进行大范围、高覆盖度的重复调查(Hartemink et al, 2008)。卫星与航空遥感、近地传感在内的星地遥感技术的蓬勃发展为土壤调查提供了新机遇,从20世纪末至今土壤遥感与近地传感的研究论文数量的与日俱增就是最好的体现。按照平台设计机制,土壤星地遥感技术可以大致分为卫星、航空、无人机和地面4种,不同平台获取数据的空间和时间分辨率、覆盖面积等差异明显。卫星遥感获取的信息从亚米级的高分辨率到大于1000 m的低分辨率,能较好地满足不同应用的需求(Mulder et al, 2011)。按照工作原理,土壤星地遥感技术包括光学与辐射型、电与电磁型、电化学型、机械式型等种类。地面传感包括了上述4类方式,其中卫星和航空遥感搭载的传感器主要是基于光学与辐射型。
土壤光谱探测技术研究热点集中在数据预处理与预测模型方面,土壤光谱预测模型方法主要包括各类线性模型与非线性模型,如支持向量机、随机森林、人工神经网络等。由于土壤波谱特性和预测模型的较大差异,建立全球、国家、区域尺度的波谱数据库是系统集成土壤预测能力的有效解决方案(Brown et al, 2006)。自20世纪60年代起,研究者基于vis-NIR光谱较为成功反演了一系列的土壤理化属性(Viscarra Rossel et al, 2006)。近年来,结合野外原位测量光谱和其他传感器进行空间变异制图的研究越来越广泛(Muñoz et al, 2011)。土壤光学遥感探测通过大尺度的环境信息提取与光谱信息的反演,已广泛应用于土壤类型制图、土壤属性预测与土壤退化监测(Miller et al, 2015)。土壤信息多源获取集成平台的研发是研究热点之一,如澳大利亚CSIRO集成了EM、vis-NIR、γ射线等多传感器(Viscarra Rossel et al, 2017)、美国Veris公司集成了pH电化学测试仪、vis-NIR和EC传感器等。
基于土壤介电特性与水分的耦合关系,土壤微波遥感主要用于监测土壤水分、土壤盐分及干旱度。土壤微波遥感分为主动获取与被动获取两种方式。被动微波遥感能更好地反映土壤水分状况,空间分比率较低(Moran et al, 2004),不受云层、天气等条件的限制。反之,主动微波遥感具有高空间分比率的特征,却对地表起伏和植被响应较为敏感(Petropoulos et al, 2015)。因此,主动遥感与被动遥感的融合、微波遥感与光谱遥感的结合已逐渐成为微波遥感的重要研究主题(Shi et al, 2014)。探地雷达是地球物理科学中的核心技术手段之一,在土壤地理学研究中能有效地反演土壤质地、土壤水分等重要信息(Huisman et al, 2003),正逐渐广泛应用于地球表层系统科学中土壤剖面特征结构的探测。
早期电磁感应仪主要是对土壤水分及盐分的二维反演制图。土壤表层盐分信息尚不能满足盐碱地改良的需求,而基于电磁感应仪的土壤盐分三维制图分析为此提供了新的技术手段(Li, Shi et al, 2013),例如基于EM38对坡面土壤盐分的制图分析(Huang et al, 2015)。近年来,激光等离子体光谱(LIBS)和X射线荧光光谱传感器(XRF)在土壤重金属含量的探测方面已取得显著进展(Santos et al, 2009)。可以说,基于不同平台和频率的电磁感应探测已成为现代土壤调查和土壤信息获取的重要手段,相关研究势必成为今后该领域研究的主流。
对精细准确土壤信息的需求推动了数字土壤制图(亦即预测性土壤制图)的兴起,其特点是以土壤与景观定量模型为基础、以栅格数据作为表达方式在计算环境下机器辅助成图。20世纪90年代初开始萌芽,当时主要运用线性回归和判别分析等建立土壤与环境关系并绘制土壤类型或属性分布(Moore et al, 1993)。数字土壤制图发展中有3个重要里程碑：①2004年在法国蒙彼利埃市召开的数字土壤制图国际研讨会;②2005年IUSS设立数字土壤制图工作组,决定以2004年为起点,每隔2年举行1次全球性研讨会;③2009年在美国哥伦比亚大学正式启动了“全球数字土壤制图计划”(Sanchez et al, 2009)(GlobalSoilMap.net)。数字土壤制图的理论范式仍是成土因素学说,在此基础上McBratney等(2003)提出了面向数字土壤制图更具操作性的SCORPAN范式,Grunwald等(2011)提出了显式考虑人类活动的STEP-AWBH概念方程。
数字土壤制图进展包括土壤数据获取、成土环境表征与模型算法等3个主要方面。历史土壤图可作为协同变量或土壤环境关系规则的载体参与制图(Yang et al, 2011; Adhikari et al, 2014),历史土壤样点缺少精确地理参考,不符合任何统计标准,限制了地统计方法的使用(Liu et al, 2016)。土壤采样是获取样本数据的重要途径,可分为基于设计和基于模型的2类采样方法(De Gruijter et al, 2006)。对于制图验证目的,宜选用基于设计的方法;而对模型训练,发展了多种方法,如地统计、条件拉丁立方、基于聚类的目的性采样等(Minasny et al, 2006)。上述方法都不同程度地存在野外可操作性问题(McBratney et al, 2003),未来仍需开发更为灵活有效的采样方法。最新的研究动态包括基于空间推测不确定性的补样方法和考虑可达性或采样成本的采样方法,有学者提出兼顾属性域和空间域的补样方案(Li et al, 2016)、在拉丁超立方采样中加入可达性限制或成本限制(Yin et al, 2016)。
环境协同变量是土壤制图的重要支撑,与气候、地形、植被和人为活动因素相比,母质信息相对难以获取,多用地质岩性与地貌单元替代,母质类型空间分布制图是一个重要研究问题。最新研发的环境协同变量包括：地表动态反馈信息、模糊坡位、人类活动因子等。平缓地区土壤制图是一个难题,Liu等(2012)提出了基于地表动态反馈,利用时间序列遥感观测开发环境协同变量进行土壤制图的思路和方法,已在国内外多个研究区得到成功的应用与验证(Zhao et al, 2014),近几年得到了较大的推进和发展(Guo et al, 2016; Zeng et al, 2017)。
数字土壤制图的模型方法主要有统计、空间统计、专家知识和机器学习方法等。空间统计中以回归克里格和地理加权回归(Song et al, 2016)最为常用,近年的发展有面点克里格(Kerry et al, 2012)等;基于专家知识的方法有SoLIM(soil-land inference model)(Zhu et al, 1996)等;机器学习方法包括神经网络、支持向量机和随机森林等(Yang et al, 2016)。目前地统计方法仍占主导地位,但机器学习开始崛起,在揭示土壤空间变异方面已显示出较大潜力。三维数字土壤制图可完整地揭示土壤分布模式,近10年来也有了较大进展(Malone et al, 2009; Liu et al, 2013; Yang et al, 2017)。
数字土壤制图未来发展主要有以下趋势(Zhang et al, 2017)：①从小面积实验区走向区域、国家和全球以及实现多尺度制图,以关键属性为目标的高精度土壤图必将成为土壤资源管理的依据;②从简单景观走向复杂景观及人为作用强烈地区,攻克高度变异环境下土壤空间变化模拟的难点;③从仅关注表层的二维走向整个土体甚至风化层的三维制图,完整描述地球表层的变异,向“透明土壤”迈进;④从地统计为主导走向机器学习为主导模型,同时发生学知识与机器学习算法结合将更加深入;⑤从单源数据走向多源土壤数据和多平台传感数据的集成利用;⑥从农业生产管理应用走向生态系统服务和关键带过程研究等领域(Zhang et al, 2017)。
基于土壤地理学目前的发展动态与研究热点,未来发展趋势与核心问题主要有以下几点：
(1) 理论研究的拓展。全球环境变化和人为活动强烈条件下对土壤发生研究提出了新的挑战,土壤发生研究从以土壤为主体走向以土壤为中心的地球表层系统。未来的土壤发生研究将采用传统方法与先进的实验和同位素方法,结合动态和静态研究,以微观和宏观结合的定量化手段研究成土过程,将重点围绕变化的自然条件和强烈的人为干扰条件下土壤演变机理与速率。土壤发生过程模拟研究和模型的建立,有望为土壤资源的可持续利用奠定基础。
(2) 调查技术的革新。土壤信息获取技术将是全球生物地球化学、关键带科学研究中不可或缺的重要组成部分。因此,各种新型土壤传感器平台的构建与综合,不仅能拓展土壤理化性质与水土过程的长期监测,也能实时监测大范围易获取、易变土壤属性信息及相关环境信息。卫星与航空遥感、近地传感在内的星地遥感技术的快速发展,将提供实时/准实时全球尺度的土壤地球化学组分网格数据,为长期的全球气候变化和环境监测服务。基于全球尺度的多源土壤星地遥感数据融合,有望为土壤地理学大数据分析提供契机。
(3) 时空演变过程的模型与模拟。以土壤空间分布规律为核心的传统土壤地理学正逐步从定性描述走向数字化土壤形态、数字土壤制图和计量土壤学。随着多源、多平台传感器的发展以及土壤地理信息系统技术的不断进步,数字土壤制图可预测区域、国家、全球尺度上的土壤理化属性空间分布特征。未来数字土壤制图的发展需要研究对环境要素进行刻画的新技术——特别是体现人类活动方面的环境因子、历史数据与新型数据无缝集成的新分析方法、土壤发生知识与数学模型紧密结合的新推理方法及大数据多终端的计算模式。
The authors have declared that no competing interests exist.
