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遥感技术与应用  2022, Vol. 37 Issue (6): 1404-1413    DOI: 10.11873/j.issn.1004-0323.2022.6.1404
土壤水分专栏     
Sentinel-1和Sentinel-2协同反演地表土壤水分
杜绍杰1,2(),赵天杰1(),施建成3,马春锋4,邹德富4,王振5,姚盼盼1,彭志晴1,郑景耀6
1.中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
2.中国科学院大学,北京 100049
3.中国科学院国家空间科学中心,北京 100190
4.中国科学院西北生态环境资源研究院,甘肃 兰州 730000
5.国家基础地理信息中心,北京 100830
6.河海大学 水文水资源学院,江苏 南京 210024
Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture
Shaojie Du1,2(),Tianjie Zhao1(),Jiancheng Shi3,Chunfeng Ma4,Defu Zou4,Zhen Wang5,Panpan Yao1,Zhiqing Peng1,Jingyao Zheng6
1.State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China
4.Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
5.National Basic Geographic Information Center,Beijing 100830,China
6.School of Hydrology and Water Resources,Hohai University,Nanjing 210024,China
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摘要:

土壤水分是水文循环、生态环境、气候变化等研究中的关键参数,获取高分辨率长时间序列的土壤水分信息对农业管理、作物生长监测等具有重要的意义,同时也是研究的难点。基于时间序列(2019年至2020年)的Sentinel-1雷达数据和Sentinel-2光学数据,构建了地表土壤水分的雷达与光学数据协同反演模型,即裸土条件下地表土壤水分的变化检测方法,并利用归一化植被指数对植被影响进行校正,实现了青藏高原多年冻土区(五道梁)100 m空间分辨率的土壤水分反演。与地面实际观测的土壤水分进行对比验证,结果表明土壤水分反演结果与地面实测数据的相关系数介于0.672与0.941之间,无偏均方根误差介于0.031 m3/m3与0.073 m3/m3之间,土壤水分变化与区域降水事件和特征密切相关,验证了本文提出的考虑植被物候的变化检测方法在地势平坦、植被稀疏的青藏高原地区具有极高的适用性。

关键词: 土壤水分变化检测多年冻土青藏高原Sentinel-1/2    
Abstract:

Soil moisture is a key parameter in the study of hydrological cycle, ecological environment, climate change, etc., The acquisition of high-resolution long time series soil moisture information is of great significance for agricultural management and crop growth monitoring, and remote sensing monitoring is also a difficult problem in research. Based on the Sentinel-1 radar data and Sentinel-2 optical data of the time series(2019—2020), this paper constructs a synergistic retrieval model of surface soil moisture, that is, a method for detecting changes in surface soil moisture under bare soil conditions, And the normalized vegetation index was used to correct the vegetation impact. The proposed method has achieved soil moisture mapping with a spatial resolution of 100 meters in the permafrost region (Wudaoliang) of the Qinghai-Tibet Plateau. The comparison and validation with the in-situ measured soil moisture observed show that the correlation coefficient between the soil moisture estimates and the ground measurements is 0.672≤R≤0.941, and the unbiased root mean square error (ubRMSE) is between 0.031 m3/m3 and 0.073 m3/m3. Soil moisture changes are closely related to regional precipitation events and characteristics, verifying that the change detection method proposed in this study has high applicability in the flat terrain and sparse vegetation areas on the Qinghai-Tibet Plateau.

Key words: Soil moisture    Change detection    Permafrost    Qinghai-Tibet Plateau    Sentinel-1/2
收稿日期: 2022-05-22 出版日期: 2023-02-15
ZTFLH:  S152.5  
基金资助: 第二次青藏高原综合科学考察研究专题“亚洲水塔区水循环动态监测与模拟”(2019QZKK0206)
通讯作者: 赵天杰     E-mail: dushaojie19@mails.ucas.ac.cn;zhaotj@aircas.ac.cn
作者简介: 杜绍杰(1992-),男,河南开封人,硕士研究生,主要从事主动微波土壤水分反演研究。E?mail: dushaojie19@mails.ucas.ac.cn
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杜绍杰
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引用本文:

杜绍杰,赵天杰,施建成,马春锋,邹德富,王振,姚盼盼,彭志晴,郑景耀. Sentinel-1和Sentinel-2协同反演地表土壤水分[J]. 遥感技术与应用, 2022, 37(6): 1404-1413.

Shaojie Du,Tianjie Zhao,Jiancheng Shi,Chunfeng Ma,Defu Zou,Zhen Wang,Panpan Yao,Zhiqing Peng,Jingyao Zheng. Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture. Remote Sensing Technology and Application, 2022, 37(6): 1404-1413.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.6.1404        http://www.rsta.ac.cn/CN/Y2022/V37/I6/1404

图1  研究区概况
图2  NDVI对后向散射系数变化量的影响关系
图3  研究区域不同NDVI下后向散射变化量的趋势
图4  土壤水分反演值与地面观测值对比
图5  10个站点土壤水分反演值与地面观测值的时间序列验证
站点12345678910
R0.8060.6920.8490.7850.8110.7720.9150.9440.8560.876
RMSE/(m3/m3)0.0320.0490.0200.0820.0320.0780.1100.0820.0270.038
ubRMSE/(m3/m3)0.0310.0490.0170.0470.0290.0520.0720.0500.0270.036
表1  10个站点土壤水分反演值与地面观测值的验证指标
图6  研究区平均土壤水分对比
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