閬ユ劅鎶�鏈笌搴旂敤 鈥衡�� 2020, Vol. 35 鈥衡�� Issue (1): 23-32.DOI: 10.11873/j.issn.1004-0323.2020.1.0023

鈥� 鍦熷¥姘村垎涓撴爮 鈥� 涓婁竴绡�    涓嬩竴绡�

鍩轰簬Sentinel-1鏁版嵁鐨勯粦娌充腑娓稿湡澹ゆ按鍒嗗弽婕�

缃楀椤�1,2,3,閭卞缓绉�1,2,3(),璧靛ぉ鏉�4,鐜嬪ぇ鍒�1,2,3   

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    3. 鍗楁柟娴锋磱绉戝涓庡伐绋嬪箍涓滅渷瀹為獙瀹�(鐝犳捣)锛屽箍涓� 鐝犳捣 519000
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  • 鏀剁鏃ユ湡:2019-06-30 淇洖鏃ユ湡:2020-01-12 鍑虹増鏃ユ湡:2020-02-20 鍙戝竷鏃ユ湡:2020-04-01
  • 閫氳浣滆��: 閭卞缓绉�
  • 浣滆�呯畝浠�:缃楀椤�(1994-)锛屽コ锛屽箍涓滃鍩庝汉锛岀澹爺绌剁敓锛屼富瑕佷粠浜嬪井娉㈠湡澹ゆ按鍒嗗弽婕旂爺绌躲�侲?mail锛歭uojsh3 @mail2.sysu.edu.cn
  • 鍩洪噾璧勫姪:
    鍥藉鑷劧绉戝鍩洪噾闈笂椤圭洰(41971031);骞夸笢鐪佽嚜鐒剁瀛﹀熀閲戦」鐩�(2016A030310154)

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. 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
  • Received:2019-06-30 Revised:2020-01-12 Online:2020-02-20 Published:2020-04-01
  • Contact: Jianxiu Qiu

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鍏抽敭璇�: 榛戞渤娴佸煙, 鍦熷¥姘村垎, 鍙樺寲妫�娴�, Sentinel-1

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 ( ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the ( - 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 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

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