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地球关键带(Earth Critical Zone)由美国国家研究理事会于2001年正式提出以来,倍受关注,取得了重要研究进展。总结分析了美国、德国、澳大利亚、法国和中国等国家,以及欧盟等相关国际组织相继开展和部署的地球关键带研究项目及相关研究计划、主要科学问题、未来发展方向等,并结合我国研究现状,提出我国应加强地球关键带结构、形成与演化机制,地球关键带物质迁移转化与多过程耦合作用机制,地球关键带服务功能、演化特点及其对可持续发展的支撑和影响,地球关键带过程及系统的模型模拟4个方面研究,通过积极与国际科技发达国家开展合作和交流,提高国际大型计划的参与度。
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地球关键带(Earth Critical Zone)由美国国家研究理事会于2001年正式提出以来,倍受关注,取得了重要研究进展。总结分析了美国、德国、澳大利亚、法国和中国等国家,以及欧盟等相关国际组织相继开展和部署的地球关键带研究项目及相关研究计划、主要科学问题、未来发展方向等,并结合我国研究现状,提出我国应加强地球关键带结构、形成与演化机制,地球关键带物质迁移转化与多过程耦合作用机制,地球关键带服务功能、演化特点及其对可持续发展的支撑和影响,地球关键带过程及系统的模型模拟4个方面研究,通过积极与国际科技发达国家开展合作和交流,提高国际大型计划的参与度。
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基础土壤地理学的核心内容是土壤在时间和空间中的变化，目标是预测土壤在生态系统中的行为以及在自然和人为影响下的演变、实现土壤资源的有效管理。我国土壤地理学的发展经历了几个重要阶段，20世纪50年代区域和全国性的土壤调查和综合考察奠定了我国土壤地理学的发展基础；20世纪80年代随着全国第二次土壤普查的开始和科学研究恢复，土壤地理学重新复兴，包括土壤发生、土壤分类、土壤制图、土壤遥感等各个分支学科在内的土壤地理学得以迅速发展，这其中1984年开始的“中国土壤系统分类”研究贯穿了此后20多年的发展历程并推动了相关学科的进步；20世纪90年代以后，以3s技术为代表的新技术和新方法全面促进了土壤地理研究的现代化，土壤空间变化描述的内容、方式、应用等都发生了重大变化。未来的土壤地理学研究将面对我国土壤资源制约的国情，重点应该围绕变化中的自然条件和强烈的人为干扰下土壤质量与功能的演变、以土系为主体的土壤基层分类体系、以信息技术和模型模拟相结合的土壤资源数字化表达与管理系统等中心内容，为我国土壤资源的可持续管理、环境保护以及应对全球环境变化服务。
Retrospect and prospect of soil geography in China [J].https://doi.org/10.3321/j.issn:0564-3929.2008.05.005 [本文引用: 3] 摘要
基础土壤地理学的核心内容是土壤在时间和空间中的变化，目标是预测土壤在生态系统中的行为以及在自然和人为影响下的演变、实现土壤资源的有效管理。我国土壤地理学的发展经历了几个重要阶段，20世纪50年代区域和全国性的土壤调查和综合考察奠定了我国土壤地理学的发展基础；20世纪80年代随着全国第二次土壤普查的开始和科学研究恢复，土壤地理学重新复兴，包括土壤发生、土壤分类、土壤制图、土壤遥感等各个分支学科在内的土壤地理学得以迅速发展，这其中1984年开始的“中国土壤系统分类”研究贯穿了此后20多年的发展历程并推动了相关学科的进步；20世纪90年代以后，以3s技术为代表的新技术和新方法全面促进了土壤地理研究的现代化，土壤空间变化描述的内容、方式、应用等都发生了重大变化。未来的土壤地理学研究将面对我国土壤资源制约的国情，重点应该围绕变化中的自然条件和强烈的人为干扰下土壤质量与功能的演变、以土系为主体的土壤基层分类体系、以信息技术和模型模拟相结合的土壤资源数字化表达与管理系统等中心内容，为我国土壤资源的可持续管理、环境保护以及应对全球环境变化服务。
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中国土壤系统分类土族和土系划分标准 [J].https://doi.org/10.11766/trxb201303180124 URL [本文引用: 3] 摘要
中国土壤系统分类建立了亚类以上高级单元分类标准和检索,但在基层分类标准方面尚待系统建立和完善。本文主要介绍了中国土壤系统分类土族与土系划分的标准,从标准建立的背景、原则与特点到标准本身进行了较为详尽的描述,并用实例演示了土族土系标准在具体土壤上的应用。
Criteria for establishment of soil family and soil series in Chinese Soil Taxonomy [J].https://doi.org/10.11766/trxb201303180124 URL [本文引用: 3] 摘要
中国土壤系统分类建立了亚类以上高级单元分类标准和检索,但在基层分类标准方面尚待系统建立和完善。本文主要介绍了中国土壤系统分类土族与土系划分的标准,从标准建立的背景、原则与特点到标准本身进行了较为详尽的描述,并用实例演示了土族土系标准在具体土壤上的应用。
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发展与创新现代土壤科学 [J].Development and innovation of modern soil science [J]. |
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土壤安全: 从地球关键带到生态系统服务 [J].https://doi.org/10.11821/dlxb201512001 URL [本文引用: 1] 摘要
土壤是人类赖以生存和文明建设的重要基础资源。作为地球关键带的核心要素,土壤圈是地球表层系统最为活跃的圈层,而且土壤过程是控制地球关键带中物质、能量和信息流动与转化的重要节点。由于土壤的多重功能不断得到重视,传统的土壤概念已经无法全面反应土壤的功能和作用,为此本文提出了土壤安全的概念。土壤安全是一种基于土壤可持续发展目标而提出一种系统战略框架,为土壤资源的可持续利用和保护提供了理论基础。本文重点论述了地球关键带和土壤安全的内涵以及两者之间的差异和紧密关系。此外,还对土壤安全框架下的生态系统服务进行了梳理和总结,最后对面向生态系统服务的土壤安全需求进行了展望。
Soil security: From Earth's critical zone to ecosystem services [J].https://doi.org/10.11821/dlxb201512001 URL [本文引用: 1] 摘要
土壤是人类赖以生存和文明建设的重要基础资源。作为地球关键带的核心要素,土壤圈是地球表层系统最为活跃的圈层,而且土壤过程是控制地球关键带中物质、能量和信息流动与转化的重要节点。由于土壤的多重功能不断得到重视,传统的土壤概念已经无法全面反应土壤的功能和作用,为此本文提出了土壤安全的概念。土壤安全是一种基于土壤可持续发展目标而提出一种系统战略框架,为土壤资源的可持续利用和保护提供了理论基础。本文重点论述了地球关键带和土壤安全的内涵以及两者之间的差异和紧密关系。此外,还对土壤安全框架下的生态系统服务进行了梳理和总结,最后对面向生态系统服务的土壤安全需求进行了展望。
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Digital mapping of soil organic carbon contents and stocks in Denmark [J].https://doi.org/10.1371/journal.pone.0105519 URL PMID: 4138211 [本文引用: 1] 摘要
Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0615, 56115, 156130, 306160 and 6061100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg611 was reported for 0615 cm soil, whereas there was on average 2.2 g SOC kg611 at 6061100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg611 was found at 6061100 cm soil depth. Average SOC stock for 06130 cm was 72 t ha611 and in the top 1 m there was 120 t SOC ha611. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.
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Global patterns of dust and bedrock nutrient supply to montane ecosystems [J].https://doi.org/10.1126/sciadv.aao1588 URL PMID: 29226246 [本文引用: 1] 摘要
Abstract A global compilation of erosion rates and modeled dust fluxes shows that dust inputs can be a large fraction of total soil inputs, particularly when erosion is slow and soil residence time is therefore long. These observations suggest that dust-derived nutrients can be vital to montane ecosystems, even when nutrient supply from bedrock is substantial. We tested this hypothesis using neodymium isotopes as a tracer of mineral phosphorus contributions to vegetation in the Sierra Nevada, California, where rates of erosion and dust deposition are both intermediate within the global compilation. Neodymium isotopes in pine needles, dust, and bedrock show that dust contributes most of the neodymium in vegetation at the site. Together, the global data sets and isotopic tracers confirm the ecological significance of dust in eroding mountain landscapes. This challenges conventional assumptions about dust-derived nutrients, expanding the plausible range of dust-reliant ecosystems to include many temperate montane regions, despite their relatively high rates of erosion and bedrock nutrient supply.
