基于Sentinel-1数据的黑河中游土壤水分反演

罗家顺; 邱建秀; 赵天杰; 王大刚

doi:10.11873/j.issn.1004-0323.2020.1.0023

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

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鍩轰簬Sentinel-1鏁版嵁鐨勯粦娌充腑娓稿湡澹ゆ按鍒嗗弽婕�

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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 Luo^1,^2,³,Jianxiu Qiu^1,^2,³(),Tianjie Zhao⁴,Dagang Wang^1,^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

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

鎽樿/Abstract

鎽樿锛�

鍩轰簬Sentinel-1鍚堟垚瀛斿緞闆疯揪 (SAR) 鏁版嵁鍙婄浉鍚屾椂娈电殑涓垎杈ㄧ巼鎴愬儚鍏夎氨浠紙MODIS锛夊拰Landsat 8涓ょ褰掍竴鍖栨琚寚鏁帮紙NDVI锛夛紝鏋勫缓鍙樺寲妫�娴嬫ā鍨嬩互浼扮畻榛戞渤涓父鐨勯珮鍒嗚鲸鐜囧湡澹ゆ按鍒嗭紝骞舵帰璁ㄦā鍨嬩腑鍏蜂綋鍙傛暟璁剧疆瀵逛及绠楃簿搴︾殑褰卞搷銆傜粨鏋滆〃鏄庯細鈶犲湪瀵瑰悗鍚戞暎灏勭郴鏁版椂闂村簭鍒楃殑宸�� ( $螖蟽$ ) 鍜屾琚寚鏁� ( $V I$ ) 杩涜绾挎�у缓妯¤繃绋嬩腑锛孧ODIS NDVI鍜孡andsat 8 NDVI杩欎袱绉嶆琚骇鍝佹墍鏋勫缓鐨勬ā鍨嬪湪 $螖蟽 - V I$ 绌洪棿涓墍閫夊彇鐨勯噰鏍风偣姣斾緥鍒嗗埆涓�2%鍜�4%鏃讹紝鍚勮嚜鍙栧緱鏈�浼樼簿搴�; 鈶′互鍦熷￥姘村垎鍙嶆紨涓虹洰鏍囷紝浣跨敤Landsat 8 NDVI鏋勫缓鐨勫彉鍖栨娴嬫ā鍨嬬暐浼樹簬浣跨敤MODIS NDVI鏋勫缓鐨勫彉鍖栨娴嬫ā鍨嬶紝涓ょ妯″瀷鐨勫潎鏂规牴璇樊RMSE鍒嗗埆涓�0.040 m³/m³鍜�0.044 m³/m³锛岀浉鍏崇郴鏁�R鍒嗗埆涓�0.86鍜�0.83; 鈶㈠浜庡彉鍖栨娴嬫柟娉曠殑鍏抽敭鍙傛暟锛岃嫢浣跨敤浣庡垎杈ㄧ巼鐨凷MAP/Sentinel-1 L2_SM_SP鍦熷￥姘村垎鏁版嵁鍒嗗埆浠ｆ浛绔欑偣瑙傛祴鐨勫湡澹ゆ按鍒嗗垵濮嬪�煎拰缂╂斁鍥犲瓙 (鍗充袱涓繛缁椂鐩稿湡澹ゆ按鍒嗗彉鍖栫殑鏈�澶у�� $螖 M s m a x$ ) 杩欎袱涓弬鏁帮紝鍒欏湡澹ゆ按鍒哛MSE灏嗗垎鍒鍔�0.01 m³/m³鍜�0.04 m³/m³銆傚嵆鍦熷￥姘村垎缂╂斁鍥犲瓙杩欎竴鍙傛暟鐨勮宸鍙嶆紨缁撴灉鐨勫奖鍝嶅ぇ浜庡湡澹ゆ按鍒嗗垵濮嬪�艰宸鍙嶆紨缁撴灉鐨勫奖鍝嶏紝鏁呴噰鐢ㄩ珮绮惧害鐨勭缉鏀惧洜瀛愯繘琛屽彉鍖栨娴嬩及绠椼�傜爺绌剁粨璁哄浜庡埄鐢ㄦ柊鍏寸殑Sentinel-1 SAR鏁版嵁锛岄�氳繃鍙樺寲妫�娴嬬畻娉曞噯纭幏鍙栭珮鍒嗚鲸鐜囧湡澹ゆ按鍒嗕俊鎭叿鏈夊疄闄呭弬鑰冧环鍊笺��

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 ( $螖蟽$ ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the $(螖蟽 - V I)$ 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 $螖 M s m a x$ ) 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

涓浘鍒嗙被鍙�:

TP79

缃楀椤�,閭卞缓绉�,璧靛ぉ鏉�,鐜嬪ぇ鍒�. 鍩轰簬Sentinel-1鏁版嵁鐨勯粦娌充腑娓稿湡澹ゆ按鍒嗗弽婕擺J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(1): 23-32.

