摘要
该文聚焦数字土壤制图技术,系统梳理其发展脉络,剖析核心技术,挖掘应用潜力,审视现存挑战并展望未来趋向.数字土壤制图技术早期囿于技术瓶颈,土壤制图依赖传统实地勘查与手工绘制,成效欠佳,自1970年代个人计算机技术萌芽后开启数字化尝试,1980年代至2010年代历经地理信息技术革新与建模技术突破,2010年代后,多源数据融合、人工智能赋能创新及全球协同合作共享渐次深化,推动数字土壤制图技术日臻成熟完善.数字土壤制图的核心技术架构具有传统与现代技术有机结合的特点,数据采集环节融合传统采样、遥感探测及近地传感等技术方法,全方位提升样本品质与数据丰富度、效率及分辨率;数据处理分析依托地统计学进阶与机器学习算法优化,强化土壤属性预测精密度、模型普适性与可阐释性;模型构建则将传统模型改良深度嵌入智能模型,模拟土壤过程细节、构建新颖算法框架,提升模拟效能与制图精度.数字土壤制图技术应用于精准农业,为施肥灌溉及田间管理精准赋能,增益农业综合效益与可持续性;应用于土地资源管理,指引土地规划布局、防治土壤侵蚀退化;应用于生态环境与气候变化研究,评估生态系统服务功能、支撑生物多样性保护与气候适应协同;应用于土壤调查与普查领域,驱动土壤普查技术体系升级、深化土壤三普数据价值.目前,数字土壤制图虽仍困于数据质量参差、不确定性高、模型精度局限、跨学科协作阻滞及知识融合路径狭窄等难题,但是,伴随人工智能、大数据、物联网前沿科技深度交融,数字土壤制图必将开启智能化、精细化、实时化新纪元,借人工智能洞悉数据潜能、凭大数据构筑共享平台、依物联网达成实时感知传输,持续拓宽应用边际、深化融合层次,为全球土壤资源稳健管理、生态和谐稳定、农业绿色发展及气候变化积极应对筑牢根基.
This study examines digital soil mapping(DSM)technology by systematically reviewing its developmental trajectory,analyzing core methodologies,evaluating application potential,addressing current challenges,and forecasting future trends.Initially constrained by technological limitations,DSM relied on traditional field surveys and manual cartography,resulting in suboptimal outcomes.The introduction of personal computing in the 1970s catalyzed early digitization efforts,leading to transformative advancements in geographic information systems(GIS)and modeling innovations from the 1980s to the 2010s.Post-2010,DSM matured through the fusion of multi-source data,artificial intelligence(AI)-driven innovation,and global collaborations.The technical framework of DSM integrates traditional and modern approaches:data acquisition combines conventional sampling,remote sensing,and proximal sensing to enhance data quality,diversity,operational efficiency,and spatial resolution;data processing applies advanced geostatistics and optimized machine learning algorithms to improve prediction accuracy,model generalizability,and interpretability;and model development merges traditional methods with intelligent frameworks,simulating soil processes and constructing novel algorithms to enhance simulation fidelity and mapping precision.DSM demonstrates extensive applications across multiple domains:precision agriculture—optimizing fertilization,irrigation,and field management to enhance agricultural productivity and sustainability;land resource management—facilitating spatial planning and preventing soil degradation;environmental-climate research—quantifying ecosystem services while safeguarding biodiversity through climate resilience strategies;and soil census innovation—modernizing survey methodologies and unlocking multidimensional value in Third National Soil Census datasets.Current challenges include inconsistent data quality,prediction uncertainties,limited model accuracy,barriers to cross-disciplinary collaboration,and insufficient integration of knowledge.However,the convergence of AI,big data,and Internet of Things(IoT)technologies is expected to drive DSM into a new era of intelligent,refined,and real-time applications:AI unlocks the full potential of data,big data fosters global sharing platforms,and IoT enables real-time sensing and data transmission.These innovations are set to expand DSM’s application scope,deepen interdisciplinary collaboration,and strengthen the foundations of sustainable soil management,ecological stability,green agriculture,and climate resilience.
作者
于雷
周雪妍
易军
刘目兴
梁嘉怡
李硕
YU Lei;ZHOU Xueyan;YI Jun;LIU Muxing;LIANG Jiayi;LI Shuo(Hubei Province Laboratory for Geographical Process Analyzing&Modeling,Wuhan 430079,China;College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China)
出处
《华中师范大学学报(自然科学版)》
北大核心
2025年第4期509-519,共11页
Journal of Central China Normal University:Natural Sciences
基金
国家自然科学基金项目(42171270).
关键词
数字土壤制图
土壤地理
技术演进
挑战与展望
digital soil mapping
soil geography
technological evolution
challenge and prospect