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Sustaining Earth's critical zone basic science and interdisciplinary solutions for global challenges [M]. |
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Understanding soil time [J].https://doi.org/10.1126/science.1161132 URL [本文引用: 1] |
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Global soil characterization with VNIR diffuse reflectance spectroscopy [J].https://doi.org/10.1016/j.geoderma.2005.04.025 URL [本文引用: 1] 摘要
There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (< 2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg 61 1 for clay, 7.9 g kg 61 1 for soil organic C (SOC), 5.6 g kg 61 1 for inorganic C (IC), 8.9 g kg 61 1 for dithionate–citrate extractable Fe (FEd), and 5.5 cmol c kg 61 1 for cation exchange capacity (CEC) with NH 4 at pH = 7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0–5 on ordinal scale). Given the amount of information obtained in this study with 654 × 10 3 samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 10 4–10 5 samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required.
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Digital soil assessments: Beyond DSM [J].https://doi.org/10.1016/j.geoderma.2007.08.015 URL [本文引用: 1] 摘要
Over the last 10 years Digital Soil Mapping (DSM) has emerged as a credible alternative to traditional soil mapping. However, DSM should not be seen as an end in itself, but rather as a technique for providing data and information for a new framework for soil assessment which we call Digital Soil Assessment (DSA). Although still somewhat fluid, a procedural framework for DSM and DSA with its links and feedbacks is set out diagrammatically and discussed. A significant advantage inter alia of DSM over conventional methods in this context is the intended provision of estimates of predictor uncertainties. DSA comprises three main processes: (1) soil attribute space inference, (2) evaluation of soil functions and the threats to soils, and (3) risk assessment and the development of strategies for soil protection. Digital Soil Risk Assessment (DSRA) consists of integrating political, social, economical parameters and general environmental threats to DSA outputs for building, modelling and testing some scenarios about environmental perspectives. The procedure as a whole is illustrated using an example.
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Soil characteristic response times and pedogenic thresholds during the 1000-year evolution of a paddy soil chronosequence [J].https://doi.org/10.2136/sssaj2011.0006 URL [本文引用: 2] 摘要
A paddy soil chronosequence consisting of five profiles derived from calcareous marine sediments with cultivation history from 0 to 1000 yr was studied to assess the dynamic changes in soil properties and major elemental mass balance during soil evolution and to understand the response rates of soil properties at different time scales. The threshold concept was applied to increase our understanding of paddy soil genesis processes. Results showed that 50 yr of paddy cultivation induced measurable accumulation of soil organic C (SOC) in the surface horizon and marked reduction of magnetic susceptibility (MS), soft isothermal remanent magnetization (IPM
|
| [16] |
Geophysical imaging reveals topographic stress control of bedrock weathering [J].https://doi.org/10.1126/science.aab2210 URL PMID: 26516279 [本文引用: 1] 摘要
Bedrock fracture systems facilitate weathering, allowing fresh mineral surfaces to interact with corrosive waters and biota from Earth's surface, while simultaneously promoting drainage of chemically equilibrated fluids. We show that topographic perturbations to regional stress fields explain bedrock fracture distributions, as revealed by seismic velocity and electrical resistivity surveys from three landscapes. The base of the fracture-rich zone mirrors surface topography where the ratio of horizontal compressive tectonic stresses to near-surface gravitational stresses is relatively large, and it parallels the surface topography where the ratio is relatively small. Three-dimensional stress calculations predict these results, suggesting that tectonic stresses interact with topography to influence bedrock disaggregation, groundwater flow, chemical weathering, and the depth of the "critical zone" in which many biogeochemical processes occur.
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Sampling for natural resource monitoring [M]. |
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From profile morphometrics to digital soil mapping [M]// |
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Guidelines for soil profile descriptions [R]. |
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Multi-criteria characterization of recent digital soil mapping and modeling approaches [J].https://doi.org/10.1016/0741-5214(87)90241-2 URL [本文引用: 1] 摘要
The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to characterize recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review 90 DSMM publications from two high-impact international soil science journals Geoderma and Soil Science Society of America Journal. A selective approach was used to identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates, (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modeling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge environmental landscape data and denser soil datasets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and pedological expertise and all should be ideally in balance.
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| [21] |
Digital soil mapping and modeling at continental scales: Finding solutions for global issues [J].https://doi.org/10.2136/sssaj2011.0025 URL [本文引用: 1] 摘要
ABSTRACT Profound shifts have occurred during the last three centuries in which human actions have become the main driver to global environmental change. In this new epoch, the Anthropocene, human-driven changes such as population growth, climate, and land use change are pushing the Earth system well outside of its normal operating range, causing severe and abrupt environmental change. In the Anthropocene, soil change and soil formation or degradation have also accelerated, jeopardizing soil quality and health. Thus, the need for up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize the physicochemical, biological, and hydrologic conditions of ecosystems across continents has intensified. These needs are in sharp contrast to available digital soil data representing continental and global soil systems, which only provide coarse-scale (1:1,000,000 or coarser) vector polygon maps with highly aggregated soil classes represented in the form of crisp map units derived from historic observations, lacking site-specific pedogenic process knowledge, and only indirectly relating to pressing issues of the Anthropocene. Furthermore, most available global soil data are snapshots in time, lacking the information necessary to document the evolution of soil properties and processes. Recently, major advancements in digital soil mapping and modeling through geographic information technologies, incorporation of soil and remote sensing products, and advanced quantitative methods have produced domain-specific soil property prediction models constrained to specific geographic regions, which have culminated in the vision for a global pixel-based soil map. To respond to the challenges soil scientists face in the Anthropocene, we propose a space-time modeling framework called STEP-AWBH ("step-up"), explicitly incorporating anthropogenic forcings to optimize the soil pixel of the futurevv.
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| [22] |
Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area [J].https://doi.org/10.1016/j.jag.2016.02.002 URL [本文引用: 1] 摘要
Detailed and accurate information on the spatial variation of soil types and soil properties are critical components of environmental research and hydrological modeling. Early studies introduced a soil feedback pattern as a promising environmental covariate to predict spatial variation over low-relief areas. However, in practice, local evaporation can have a significant influence on these patterns, making them incomparable at different locations. This study aims to solve this problem by examining the concept of transforming the dynamic patterns of soil feedback from the original time-related space to a new evaporation-related space. A study area in northeastern Illinois with large low-relief farmland was selected to examine the effectiveness of this idea. Images from MODIS in Terra for every April ay period over 12 years (2000 2011) were used to extract the soil feedback patterns. Compared to the original time-related space, the results indicate that the patterns in the new evaporation-related space tend to be more stable and more easily captured from multiple rain events regardless of local evaporation conditions. Random samples selected for soil subgroups from the SSURGO soil map show that patterns in the new space reveal a difference between different soil types. And these differences in patterns are closely related to the difference in the soil structure of the surface layer.
|
| [23] |
A soil science renaissance [J].https://doi.org/10.1016/j.geoderma.2008.10.006 URL [本文引用: 2] |
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Towards digital soil morphometrics [J].https://doi.org/10.1016/j.geoderma.2014.03.008 URL [本文引用: 1] 摘要
61There is a range of new tools and techniques to describe and analyse soil profiles.61Small depth increments can be sampled providing continuous depth functions of soil properties.61Digital morphometrics and continuous depth functions may enhance our pedological understanding.
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| [25] |
Developments in digital soil morphometrics [M]//Hartemink A E, Minasny B. |
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Clay minerals in a soil chronosequence derived from basalt on Hainan Island, China and its implication for pedogenesis [J].https://doi.org/10.1016/j.geoderma.2008.10.007 URL [本文引用: 1] 摘要
A soil chronosequence consisting of six profiles formed on quartz tholeiite basalt ranging in age from 10,00002years to 1. 802Million years (My) was studied here. Soil clays were identified using XRD diffractogram decomposition methods for samples obtained from the A and C horizons of profiles. The results showed that kaolinite minerals dominated in all the clay fractions. Gibbsite was prominent in the C horizons in the soils from older rocks. Clays in the A horizon of relatively young soils showed an initial stage of illite formation, followed by smectite mixed layer minerals (illite–smectites and then vermiculite–illite) and finally by vermiculite. The initial presence of illite is interesting as there is no magmatic micaceous or phyllosilicate phase in these basalts and the formation of illite we attribute to a secondary process, probably created by alkali transport by plant materials. The change in 2:1 clay mineralogy reflects the overall change in Si/Al ratios in the soils over longer periods of weathering. In all cases gibbsite is more abundant in the C horizons than the A horizons. The difference in gibbsite content between the A and C horizons we attribute to plant transport of siliceous phytolite material to the surface. Continued high rainfall over long periods of time removed the alkali faster than the plants could bring it to the surface, which led to continuous lowering of 2:1 minerals from younger to older in the soil chronosequence. Nevertheless a 2:1, silica-rich mineral persists in the clay assemblages although in very minor amounts.
