首页|星载GNSS-R反演土壤湿度研究进展与思考

星载GNSS-R反演土壤湿度研究进展与思考

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土壤湿度作为陆地水循环中最为活跃的部分,在陆面水分的形成、转化和消耗过程中扮演重要角色,同时它也是影响水文过程、植被状态及气候条件的关键参数.传统的直接测量法耗时耗力,无法实现大范围监测需求,而光学遥感法易受云层、植被遮蔽,微波遥感法则无法兼顾时空分辨率.首先,介绍利用全球导航卫星系统反射测量(global naviga-tion satellite system-reflectometry,GNSS-R)技术反演土壤湿度的基本理论,并分析反演过程中出现的误差源以及现有的误差校正方法及其局限性.然后,从3个方面分析了近年来星载GNSS-R反演土壤湿度的研究进展:(1)星载GNSS-R星座发展现状;(2)星载GNSS-R反演土壤湿度的算法综述,包括经验模型、半经验模型和机器学习方法;(3)融合星载GNSS-R与其他数据反演土壤湿度的进展及方向.最后,讨论了 目前利用星载GNSS-R技术反演土壤湿度所面临的技术难点,并对未来研究重点进行展望.
Research Advances and Some Thoughts on Soil Moisture Retrieval by Space-Borne GNSS-R
As the most dynamic component of terrestrial water cycling,soil moisture plays a pivotal role in the formation,transformation,and consumption of surface water.Simultaneously,it serves as a crucial pa-rameter influencing hydrological processes,vegetation status,and climatic conditions.Therefore,high-precision,high spatiotemporal resolution soil moisture data holds significant importance across various fields,including agriculture,forestry,and meteorology.Presently,traditional methods such as the time-consuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring de-mands,highlighting the advantages of remote sensing methods.However,optical sensors are susceptible to cloud cover and vegetation obscuration,while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions.The space-borne global navigation satellite system-reflectome-try(GNSS-R)technology,characterized by short revisit cycles and pseudo-random sampling,presents a new opportunity for soil moisture retrieval.This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process.This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected sig-nals,as well as the impact of water bodies on reflectivity.Existing error correction methods and their limita-tions are summarized,and potential avenues for improvement are discussed.Subsequently,the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three per-spectives:(1)The development status of space-borne GNSS-R constellation.(2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval,including empirical models,semi-empirical models,and machine learning methods.(3)Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval.In conclusion,based on the scattering mechanism of reflected signals,the selection of auxiliary data,and the utilization of incident angle information,this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into fu-ture research directions.

space-borne GNSS-Rsoil moisturemachine learningCYGNSS

张双成、郭沁雨、马中民、刘奇、胡胜伟、周昕、赵贺斌

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长安大学地质工程与测绘学院,陕西 西安,710054

西部矿产资源与地质工程教育部重点实验室,陕西 西安,710054

地理信息工程国家重点实验室,陕西 西安,710054

星载GNSS-R 土壤湿度 机器学习 旋风全球导航卫星系统

国家自然科学基金国家重点研发计划地理信息工程国家重点实验室项目中央高校基本科研业务费专项中央高校基本科研业务费专项陕西省科技创新团队项目陕西省地学大数据与地质灾害防治创新团队项目(2022)

420740412019YFC1509802SKLGIE2022-ZZ2-073001022603013001022624012021TD-51

2024

武汉大学学报(信息科学版)
武汉大学

武汉大学学报(信息科学版)

CSTPCD北大核心
影响因子:1.072
ISSN:1671-8860
年,卷(期):2024.49(1)
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