閬ユ劅鎶�鏈笌搴旂敤 鈥衡�� 2020, Vol. 35 鈥衡�� Issue (1): 13-22.DOI: 10.11873/j.issn.1004-0323.2020.1.0013

鈥� 鍦熷¥姘村垎涓撴爮 鈥� 涓婁竴绡�    涓嬩竴绡�

鍩轰簬Sentinel-1鍙奓andsat 8鏁版嵁鐨勯粦娌充腑娓稿啘鐢板湡澹ゆ按鍒嗕及绠�

鐜嬫爲鏋�1(),椹槬閿�2(),璧垫辰鏂�2,榄忛緳2   

  1. 1. 姹熻嫃甯堣寖澶у鍦扮悊娴嬬粯涓庡煄涔¤鍒掑闄紝姹熻嫃 寰愬窞 221116
    2. 涓浗绉戝闄㈣タ鍖楃敓鎬佺幆澧冭祫婧愮爺绌堕櫌锛岀敇鑲� 鍏板窞 730000
  • 鏀剁鏃ユ湡:2019-06-06 淇洖鏃ユ湡:2020-01-08 鍑虹増鏃ユ湡:2020-02-20 鍙戝竷鏃ユ湡:2020-04-01
  • 閫氳浣滆��: 椹槬閿�
  • 浣滆�呯畝浠�:鐜嬫爲鏋滐紙1980-锛夛紝鐢凤紝鐢樿們鍏板窞浜猴紝鍗氬+锛岃甯堬紝涓昏浠庝簨瀹氶噺閬ユ劅鐮旂┒銆侲?mail:swang@jsnu.edu.cn銆�
  • 鍩洪噾璧勫姪:
    姹熻嫃鐪佽嚜鐒剁瀛﹀熀閲戦」鐩�(BK20171165);鍥藉鑷劧绉戝鍩洪噾椤圭洰(41971305);涓闄⑩�滆タ閮ㄤ箣鍏夆�濋潚骞翠汉鎵嶉」鐩瓸绫�

Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery

Shuguo Wang1(),Chunfeng Ma2(),Zebin Zhao2,Long Wei2   

  1. 1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
    2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • Received:2019-06-06 Revised:2020-01-08 Online:2020-02-20 Published:2020-04-01
  • Contact: Chunfeng Ma

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鍏抽敭璇�: 鍦熷¥姘村垎, SAR, 鍦拌〃绮楃硻搴�, 妞嶈鍚按閲�, Sentinel-1, Landsat 8

Abstract:

Soil moisture is a key variable in land surface system. Using active microwave remote sensing observations, especially Synthetic Aperture Radar (SAR), has been proven a promising way on the estimation of spatial-temporal distribution of surface soil moisture by a lot of studies. However, there is still challenging in this field, because of the impacts caused by surface roughness and vegetation cover. In this context, this paper proposes an optimal estimation approach combined using SAR and optical remote sensing imagery, in order to retrieve vegetation water content, roughness and soil moisture simultaneously. First, water-cloud model is used to correct vegetation effect on microwave scattering process. In this step, vegetation transmittance factor (closed related to vegetation water content) is estimated by using three optical remote sensing indexes, namely, Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). Second, a cost function is constructed based on SAR observations and Oh model simulations, then soil moisture and surface roughness can be estimated through global optimization by shuffled complex evolution algorithm. The proposed method is performed by using Sentinel-1and Landsat 8 data in the middle researches of the Heihe River Basin, retrieved results are validated against ground measurements. Results show a good agreement between remote sensing estimates and ground measurements, which indicates the proposed method can retrieve soil moisture accurately. For soil moisture, the determination coefficient (R 2) is higher than 0.7, the root mean square error (RMSE) is 0.073 m3/m3. With respect to vegetation water content,R 2 is higher than 0.9 and RMSE is 0.885 kg/m2. In the meantime, it is found that the result of estimated vegetation water content and the parameterization scheme of vegetation parameters have pronounced influence on the accuracy of soil moisture estimates, which need to be further addressed in future research.

Key words: Soil moisture, SAR, Surface roughness, Vegetation water content, Sentinel-1, Landsat 8

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