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| [27] |
The next generation of soil survey digital products [M]//Hartemink A E, Minasny B. |
| [28] |
An experiment in ordination of some soil profiles [J].https://doi.org/10.2136/sssaj1960.03615995002400040028x URL [本文引用: 1] 摘要
An attempt is made to apply Goodall's arrangement of units in a uni- or multidimensional order (Angew. PflSoziol., 1954, 168-182) to the classification of soil profiles, using data of the Miami family ana catena and of 25 profiles representing as many soil groups. A three-dimensional model showing relationships between these profiles illustrates the possibilities of this kind of analysis.
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| [29] |
Modeling soil salinity along a hillslope in Iran by inversion of EM38 data [J].https://doi.org/10.2136/sssaj2014.11.0447 URL [本文引用: 1] 摘要
Abstract Electromagnetic (EM) induction has been used to characterize the spatial distribution of salinity. However, most studies have been undertaken to map the areal distribution of average profile salinity using measurements of the apparent electrical conductivity (ECa – mS m-1). In this study, an EM38 was used to map the distribution of salinity with depth along a 26-km hill slope in central Iran. We generated electromagnetic conductivity images (EMCI) by inverting EM38 ECa data collected at various heights in the EM4Soil software. A number of parameters including forward modelling (cumulative function-CF and full solution-FS), inversion algorithm (S1 and S2), damping factor (85) and combinations of different heights were considered to generate calculated soil true electrical conductivity (82 - mS m-1). By comparing different 82 against electrical conductivity of a saturated soil-paste extract (ECe - dS m-1) at various depths, we found that the strongest correlation and smallest modelling error was achieved using the FS, S1, 85 = 12 and ECa data collected at 0.4 m alone. We then compared the results achieved by developing a linear regression between 82 and ECe at various depths with those achieved using multiple linear regression (MLR) established between ECa and ECe. The inversion method was less time-consuming and more robust than the MLR approach. The predicted ECe increased from the crest to the base of the toposequence. The results were consistent with the underlying geology, climate and local topography. The methodology can be used as guidance for base line salinity monitoring and management.
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| [30] |
Weathering and soil formation rates based on geochemical mass balances in a small forested watershed under acid precipitation in subtropical China [J].https://doi.org/10.1016/j.catena.2013.01.002 URL [本文引用: 2] 摘要
Accurate weathering and soil formation rates in natural environment and their quantitative dependences on environmental factors remain poorly understood, despite their significance in the understanding of biogeochemical cycling and for the development of sustainable land-use strategies. In the present study, rates of weathering and soil formation on granite and their dependences on acid precipitation were studied in a small forested watershed, Fengxingzhuang (FXZ), in subtropical China using geochemical mass balance equations and multiple regression analysis. Atmospheric input from wet and dry deposition, and stream output through runoff were monitored from March, 2007 to February, 2010. The physical and chemical properties of soil and granite rock were also determined. The results show that acid precipitation is very severe in the FXZ forested watershed by bringing in H + both directly from strong acids (74102mol02ha 61021 02yr 61021 ) and indirectly from nitrogen and sulfur transformations (83102mol02ha 61021 02yr 61021 ), which serves as an important driving force for weathering and soil formation. The FXZ forested watershed currently remains as a net sink for hydrogen ion, inorganic nitrogen, sulfur, potassium, and aluminum, with a mean of 0.07, 1.60, 1.48, 0.10, and 0.4402g02m 61022 02yr 61021 , respectively, and a net source for dissolved silicon, sodium, calcium, and magnesium, with a mean of 5.33, 2.95, 1.04, and 0.3402g02m 61022 02yr 61021 , respectively. Based on geochemical mass balance equations, the weathering rate of granite (R) in the FXZ forested watershed is 1.0402±020.6502t02ha 61021 02yr 61021 , but varying according to the changes in environmental conditions such as rainfall amount, air temperature, and H + input within different seasons. Correspondingly, soil formation rate (S) is 0.9502±020.6902t02ha 61021 02yr 61021 , which equals to 0.06602±020.04802mm02yr 61021 of soil depth, suggesting a base for establishing the soil loss tolerance value in the granitic region of subtropical China. Acid precipitation significantly promotes weathering and soil formation by the input of rainfall and H + , although air temperature effects occur simultaneously. A proposed model that quantitatively describes weathering and soil formation rates is a combined product of rainfall amount ( x 1 ), H + input ( x 2 ) and air temperature ( x 3 ) resulting from multiple regressions: R02=020.00045026502 x 1 02+020.026026502e 0.0022 x 2 02+020.012026502e 0.0459 x 3 ; S02=020.00035026502 x 1 02+020.029026502e 0.002 x 2 02+020.016026502e 0.0424 x 3 , which improves the prediction from simple linear regression.
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| [31] |
Comparisons between USDA Soil Taxonomy and the Australian Soil Classification System I: Data harmonization, calculation of taxonomic distance and inter-taxa variation [J].https://doi.org/10.1016/j.geoderma.2017.08.009 URL [本文引用: 1] |
| [32] |
Measuring soil water content with ground penetrating radar [J].https://doi.org/10.2136/vzj2003.0476 URL [本文引用: 1] 摘要
Abstract Ground penetrating radar (GPR) was used to measure the distribution of soil water content below point and line sources of water. Vertical time domain reflectometry (TDR) probes were installed below and around the water sources. The TDR probes were used to measure soil water content for comparison to the GPR data. The GPR measurements were performed in zero offset gather (ZOG) and multiple offset gather (MOG) survey modes with the antennas in horizontal bore holes located on opposite sides and below the water sources. The ZOG survey mode gave an estimate of the average water content in the horizontal plane below the water sources at a prescribed interval between the horizontal boreholes. The MOG survey mode produced a tomographic image of the distribution of soil water content in the same horizontal plane below the water sources. I similar range of values of water contents was measured by TDR and GPR methods.
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| [33] |
Mineral weathering and elemental transport during hillslope evolution at the Susquehanna/Shale Hills Critical Zone Observatory [J].https://doi.org/10.1016/j.gca.2010.03.036 URL [本文引用: 1] 摘要
The losses of Mg and K in the soils occur largely as solute fluxes; in contrast, losses of Al and Fe are mostly as downslope transport of fine particles. Physical erosion of bulk soils also occurs: results from a steady-state model demonstrate that physical erosion accounts for about half of the total denudation at the ridgetop and midslope positions. Chemical weathering losses of Mg, Na, and K are higher in the upslope positions likely because of the higher degree of chemical undersaturation in porewaters. Chemical weathering slows down in the valley floor and Al and Si even show net accumulation. The simplest model for the hillslope that is consistent with all observations is a steady-state, clay weathering-limited system where soil production rates decrease with increasing soil thickness.
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| [34] |
What is digital soil morphometrics and where might it be going [M]//Hartemink A E, Minasny B. |
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Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale [J].https://doi.org/10.1016/j.geoderma.2011.10.007 URL PMID: 4341910 [本文引用: 1] 摘要
Legacy soil polygon data disaggregated by AtoP kriging and simple rasterization were used in a RK framework for estimating soil organic carbon (SOC) concentrations across the whole of Northern Ireland, using soil sample data from the Tellus survey of Northern Ireland and with other covariates (altitude and airborne radiometric potassium). This allowed direct comparison with previous analysis of the Tellus survey data. Incorporating the legacy data, whether from simple rasterization of the polygons or AtoP kriging, substantially reduced the MAEs of RK compared with previous analyses of the Tellus data. However, using legacy data disaggregated by AtoP kriging in RK resulted in a greater reduction in MAEs. A jack-knife procedure was also performed to determine a suitable number of additional soil samples that would need to be collected for RK of SOC for the whole of Northern Ireland depending on the availability of ancillary data. We recommend i) if only legacy soil polygon map data are available, they should be disaggregated using AtoP kriging, ii) if ancillary data are also available legacy data should be disaggregated using AtoP RK and iii) if new soil measurements are available in addition to ancillary and legacy soil map data, the legacy soil map data should be first disaggregated using AtoP kriging and these data used along with ancillary data as the fixed effects for RK of the new soil measurements.
