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基于遥感影像数据的土壤制图研究进展

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  • NETL
  • NSTL
  • 万方数据
[目的]数字土壤制图成本低、精度高，是刻画土壤环境变量的新技术和新工具，遥感技术在数字土壤制图业务化应用中发挥了重要作用，但是还存在诸多问题和不确定性。文章总结相关研究进展，以期为基于遥感影像数据的土壤数字制图提供有益参考。[方法]梳理遥感技术在土壤类型与属性制图研究中的进展情况并进行总结展望，以期更好地发挥高空间、时间、光谱分辨率遥感数据在土壤数字制图方面的作用，为研发新模型、提高土壤制图模型精度提供信息支撑，为第三次全国土壤普查数字土壤制图提供有益参考。[结果]遥感数据的特征与土壤发生学理论契合并推动了土壤制图的发展，但是，基于遥感的土壤制图技术在快速土壤环境变量因子提取、自适应制图指标筛选、满足多层级制图需求的土壤推测制图模型构建等方面还需要进一步深入探讨。[结论]为了实现土壤类型与属性制图的业务化、流程化和自动化，未来的研究应该着眼于从"环境变量分析—评价指标体系筛选—预测模型构建"一体化解决方案入手，探讨如何融合多种遥感载荷、综合运用新型模型，以提高数字土壤制图的精度，为农业生产和土地资源管理提供更为精准的信息支撑。
Research progress on soil mapping based on remote sensing image data
[Purpose]Digital soil mapping,with low cost and high accuracy,is a new technology and a new tool for portraying soil environmental variables.Remote sensing technology plays an important role in the operational application of digital soil mapping,but there are still many problems and uncertainties.This paper summarizes the progress of related research with the aim of providing useful references for digital soil mapping based on remotely sensed image data.[Method]This paper reviewed the progress of remote sensing technology in the study of soil type and property mapping and provided a summary and outlook,with a view to better exploiting the role of high spatial,temporal,and spectral resolution remote sensing data in digital soil mapping,providing information support for the development of new models,and improving the accuracy of soil mapping models for the upcoming third national soil survey.[Results]The study indicated that the characteristics of remote sensing data align with soil science theories and drive the development of soil mapping.However,further exploration was needed in areas such as rapid extraction of environmental variable factors,adaptive indicator selection and the construction of predictive models that meet the needs of multi-level mapping.[Conclusion]In order to achieve the operational,procedural and automated mapping of soil types and properties,future research should focus on an integrated solution from"environmental variable analysis-evaluation indicator system selection-predictive model construction",exploring how to integrate various remote sensing payloads and comprehensively apply new models to improve the accuracy of digital soil mapping to provide more accurate support for agricultural production and land resource management.

remote sensingsoil environmental variablesthird national soil surveyuncertaintydigital soil mapping

赵良军、杨颢、胡月明、刘振华、彭代亮、王璐、任必武、黄琦丹、彭小桃

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自然资源部华南热带亚热带自然资源监测重点实验室,广东广州 510663

四川轻化工大学计算机科学与工程学院,宜宾 644000

海南大学热带农林学院,海口 570228

华南农业大学资源环境学院,广东广州 510642

中国科学院空天信息创新研究院,北京 101408

广州市华南自然资源科学技术研究院,广东广州 510610

南京大学地理与海洋科学学院,江苏南京 210023

广东省国土资源测绘院,广州 510663

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遥感 土壤环境变量 第三次土壤普查 不确定性 数字土壤制图

自然资源部华南热带亚热带自然资源监测重点实验室开放基金高分辨率对地观测系统国家科技重大专项(民用部分)科研项目国家自然科学基金联合基金集成项目

2023NRMK0585-Y50G26-9001-22/23U1901601

2024

10.12105/j.issn.1672-0423.20240103

中国农业信息
中国农学会农业信息分会 中国农科院农业自然资源和农业区划研究所

中国农业信息

影响因子：1.424
ISSN：1672-0423
年,卷(期)：2024.36(1)
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