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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:

第47卷

期数:

2025年第03期

页码:

513-525

栏目:

黄河流域生态保护和高质量发展专刊(上)

出版日期:

2025-05-15

文章信息/Info

Title:

Soil Moisture Retrieved with CYGNSS Data in Lower Yellow River Region, China from 2020 to 2023 and Its Response to Extreme Precipitation Event

文章编号:

1672-6561(2025)03-0513-13

作者:

龚劲章¹; 2; 3; 胡羽丰¹; 2; 4*

(1.长安大学 地质工程与测绘学院,陕西 西安 710054; 2.黄土科学全国重点实验室,陕西 西安 710054; 3.长安大学 地学与卫星大数据研究中心,陕西 西安 710054; 4.长安大学 西部矿产资源与地质工程教育部重点实验室,陕西 西安 710054)

Author(s):

GONG Jin-zhang¹; 2; 3;  HU Yu-feng¹; 2; 4*

(1.School of Geological Engineering and Geomatics,Chang'an University,Xi'an 710054,Shaanxi,China; 2.State Key Laboratory of Loess Science,Xi'an 710054,Shaanxi,China; 3.Big Data Center for Geosciences and Satellites,Chang'an University,Xi'an 710054,Shaanxi,China; 4.Key Laboratory of Western China's Mineral Resources and Geological Engineering of Ministry of Education,Chang'an University,Xi'an 710054,Shaanxi,China)

关键词:

GNSS-R技术;  土壤湿度;  旋风全球导航卫星系统;  GNSS-R RSC模型;  降雨响应;  黄河

Keywords:

GNSS-R technology;  soil moisture;  CYGNSS;  GNSS-R RSC model;  precipitation response;  Yellow River

分类号:

P333; P237

DOI:

10.19814/j.jese.2024.11037

文献标志码:

A

摘要:

黄河下游区域是我国重要的粮食生产基地,获取大范围高时空分辨率的土壤湿度有助于生态保护和精准农业发展,对推动黄河流域生态保护和高质量发展具有重要意义。目前,星载GNSS-R技术已经成为大范围地表土壤湿度反演的一个有效手段。基于旋风全球导航卫星系统(CYGNSS)的GNSS-R数据,首次提出利用CYGNSS卫星的相干比率参数表征地表粗糙度,提出了星载GNSS-R粗糙度衰减自修正土壤湿度反演模型,即GNSS-R RSC模型,并反演得到黄河下游区域2020～2023年的9 km空间分辨率地表土壤湿度。以SMAP土壤湿度数据作为参考,反演得到的土壤湿度产品均方根误差(RMSE)和相关系数(R)分别为0.051 cm³·cm^-3和0.75。将该土壤湿度升尺度至36 km空间分辨率时,均方根误差为0.048 cm³·cm^-3,相关系数为0.80,较36 km空间分辨率CYGNSS官方土壤湿度,均方根误差降低了50%,相关系数提高了73%。利用生成的土壤湿度产品进行降雨响应分析,结果显示该模型得到的土壤湿度与研究区域降雨季节变化趋势基本一致,月均降水量与土壤湿度相关系数为0.76,且在2021年河南“7·20”极端降雨事件中有较为敏感的响应。

Abstract:

The lower Yellow River region, a vital grain production area in China, requires high temporal-spatial resolution soil moisture(SM)data to support ecological conservation and precision agriculture. This is essential for advancing ecological protection and high-quality development in Yellow River Basin. Currently, spaceborne GNSS-R technology has emerged as an effective approach for large-scale soil moisture retrieval. Based on GNSS-R data from CYGNSS, the CYGNSS-derived coherency ratio was proposed to characterize the surface roughness, and GNSS-R RSC model, a spaceborne GNSS-R roughness attenuation self-correction soil moisture retrieval model, was proposed. Using this model, the soil moisture from 2020 to 2023 for the lower Yellow River region at a 9 km spatial resolution was derived. Using SMAP soil moisture data as the reference, the soil moisture product derived achieves a root mean square error(RMSE)of 0.051 cm³·cm^-3 and a correlation coefficient(R)of 0.75. After upscaling the soil moisture data to a 36 km spatial resolution, the RMSE and R improve to 0.048 cm³·cm^-3 and 0.80, respectively, representing a 50% reduction in RMSE and a 73% increase in R compared to the official CYGNSS soil moisture product at the same resolution. Precipitation response shows that the retrieval soil moisture product closely matches seasonal precipitation trends, with a monthly R of 0.76; the model also demonstrates high sensitivity to extreme precipitation events, such as the extreme rainstorm in Henan province in July 20th, 2021.

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相似文献/References:

备注/Memo

备注/Memo:

收稿日期:2024-11-29; 修回日期:2025-01-16
基金项目:国家自然科学基金项目(42041006); 陕西省自然科学研究计划项目(2024JC-YBMS-197); 长安大学中央高校基本科研业务费专项资金项目(300102263203,300104240914); 陕西省地学大数据与地质灾害防治创新团队项目(2022)
*通信作者:胡羽丰(1989-),男,湖北应城人,长安大学副教授,工学博士,E-mail:yfhu@chd.edu.cn。

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