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| [36] |
Mapping the three-dimensional variation of soil salinity in a rice-paddy soil [J].https://doi.org/10.1016/j.geoderma.2012.11.005 URL [本文引用: 1] 摘要
Soil salinity is widespread in a variety of environments, and land managers need to map its severity and extent both laterally and vertically. In this research we explore the inversion of apparent electrical conductivity (EC a ) measured with an EM38 using a linear model and Tikhonov regularization to model electrical conductivity (σ) profiles in a saline paddy field in the Yangtze delta of China. The modelled σ matched closely the directly measured bulk electrical conductivity (σ b ) in the topsoil within our calibration field. Discrepancies were greatest between 0.4 and 0.802m, below which they converged again, and were judged small enough to map soil salinity. Equivalent EC a data, recorded in an adjacent field, was similarly inverted with the modelled σ analysed geostatistically. In this regard, the σ data at 10 depths were treated as 10 correlated variates, and experimental auto-and cross-variograms were computed by the method of moments from them. A linear model of coregionalization fitted well, and it was used to cokrige σ on 502m02×02502m blocks on a fine grid. The kriging errors, computed as the square roots of the cokriging variances, were typically about 5% of the kriged estimates. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e. electrical conductivity of a saturated soil paste extract — EC e ). The results indicate that an irregularly shaped patch of strongly saline topsoil (i.e. 8–1202dS02m 61021 ) and subsoil salinity (i.e. >021602dS02m 61021 ) at the southern end of the field was consistent with a yield reduction of some 33%; and as compared with the weakly saline conditions evident at the northern end of the field (e.g. topsoil EC e 2–402dS02m 61021 ) where yield was much larger. We conclude that the approach has merit and might be useful in providing a baseline set of data and a method that can used to monitor and evaluate the management of salinity.
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| [37] |
Nd isotope evidence for dust accretion to a soil chronosequence in Hainan Island [J].https://doi.org/10.1016/j.catena.2012.09.014 URL [本文引用: 1] 摘要
Hainan soils contain a mixture of material derived from in situ weathering of parent material plus atmospheric inputs dominated by continental dust. We use Nd isotope geochemistry to evaluate the impact of continental dust on a chronosequence of Hainan soils. The results demonstrate that dust has a profound effect on soils in 180 ka and older in tropical Hainan Island. Dust is the dominant source of Nd in the oldest, most intensely weathered soils, with radiogenic dust-derived isotopic signatures (epsilon(Nd)(0) = -6.46) and significant high mass fraction of dust-derived Nd (75%). The f(dust)(Nd) values for soils show an increasing trend with time, implying that dust accumulates with the ages of soils. We also calculate a long term average dust accretion rates of 14.90 and 12.39 mg cm(-2) ka(-1) at the 180 and 300 ka sites; whereas the rates of the old sites are low, indicating underestimates of time-averaged dust deposition rates over million year time scales. On the basis of the geomorphic stability sites, we think that the decrease of dust inputs due to climate changes and the loss of dust due to weathering are the dominant loss mechanisms for dust and lead to underestimates of time-averaged dust deposition rates in the old sites. The results also underscore the potential for neodymium isotopes to constrain the origin of soils and paleosols. (c) 2012 Elsevier B.V. All rights reserved.
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Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain [J].https://doi.org/10.1016/j.geoderma.2016.08.013 URL [本文引用: 1] 摘要
This paper presents an uncertainty-directed sampling method that can be used to design additional samples for soil mapping. The method is based on uncertainty from both the feature domain (the domain of relationships with environmental covariates) and the spatial domain (the domain of spatial autocorrelation). Existing soil samples are also taken into account. The method comprises three steps: 1) the selection of a ranked list of additional sample locations based on uncertainty from the feature domain using individual predictive soil mapping (iPSM); 2) the selection of a ranked list of additional sample locations based on uncertainty from the spatial domain using ordinary kriging; 3) the determination of a final ranked list created by merging the ranked lists from steps 1) and 2) based on both uncertainties. To evaluate the method, the three lists were used to map soil organic matter (SOM) in a 299.14 km 2 study area near Fuyang city in the northwest region of Zhejiang Province, China. The mapping accuracy of each list was then calculated and used to assess the effectiveness of the method. Compared with the sampling scheme based on the uncertainty from either the feature domain or the spatial domain alone, the root-mean-squared error (RMSE), with the addition of the final list based on both uncertainties, was found to be the smallest, ranging from 0.829 to 1.126, and the agreement coefficient (AC) was the largest, ranging from 0.634 to 0.737. This confirms that sampling based on two uncertainties is better than sampling based on uncertainty from either the feature domain or the spatial domain alone. The results suggest that the proposed combined additional sampling method is more effective for sampling additional points in soil mapping.
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Biogeochemistry of a forested ecosystem [M]. |
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Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS [J].https://doi.org/10.1016/j.geoderma.2011.05.007 URL [本文引用: 1] 摘要
In low relief areas such as plains, easily obtained soil forming factors generally do not co-vary with soil conditions over space to the level that they can be used effectively in digital soil mapping. Mapping variation of soil properties over such areas remains a challenge. This paper presents an approach to mapping soil texture using environmental covariates derived from temporal responses of the land surface to a rainfall event (dynamic feedbacks) collected through remote sensing techniques. The approach consists of four steps: (1) construction of a set of environmental covariates from dynamic feedbacks of the land surface, captured daily from MODIS (Moderate Resolution Imaging Spectroradiometer) images over a short period (6 7 days) after a major rain event; (2) derivation of environmental classes based on the set of environmental covariates using a fuzzy c-means clustering; (3) Identification of typical soil texture value for each of the environmental classes from a dataset of field soil samples; (4) mapping of spatial variation of soil texture through a linearly weighted averaging function. The approach was applied to produce soil texture maps in a low relief area situated in south-central Manitoba, Canada. Its performance was assessed through comparison with soil texture maps generated from 1:20,000 traditional soil survey. The assessment was based on 34 field sample sites, independent of the samples used for prediction. The error values (9.42 for MAE and 12.56 for RMSE) of A-horizon percentage of sand from the proposed approach are less than these from the detailed soil survey (10.59 for MAE and 15.12 for RMSE). Similar results were obtained for A-horizon percentage of clay. In addition, the difference between the results of multiple linear regression analysis without and with the MODIS derived variables further demonstrated the effectiveness of the variables at differentiating patterns of soil texture. These indicated that the proposed approach is effective for mapping the variation of soil texture over the low relief area and it could be used to map other soil property variation over similar areas.
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Soil polygon disaggregation through similarity-based prediction with legacy pedons [J].https://doi.org/10.1007/s40333-016-0087-7 URL [本文引用: 1] |
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Mapping the three-dimensional distribution of soil organic matter across a subtropical hilly landscape [J].https://doi.org/10.2136/sssaj2012.0317 URL [本文引用: 1] 摘要
There is a serious lack of detailed and accurate three-dimensional soil distribution information worldwide. This study examined the effectiveness of combining radial basis function (RBF) neural networks and profile depth functions to map the three-dimensional distribution of soil organic matter (SOM) in a subtropical hilly landscape in southern Anhui Province, China. The RBF networks were used to predict the lateral distribution of SOM based on its relations with terrain attributes and land uses, while the depth functions were used to fit its vertical distribution based on sparse measurements of SOM in soil genetic horizons. Compared with power and logarithmic functions, the equal-area quadratic splines had smaller bias, higher accuracy, and more stable performance in fitting the vertical SOM distribution. The prediction accuracy of the whole three-dimensional mapping method decreased with depth within the upper 60 cm, while the best accuracy occurred below 60 cm. In the upper 30 cm, areas with high elevation tended to have high predicted SOM content and vice versa. There were local deviations from this pattern in areas where toeslopes and ravines had higher predicted SOM content than backslopes, even though the latter are at higher elevations. Multiple regressions with dummy variables showed that the influence of terrain conditions on SOM content was strong in the upper 60 cm and weak below 60 cm, while that of land use was strong in the upper 30 cm and weak below 30 cm. Both influences were the strongest in the upper 15-cm soil layer. Under the same terrain conditions, agricultural cultivation is associated with SOM accumulation in the upper 30 cm.
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Mapping continuous depth functions of soil carbon storage and available water capacity [J].https://doi.org/10.1016/j.geoderma.2009.10.007 URL [本文引用: 1] 摘要
There is a need for accurate, quantitative soil information for natural resource planning and management. This information shapes the way decisions are made as to how soil resources are assessed and managed. This paper proposes a novel method for whole-soil profile predictions (to 1 m) across user-defined study areas where limited soil information exists. Using the Edgeroi district in north-western NSW as the test site, we combined equal-area spline depth functions with digital soil mapping techniques to predict the vertical and lateral variations of carbon storage and available water capacity (AWC) across the 1500 km 2 area. Neural network models were constructed for both soil attributes to model their relationship with a suite of environmental factors derived from a digital elevation model, radiometric data and Landsat imagery. Subsequent fits of the models resulted in an R 2 of 44% for both carbon and AWC. For validation at selected model depths, R 2 values ranged between 20 and 27% for carbon prediction (RMSE: 0.30 0.52 log (kg/m 3)) and between 8 and 29% for AWC prediction (RMSE: 0.01 m/m). Visually, reconstruction of splines at selected validation data points indicated an average fit with raw data values. In order to improve upon our model and validation results there is a need to address some of the structural and metrical uncertainties identified in this study. Nevertheless, the resulting geo-database of quantitative soil information describing its spatial and vertical variations is an example of what can be generated with this proposed methodology. We also demonstrate the functionality of this geo-database in terms of data enquiry for user-defined queries.
