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引用本文:

DOI:

10.11834/jrs.20233164

收稿日期:

2023-05-18

修改日期:

2023-10-09

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基于星地传感技术的土壤盐渍化监测:进展与展望
王敬哲1, 丁建丽2, 葛翔宇2, 彭杰3, 胡忠文4
1.深圳职业技术大学人工智能学院;2.新疆大学地理与遥感科学学院;3.塔里木大学农学院;4.深圳大学自然资源部大湾区地理环境监测重点实验室
摘要:

土壤盐渍化是全球面临的土壤退化和环境恶化的共性问题。近地传感、卫星遥感与机载遥感等星地传感技术的蓬勃发展使得高效且准确的土壤盐分周期监测成为可能,并为土壤盐渍化研究提供了坚实技术支撑。本文针对土壤盐渍化星地传感监测的发展进程,以“数据-方法-应用”为脉络,详细分析梳理了土壤盐渍化监测的原理、主要数据源及主流方法;随后归纳总结当前技术体系的发展现状以及监测数据、监测方法及尺度效应等局限性;最后针对基于星地传感技术的土壤盐渍化监测的进一步发展提出了展望和设想,明确了多源星地数据融合的次生土壤盐渍化监测;依托多平台开展土壤盐渍化的多尺度协同监测;借助学科交叉加深土壤盐渍化的探测深度;基于云计算的土壤盐渍化共享数据集与平台开发等未来需要重点关注的研究方向。

Monitoring soil salinization based on remote sensing and proximal soil sensing: progress and perspective
Abstract:

As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture. Moreover, it has become one of the major environmental and socioeconomic issues globally. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing and proximal soil sensing technology has become important tools for rapid, accurate, and efficient acquisition and monitoring of soil salinization. The appropriate mapping methods are directly related to the spatial scale of interest. The need of regional soil salinity mapping was also one of the first published geostatistical applications. Macroscopic maps of salt affected soils at global scale may roughly illustrate the extent of the environmental problem, however regional or greater level assessments are based on remote sensing and geographic information systems coupled with ground measurements. It has become a trend to apply remote sensing technology to the monitoring of soil salinization to obtain soil salinization information. This article discussed the detection mechanisms, multi-source data, and methods for monitoring soil salinization. Multiple sensors installed on different platforms can provide considerable earth observation information with various temporal, spatial, and spectral resolutions. On the basis of height, the observation platforms can be divided into near ground (proximal), airborne and spaceborne remote sensing. In regard to the operating principle, these sensors can be mainly divided into electromagnetic sensors and optical/radiational sensors. Among them, spectral imaging, thermal infrared sensors are suitable for various observation platforms; while Ground Penetrating Radar (GPR) and Electromagnetic induction (EMI) are only suitable for near ground soil salinization monitoring. Specially, the mainstream methods can be categorized into: (1) thematic information extraction; (2) spectral indices development; (3) quantitative retrieval modelling and (4) digital soil mapping. On this basis, this review summarized and explained the limitations of the current research fields and framework, monitoring data, monitoring methods and scale effects. The integration of spaceborne remote sensing data with ground-based sensor information, complemented by the agile observational capabilities of unmanned aerial vehicles (UAVs), enables us to transcend the limitations of non-coordinated Earth observation techniques. This integration allows for comprehensive coverage from a broad-scale perspective down to specific localized points. In summary, the core of integration of satellite, UAV and proximal sensing for soil salinization monitoring lies in the fusion of data from diverse sources, the establishment of quantitative models, and the extension of spatial scales. Finally, for future development and actual application needs, this review discussed the prospect for the further development of soil salinization studies based on remote sensing- and proximal soil sensing. To further advance and optimize technology, analysis and retrieval methods, we identify critical future research needs and directions: (1) secondary soil salinization monitoring based on multi-source data fusion; (2) multi-scale collaborative monitoring soil salinization; (3) improving detection depth based on multi-disciplinary knowledge; and (4) shared research data and platform based on cloud computing.

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