• ISSN 1004-0323     CN 62-1099/TP
• 联合主办：中国科学院遥感联合中心
• 中国科学院兰州文献情报中心
• 中国科学院国家空间科学中心
 遥感技术与应用  2020, Vol. 35 Issue (1): 23-32    DOI: 10.11873/j.issn.1004-0323.2020.1.0023
 土壤水分专栏

1. 中山大学地理科学与规划学院，广东省城市化与地理环境空间模拟重点实验室，广东 广州 510275
2. 广东省地质过程与矿产资源探查重点实验室，广东 广州 510275
3. 南方海洋科学与工程广东省实验室(珠海)，广东 珠海 519000
4. 中国科学院遥感与数字地球研究所，遥感科学国家重点实验室，北京 100101
Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin
Jiashun Luo1,2,3,Jianxiu Qiu1,2,3(),Tianjie Zhao4,Dagang Wang1,2,3
1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Key Laboratory of Mineral Resource & Geological Processes of Guangdong Province, Guangzhou 510275, China
3. Southern Laboratory of Ocean Science and Engineering (Guangdong, Zhuhai), Zhuhai 519000, China
4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
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Abstract:

In this study, a change detection model, constructed using the Sentinel-1 Synthetic Aperture Radar (SAR) data and the simultaneous Normalized Difference Vegetation Index (NDVI) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 sensors, is applied to estimate soil moisture in middle reaches of the Heihe River Basin, and the effects of two key parameters on retrieval accuracy are comprehensively investigated. The results show that: (1) when constructing the empirical relationship between backscattering coefficient difference ( $Δ σ$ ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the $( Δ σ - V I )$ space are approximately 2% and 4% for MODIS NDVI and Landsat 8 NDVI, respectively; (2) the Landsat 8 NDVI-based change detection model slightly outperforms the MODIS NDVI-based model in soil moisture retrieval accuracy, with Root Mean Square Error(RMSE) of 0.040 m3/m3 and 0.044 m3/m3respectively; (3) for the key parameters of the change detection method, replacing the ground-based initial soil moisture and scaling factor (maximum soil moisture difference between two adjacent dates $Δ M s m a x$ ) by the low-resolution SMAP/Sentinel-1 L2_SM_SP data will increase the RMSE by 0.01 m3/m3 and 0.04 m3/m3 respectively. Comparing to the parameter of initial soil moisture, the error in soil moisture scaling factor will lead to more significant degradation in the performance of the change detection method, thus it is recommended to use the high precision scaling factor for soil moisture estimation. This study confirms the promising potential of Sentinel-1 data for retrieving high-resolution soil moisture via change detection method and provides practical insight into its application.

Key words: Heihe River Basin    Soil moisture    Change detection method    Sentinel-1

 ZTFLH: TP79