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On digital soil mapping [J].https://doi.org/10.1016/S0016-7061(03)00223-4 URL [本文引用: 3] |
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Impact of multi-scale predictor selection for modeling soil properties [J].https://doi.org/10.1016/j.geoderma.2014.09.018 URL [本文引用: 1] 摘要
61Potentially useful predictors for digital soil mapping are often overlooked.61Different analysis scales should be treated as unique predictor variables.61The use of multi-scale predictor variables can greatly increase model performance.61Experimentation with subsets of predictor pools for data mining tools can be productive.
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A conditioned Latin hypercube method for sampling in the presence of ancillary information [J].https://doi.org/10.1016/j.cageo.2005.12.009 URL [本文引用: 1] 摘要
This paper presents the conditioned Latin hypercube as a sampling strategy of an area with prior information represented as exhaustive ancillary data. Latin hypercube sampling (LHS) is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. It provides a full coverage of the range of each variable by maximally stratifying the marginal distribution. For conditioned Latin hypercube sampling (cLHS) the problem is: given N sites with ancillary variables ( X ), select x a sub-sample of size n 02 (n87N) ( n 87 N ) mathContainer Loading Mathjax in order that x forms a Latin hypercube, or the multivariate distribution of X is maximally stratified. This paper presents the cLHS method with a search algorithm based on heuristic rules combined with an annealing schedule. The method is illustrated with a simple 3-D example and an application in digital soil mapping of part of the Hunter Valley of New South Wales, Australia. Comparison is made with other methods: random sampling, and equal spatial strata. The results show that the cLHS is the most effective way to replicate the distribution of the variables.
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Soil attribute prediction using terrain analysis [J].https://doi.org/10.2136/sssaj1993.03615995005700020026x URL [本文引用: 1] |
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Estimating soil moisture at the watershed scale with satellite-based radar and land surface models [J].https://doi.org/10.5589/m04-043 URL [本文引用: 1] 摘要
Spatially distributed soil moisture profiles are required for watershed applications such as drought and flood prediction, crop irrigation scheduling, pest management, and determining mobility with lightweight vehicles. Satellite-based soil moisture can be obtained from passive microwave, active microwave, and optical sensors, although the coarse spatial resolution of passive microwave and the inability to obtain vertically resolved information from optical sensors limit their usefulness for watershed-scale applications. Active microwave sensors such as synthetic aperture radar (SAR) currently represent the best approach for obtaining spatially distributed surface soil moisture at scales of 10 100 m for watersheds ranging from 1 000 to 25 000 km2. Although SAR provides surface soil moisture, the applications listed above require vertically resolved soil moisture profiles. To obtain distributed soil moisture profiles, a combined approach of calibration and data assimilation in soil vegetation atmosphere transfer (SVAT) models based on recent advances in soil physics is the most promising avenue of research. This review summarizes the state of the science using current satellite-based sensors to determine watershed-scale surface soil moisture distribution and the state of combining SVAT models with data assimilation and calibration approaches for the estimation of profile soil moisture. The basic conclusion of this review is that currently orbiting SAR sensors combined with available SVAT models could provide distributed profile soil moisture information with known accuracy at the watershed scale. The priority areas for future research should include image-based approaches for mapping surface roughness, determination of soil moisture in densely vegetated sites, active and passive microwave data fusion, and joint calibration and data assimilation approaches for a combined remote sensing modeling system. For validation, a worldwide in situ soil moisture monitoring program should be implemented. Finally, to realize the full potential of satellite-based soil moisture estimation for watershed applications, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost.
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The use of remote sensing in soil and terrain mapping: A review [J].https://doi.org/10.1016/j.geoderma.2010.12.018 URL [本文引用: 1] 摘要
78 Remote sensing offers possibilities for improving current soil databases. 78 Soil attribute retrievals from remote sensing should be used as covariates in DSM. 78 The gap between proximal and remote sensing has to be bridged. 78 We will be seeing future instruments launched soon enhancing the perspectives of DSM. 78 A coherent multidisciplinary method for soil and terrain mapping should be developed.
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Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs [J].https://doi.org/10.1016/j.geoderma.2011.07.017 URL [本文引用: 1] 摘要
78 Pre-processing of NIR data did not improved carbon prediction accuracy in sandy soils. 78 Topographical features improved SC prediction accuracy in aerial data. 78 Aerial data produced higher prediction accuracy than NIR in independent test data sets. 78 Aerial photography with topography is preferable to spectral NIR for SC prediction in sandy soils.
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Surface soil moisture retrievals from remote sensing: Current status, products & future trends [J].https://doi.org/10.1016/j.pce.2015.02.009 URL [本文引用: 1] 摘要
It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.
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Controls on deep critical zone architecture: A historical review and four testable hypotheses [J].https://doi.org/10.1002/esp.4052 URL [本文引用: 1] 摘要
The base of Earth's critical zone (CZ) is commonly shielded from study by many meters of overlying rock and regolith. Though deep CZ processes may seem far removed from the surface, they are vital in shaping it, preparing rock for infusion into the biosphere and breaking Earth materials down for transport across landscapes. This special issue highlights outstanding challenges and recent advances of deep CZ research in a series of articles that we introduce here in the context of relevant literature dating back to the 1500s. Building on several contributions to the special issue, we highlight four exciting new hypotheses about factors that drive deep CZ weathering and thus influence the evolution of life-sustaining CZ architecture. These hypotheses have emerged from recently developed process-based models of subsurface phenomena including: fracturing related to subsurface stress fields; weathering related to drainage of bedrock under hydraulic head gradients; rock damage from frost cracking due to subsurface temperature gradients; and mineral reactions with reactive fluids in subsurface chemical potential gradients. The models predict distinct patterns of subsurface weathering and CZ thickness that can be compared with observations from drilling, sampling and geophysical imaging. We synthesize the four hypotheses into an overarching conceptual model of fracturing and weathering that occurs as Earth materials are exhumed to the surface across subsurface gradients in stress, hydraulic head, temperature, and chemical potential. We conclude with a call for a coordinated measurement campaign designed to comprehensively test the four hypotheses across a range of climatic, tectonic and geologic conditions.
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Digital soil map of the world [J].https://doi.org/10.1126/science.1175084 URL [本文引用: 1] |
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Evaluation of laser induced breakdown spectroscopy for cadmium determination in soils [J].https://doi.org/10.1016/j.sab.2009.07.030 URL [本文引用: 1] 摘要
Cadmium is known to be a toxic agent that accumulates in the living organisms and present high toxicity potential over lifetime. Efforts towards the development of methods for microanalysis of environmental samples, including the determination of this element by graphite furnace atomic absorption spectrometry (GFAAS), inductively coupled plasma optical emission spectrometry (ICP OES), and inductively coupled plasma-mass spectrometry (ICP-MS) techniques, have been increasing. Laser induced breakdown spectroscopy (LIBS) is an emerging technique dedicated to microanalysis and there is a lack of information dealing with the determination of cadmium. The aim of this work is to demonstrate the feasibility of LIBS for cadmium detection in soils. The experimental setup was designed using a laser Q-switched (Nd:YAG, 10 Hz, = 1064 nm) and the emission signals were collimated by lenses into an optical fiber coupled to a high-resolution intensified charge-coupled device (ICCD)-echelle spectrometer. Samples were cryogenically ground and thereafter pelletized before LIBS analysis. Best results were achieved by exploring a test portion (i.e. sampling spots) with larger surface area, which contributes to diminish the uncertainty due to element specific microheterogeneity. Calibration curves for cadmium determination were achieved using certified reference materials. The metrological figures of merit indicate that LIBS can be recommended for screening of cadmium contamination in soils.