Jiashun Luo,Jianxiu Qiu,Tianjie Zhao,Dagang Wang. Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin[J]. Remote Sensing Technology and Application, 2020, 35(1): 23-32.

鍙傝�冩枃鐚�

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[4]	寮犳垚鎵�,鍒樺▉,鏉ㄥ嘲,褰嚡,鍛ㄩ洩涓�. 鍩轰簬缂栫爜瀵归綈鐢熸垚瀵规姉缃戠粶鐨勫紓婧愰仴鎰熷奖鍍忓湴琛ㄨ琚彉鍖栨娴嬫柟娉�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1107-1117.
[5]	楂樻ⅵ娲�,闄堟柟,鐜嬮浄,鏉ㄩ樋寮�,浜庡崥. 鍩轰簬椹皵绉戝か闅忔満鍦虹殑PLANET楂樺垎褰卞儚婊戝潯鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1180-1191.
[6]	椹畻鏂�,閮濆嚒,瀹嬬惓,楹荤憺. 涓�绉嶅浘鍍忓洖褰掍笌鍏宠仈鍏崇郴鐗瑰緛铻嶅悎鐨勯仴鎰熷奖鍍忓彉鍖栨娴嬫柟娉�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1215-1225.
[7]	浜庡啺,浣曟櫤鍕�,璋潚闆�,寮犺繃,鏉庡箍瀹�,浣樻澃,鍚存粩,鐜嬫潹. Sentinel鈦�1A TS鈦僁InSAR娲ュ唨鏍稿績缁忔祹鍖哄箍鍩熷湴琛ㄦ矇闄嶇洃娴嬩笌鍒嗘瀽[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 454-464.
[8]	寮犱簹涓�,寮犵珛宄�,浣曟瘏,鏉ㄦ椇,鏇硅儨楣�. 涓ぉ灞卞崡渚濆唴閲屽垏鍏嬪啺宸濊酱閮ㄤ簩缁存祦閫熷強杩愬姩鏈哄埗[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1319-1327.
[9]	鏉ㄥ, 姹ょ嚂鏉�, 寮犲畞棣�, 寮犳亽鏉�, 寰愬皯鍗�. 鍩轰簬SMOS銆丼MAP鏁版嵁鐨勯潚钘忛珮鍘熷椋庡強妞嶈鐢熼暱瀛ｅ湡澹ゆ按鍒嗛暱娑堢壒寰佺爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1373-1384.
[10]	鏇归珮宀�,鏉ㄥ. 鍩轰簬鍏抽敭杈呭姪鍙傛暟鏁版嵁鐨凩-MEB浜害娓╁害妯℃嫙鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1385-1391.
[11]	榛勫仴搴�,鏉ㄥ,椹秴. SMAP L2澶氬昂搴﹀湡澹ゆ按鍒嗘暟鎹笌ISMN瀹炴祴鏁版嵁鐨勫樊寮傜壒寰佺爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1392-1403.
[12]	鏉滅粛鏉�,璧靛ぉ鏉�,鏂藉缓鎴�,椹槬閿�,閭瑰痉瀵�,鐜嬫尟,濮氱浖鐩�,褰織鏅�,閮戞櫙鑰�. Sentinel-1鍜孲entinel-2鍗忓悓鍙嶆紨鍦拌〃鍦熷￥姘村垎[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1404-1413.
[13]	榛勯挵鐜�,鍒樺嚡,鐜嬫爲涓�,鐜嬪ぇ鎴�,鑻戝嘲,鐜嬩繚鏋�,鏅枃,鐜嬩紵. 鍖楁柟鍏稿瀷鍖哄煙閬ユ劅鍜屾ā鍨嬪湡澹ゆ按鍒嗕骇鍝佺殑瀵规瘮鍙婅瘎浼�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1414-1426.
[14]	鐜嬮洩鐞�,寮犵繑,闄堣兘鎴�,椹畯浜�. 鍩轰簬鏃剁┖鍙樺紓鎬х殑鍦熷￥姘村垎瑙傛祴缃戣璁�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1437-1446.
[15]	鏂硅タ鐟�,钂嬬幉姊�,宕旀収鐝�. 鍩轰簬Sentinel-1闆疯揪鏁版嵁鐨勯潚钘忛珮鍘熷湴鍖哄湡澹ゆ按鍒嗗弽婕旂爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(6): 1447-1459.

鍩轰簬Sentinel-1鏁版嵁鐨勯粦娌充腑娓稿湡澹ゆ按鍒嗗弽婕�

Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin

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寮曠敤鏈枃

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