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Soil Moisture downscaling algorithm for combining radar and radiometer observations for SMAP mission [C]// |
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Soil survey manual [M]. |
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Mapping soil organic carbon content by geographically weighted regression: A case study in the Heihe River Basin, China [J].https://doi.org/10.1016/j.geoderma.2015.06.024 URL [本文引用: 1] 摘要
In large heterogeneous areas the relationship between soil organic carbon (SOC) and environmental covariates may vary throughout the area, bringing about difficulty for accurate modeling of the regional SOC variation. The benefit of local, geographically weighted regression (GWR) coefficients was tested in a case study on soil organic carbon mapping across a 50,810 km 2 area in northwestern China. This area is composed of an alpine ecosystem in the upper reaches and oases in the middle reaches. The benefit was quantified by comparing the quality of the maps obtained by GWR and geographically weighted ridge regression (GWRR) on the one side and multiple linear regression (MLR) on the other side. In these methods spatial dependence of model residuals is ignored. The root mean squared error (RMSE) of predictions of natural log-transformed SOC obtained with GWR was smaller than with MLR: 0.565 versus 0.618 g/kg. The use of a local ridge parameter in GWRR did not lead to an increase in accuracy. Besides we compared the quality of maps obtained by geographically weighted regression followed by simple kriging of model residuals (GWRSK) and kriging with an external drift (KED) with global regression coefficients. In these methods the spatial dependence of model residuals is incorporated in the model. The RMSE with KED was smaller than with GWRSK: 0.515 versus 0.546 g/kg. We conclude that fitting regression coefficients locally as in GWR only paid when no spatial random effect was included in the model. When a spatial random effect was included, the flexibility of local, geographically weighted regression coefficients was not needed and even undesirable as it led to less accurate predictions than KED with global regression coefficients. In comparing the accuracy of prediction methods by leave-one-out cross-validation (LOOCV) of a non-probability sample it is important to account for possible autocorrelation of pairwise differences in the prediction errors. The effective sample sizes were substantially smaller than the total number of sampling points, so that most pairwise differences in MSE were not significant at a significance level of 10% in a two-sided paired t -test.
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| [58] |
Mineral dissolution rates in plot-scale field and laboratory experiments [J].https://doi.org/10.1016/0009-2541(93)90118-3 URL [本文引用: 1] 摘要
In recent years, much research has been focused on the mechanisms by which acid deposition from the atmosphere is neutralized as it passes through soil. Although there are several short-term mechanisms of acid buffering, the dominant long-term mechanism is mineral weathering. Understanding the rates at which minerals weather in the soil is thus essential for predicting the long-term effects of acid deposition on surface-water chemistry. Mineral dissolution rates were measured on identical mineral material in field and laboratory experiments. Field dissolution rates were measured in 6 small (2 sq m) plots on a spodosol in eastern Maine, USA. The plots were irrigated with HCl at pH's 2, 2.5 and 3; soil solutions were collected by tension lysimeters at 25-cm depth. The composition of the soil solutions, together with the grain-size distribution and mineralogy of the soil, were used to calculate mineral dissolution rates. Laboratory dissolution experiments were performed on the 75-150 micrometer size fraction of soil from the site in flow-through reactors at pH-values corresponding to the pH of the bulk soil solution. The use of small plots and 'untreated' minerals from the same plots eliminates many of the uncertainties encountered in previous field-laboratory comparisons.
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| [59] |
Using data mining to model and interpret soil diffuse reflectance spectra [J].https://doi.org/10.1016/j.geoderma.2009.12.025 URL [本文引用: 1] 摘要
The aims of this paper are: to compare different data mining algorithms for modelling soil visible–near infrared (vis–NIR: 350–250002nm) diffuse reflectance spectra and to assess the interpretability of the results. We compared multiple linear regression (MLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), support vector machines (SVM), random forests (RF), boosted trees (BT) and artificial neural networks (ANN) to estimate soil organic carbon (SOC), clay content (CC) and pH measured in water (pH). The comparisons were also performed using a selected set of wavelet coefficients from a discrete wavelet transform (DWT). Feature selection techniques to reduce model complexity and to interpret and evaluate the models were tested. The dataset consists of 1104 samples from Australia. Comparisons were made in terms of the root mean square error (RMSE), the corresponding R 2 and the Akaike Information Criterion (AIC). Ten-fold-leave-group out cross validation was used to optimise and validate the models. Predictions of the three soil properties by SVM using all vis–NIR wavelengths produced the smallest RMSE values, followed by MARS and PLSR. RF and especially BT were out-performed by all other approaches. For all techniques, implementing them on a reduced number of wavelet coefficients, between 72 and 137 coefficients, produced better results. Feature selection (FS) using the variable importance for projection (FS VIP ) returned 29–31 selected features, while FS MARS returned between 11 and 14 features. DWT–ANN produced the smallest RMSE of all techniques tested followed by FS VIP –ANN and FS MARS –ANN. However, both the FS VIP –ANN and FS MARS –ANN models used a smaller number of features for the predictions than DWT–ANN. This is reflected in their AIC, which suggests that, when both the accuracy and parsimony of the model are taken into consideration, the best SOC model was the FS MARS –ANN, and the best CC and pH models were those from FS VIP –ANN. Analysis of the selected bands shows that: (i) SOC is related to wavelengths indicating C―O, C═O, and N―H compounds, (ii) CC is related to wavelengths indicating minerals, and (iii) pH is related to wavelengths indicating both minerals and organic material. Thus, the results are sensible and can be used for comparison to other soils. A systematic comparison like the one presented here is important as the nature of the target function has a strong influence on the performance of the different algorithms.
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| [60] |
Novel proximal sensing for monitoring soil organic C stocks and condition [J].https://doi.org/10.1021/acs.est.7b00889 URL PMID: 28414454 [本文引用: 1] 摘要
Abstract Soil information is needed for environmental monitoring to address current concerns over food, water and energy securities, land degradation, and climate change. We developed the Soil Condition ANalysis System (SCANS) to help address these needs. It integrates an automated soil core sensing system (CSS) with statistical analytics and modeling to characterize soil at fine depth resolutions and across landscapes. The CSS's sensors include a -ray attenuation densitometer to measure bulk density, digital cameras to image the measured soil, and a visible-near-infrared (vis-NIR) spectrometer to measure iron oxides and clay mineralogy. The spectra are also modeled to estimate total soil organic carbon (C), particulate, humus, and resistant organic C (POC, HOC, and ROC, respectively), clay content, cation exchange capacity (CEC), pH, volumetric water content, available water capacity (AWC), and their uncertainties. Measurements of bulk density and organic C are combined to estimate C stocks. Kalman smoothing is used to derive complete soil property profiles with propagated uncertainties. The SCANS provides rapid, precise, quantitative, and spatially explicit information about the properties of soil profiles with a level of detail that is difficult to obtain with other approaches. The information gained effectively deepens our understanding of soil and calls attention to the central role soil plays in our environment.
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| [61] |
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties [J].https://doi.org/10.1016/j.geoderma.2005.03.007 URL [本文引用: 2] 摘要
Historically, our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis. There is a global thrust towards the development of more time- and cost-efficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Diffuse reflectance spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. Spectroscopy is rapid, timely, less expensive, non-destructive, straightforward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterisation of various soil properties and the techniques are adaptable for ‘on-the-go’ field use. The aims of this paper are threefold: (i) determine the value of qualitative analysis in the visible (VIS) (400–700 nm), near infrared (NIR) (700–2500 nm) and mid infrared (MIR) (2500–25,000 nm); (ii) compare the simultaneous predictions of a number of different soil properties in each of these regions and the combined VIS–NIR–MIR to determine whether the combined information produces better predictions of soil properties than each of the individual regions; and (iii) deduce which of these regions may be best suited for simultaneous analysis of various soil properties. In this instance we implemented partial least-squares regression (PLSR) to construct calibration models, which were independently validated for the prediction of various soil properties from the soil spectra. The soil properties examined were soil pH Ca , pH w , lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity (CEC), exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate–nitrogen (NO 3 –N), available phosphorus (P Col ), exchangeable potassium (K) and electrical conductivity (EC). Our results demonstrated the value of qualitative soil interpretations using the loading weight vectors from the PLSR decomposition. The MIR was more suitable than the VIS or NIR for this type of analysis due to the higher incidence spectral bands in this region as well as the higher intensity and specificity of the signal. Quantitatively, the accuracy of PLSR predictions in each of the VIS, NIR, MIR and VIS–NIR–MIR spectral regions varied considerably amongst properties. However, more accurate predictions were obtained using the MIR for pH, LR, OC, CEC, clay, silt and sand contents, P and EC. The NIR produced more accurate predictions for exchangeable Al and K than any of the ranges. There were only minor improvements in predictions of clay, silt and sand content using the combined VIS–NIR–MIR range. This work demonstrates the potential of diffuse reflectance spectroscopy using the VIS, NIR and MIR for more efficient soil analysis and the acquisition of soil information.
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| [62] |
Digital soil morphometrics of krotovinas in a deep Alfisol derived from loess in Shenyang, China [J].https://doi.org/10.1016/j.geoderma.2017.04.010 URL [本文引用: 1] 摘要
Abstract Krotovinas, burrows of small mammals, occur widely in the soils of the steppes. The distribution and effect of krotovinas on soil organic carbon (SOC) stocks were studied in a deep profile (450 cm) at an archaeological site of the Palaeolithic period at Shenyang Agricultural University of China. It was found krotovinas occurred at all depths, and there was variation in their distribution and size. The density of krotovinas increased with depth, and was the highest (10/m ) at 250 cm depth. The average area of krotovinas' sections in most part of the profile ranged from 100 to 160 cm . Krotovinas covered an area of 7 to 17% of the profile wall. Average SOC concentration in the krotovinas was 5.3 g/kg, whereas it was 4.5 g/kg in the surrounding soil matrix. SOC stocks at 100 to 450 cm accounted for 72% of total SOC stocks at 0 to 450 cm depth. Total SOC stock in the whole profile (0 to 450 cm) was 295 t/ha considering the presence of krotovinas, whereas it was 274 t/ha when the krotovinas were excluded. This study shows how krotovinas can be quantified, and that krotovinas can affect SOC concentration and change patterns of SOC distribution. The amount of SOC stored in deep soils should be taken into consideration for evaluating SOC stocks.
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| [63] |
Silicon cycling by plant and its effects on soil Si translocation in a typical subtropical area [J].https://doi.org/10.1016/j.geoderma.2017.08.014 URL [本文引用: 1] |
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Estimating soil acidification rate at watershed scale based on the stoichiometric relations between silicon and base cations [J].https://doi.org/10.1016/j.chemgeo.2012.11.009 URL [本文引用: 2] 摘要
Soil acidification rate is an essential parameter to assess the vulnerability and sustainability of various ecosystems to acid deposition. The add-buffering mechanism of soil system includes two major proton (H+) consumption pathways, i.e., mineral weathering and cation exchange in which the former consumes directly H+ and does not lead to soil acidification while the latter would cause soil acidification by exporting base cations. However, no available method distinguishes between H+ consumed by the two pathways, so soil acidification may have been overestimated as the bulk H+ consumption. Here we establish a method to differentiate the two major H+ consumption pathways quantitatively using the stoichiometric relations between silicon (Si) and base cations of rock, soil and stream water in a typical undisturbed forested watershed in subtropical China. The results showed that a large quantity of external H+ was stored in soil from acid deposition, and the H+ concentration in soil was further affected by the transformations and specific adsorptions of extraneous nitrogen (N) and sulfur (S) compounds. The net input of H+ into the soil resulting from both wet and dry deposition was about 1395 mol ha(-1) yr(-1). The base cations from weathering was about 46% of the total net output of cations as calculated using the net output of Si by stream and the release ratio of base cations over Si during the weathering reaction of plagioclase, the predominant weatherable mineral in the area. The remaining output of base cations was attributed to cation exchange. Based on the ratio of H+ consumption by weathering and cation exchange, the actual soil acidification caused by cation exchange was estimated as 703 mol ha(-1) yr(-1), only about half of the net input of protons, which was substantially lower than the previous results obtained with no distinction between H+ consuming pathways. However, even taking the contribution of mineral weathering into account, the high net output of base cations indicates that the already acidic soil system is still depleting its base pools. This study provides a solution to estimate actual soil capacity to resist acid attack, which is crucial for reliable assessment of ecosystem resilience against external acid input. (C) 2012 Elsevier B.V. All rights reserved.
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| [65] |
Updating conventional soil maps through digital soil mapping [J].https://doi.org/10.2136/sssaj2010.0002 URL [本文引用: 1] 摘要
ABSTRACT Conventional soil maps, as the major data source for information on the spatial variation of soil, are limited in terms of both the level of spatial detail and the accuracy of soil attributes. These soil maps, however, contain valuable knowledge on soil-environment relationships. Such knowledge can be extracted for updating conventional soil maps through the use of available high-quality data on environmental variables and data analysis techniques. We developed a method to update conventional soil maps using digital soil mapping techniques without additional field work, which can be used in situations where the study area contains no or few soil profile descriptions at points. The basis of the method is that soil polygons on a conventional soil map correspond to landscape units, which can be considered as combinations of environmental factors. Such environmental combinations were approximated through fuzzy clustering on the environmental factors. We extracted the knowledge on soil-environment relationships by relating the environmental combinations to the mapped soil types. The extracted knowledge was then used for soil mapping using the Soil Land Inference Model (SoLIM) framework. This method was demonstrated through a case study for updating a conventional 1:20,000 soil map of Wakefield, NB, Canada. The case study showed that the updated digital soil map contained much greater spatial detail than the conventional soil map. Field validation indicated that the accuracy of the updated soil map was much higher than the conventional soil map at the level of soil associations with drainage classes, indicating that the proposed method is an effective approach to updating conventional soil maps.
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| [66] |
Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment [J].https://doi.org/10.1016/j.scitotenv.2017.05.055 URL PMID: 28535588 [本文引用: 1] 摘要
Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 102m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R 2 02=020.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.6702kg02m 61022 and ME: 61020.33 to 61020.2902kg02m 61022 ). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.4102Tg in the upper 3002cm, 113.1502Tg in the upper 5002cm and 190.3002Tg in the upper 102m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas.
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| [67] |
Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem [J].https://doi.org/10.1016/j.ecolind.2015.08.036 URL [本文引用: 1] 摘要
Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3gkg 1and 26.6gkg 1, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.
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| [68] |
A cost-constrained sampling strategy in support of LAI product validation in mountainous areas [J].https://doi.org/10.3390/rs8090704 URL [本文引用: 1] 摘要
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas.
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| [69] |
The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method [J].https://doi.org/10.1016/j.ecolind.2016.08.023 URL [本文引用: 1] 摘要
Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low-relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0–4002mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude ( Pearson ’ s r between root-mean squared error of prediction and rainfall magnitude02=02610.943 for percentage of sand and 610.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 2002mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying.
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| [70] |
Recent progress and future prospect of digital soil mapping: A review [J].https://doi.org/10.1016/S2095-3119(17)61762-3 URL [本文引用: 2] |
| [71] |
Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index [J].https://doi.org/10.1016/j.ecolind.2013.12.015 URL [本文引用: 1] 摘要
Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil andscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28km 28km and Jiangyan County, 38km 50km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3km in Jiangyan but 16km in Peixian, and accuracy of best models differed by a factor of two (3.3g/kg and 6.3g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.
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| [72] |
Automated soil inference under fuzzy logic [J].https://doi.org/10.1016/0304-3800(95)00161-1 URL [本文引用: 1] 摘要
Abstract Soil information is essential to any terrestrial ecological modelling and management activity. Polygon soil maps produced from soil surveys are currently the major source of information on the spatial distribution of soil properties for a variety of land analysis and management activity. However, there are some major problems regarding the use of current soil maps in geographic analysis and especially in geographic information systems (GIS). These problems include limited coverage at a fixed scale, locational errors, attribute errors, and insufficient information in the mapping units. Much of these problems are due to the crisp logic and cartographic techniques with which soil maps are produced. Under crisp logic standardly used in soil classification and mapping, an area belongs to one and only one soil mapping unit, and is separated from other mapping units by sharp boundary lines. However, soil in a landscape is a continuum and the discretization of such a continuum into distinct spatial and categorical groups results in a significant loss of information.This paper presents a methodology to infer and represent information on the spatial distribution of soil. The methodology combines fuzzy logic with GIS and expert system development techniques to infer soil series from environmental conditions. The methodology for every point in an area produces a soil similarity vector (SSV) showing the similarity of the soil at the point to a prescribed set of soil series. The SSV produced from this methodology can be used to infer local soil properties at values intermediate to the typical or central values assigned to each possible series. Preliminary results from the methodology using a limited set of environmental variables for an experimental watershed in Montana are presented.
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| [73] |
Assessment of plant-driven mineral weathering in an aggrading forested watershed in subtropical China [J].https://doi.org/10.1016/S1002-0160(15)60069-8 URL [本文引用: 2] 摘要
植物生长贡献矿物质捱过，但是这贡献仍然保持糟糕理解。在副热带的中国在一个 aggrading forested 分水岭捱过率借助于 geochemical 团平衡被学习。降雨，干燥免职，和 streamwater 从 2007 年 3 月被监视到 2012 年 2 月。植物的部件，降雨，干燥免职， streamwater，代表性的土壤，和父母岩石的样品 stoichiometrically 为集体平衡计算和澄清的植物驱动的捱过的机制被收集并且决定。忽略生物资源，捱过 Na + , 和 Si 是的 Ca 2+ , Mg 2+ , 评价 25.6， 10.7， 2.8，和 51.0 kg 哈 1 年 1 , 分别地。考虑生物资源，捱过 Ca 2+ , Mg 2+ , 和 Si 和捱过的和的率 Ca 2+ , Mg 2+ , Na + , K + , 和 Si 评价是 2.6， 1.8， 1.2，并且比分别地忽略生物资源的那些高的 1.5 褶层。这被归因于由于营养素的植物驱动的捱过(例如，由植被的 Ca 2+ , Mg 2+ ,和 K + )吸收和实质的质子生产在这些营养素的吸收期间，与为搬迁捱过产品和后者的作为泵的以前的行动是捱过代理人的来源增溶的矿物质部件。捱过的一样的模式，即，捱过与的更高的率比没有在集体平衡计算包括生物资源，在以前的研究被报导；然而，到哪个种捱过率的开车的程度与植被类型和气候的地区变化了。记录生物捱过由植物开车被期望在调整在地球批评地区以内的滋养的骑车和材料流动起一个关键作用。
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