基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算

王树果; 马春锋; 赵泽斌; 魏龙

doi:10.11873/j.issn.1004-0323.2020.1.0013

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

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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. 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

鎽樿/Abstract

鎽樿锛�

鍦熷￥姘村垎鏄檰鍦拌〃灞傜郴缁熶腑鐨勫叧閿彉閲忋�傚埄鐢ㄤ富鍔ㄥ井娉㈤仴鎰燂紝鐗瑰埆鏄悎鎴愬瓟寰勯浄杈撅紙Synthetic Aperture Radar, SAR锛夌殑瑙傛祴锛屽湪鐩戞祴鍜屼及璁¤〃灞傚湡澹ゆ按鍒嗘椂绌哄垎甯冩柟闈㈠凡寮�灞曚簡璇稿鐮旂┒銆傜劧鑰岋紝SAR鍦熷￥姘村垎鍙嶆紨浠嶅瓨鍦ㄨ澶氭寫鎴橈紝鐗瑰埆鏄湴琛ㄧ矖绯欏害鍜屾琚殑褰卞搷銆傚洜姝わ紝鏈枃鎻愬嚭浜嗕竴绉嶇粨鍚堜富鍔ㄥ井娉㈠拰鍏夊閬ユ劅鐨勪紭鍖栦及璁℃柟妗堬紝鏃ㄥ湪鍚屾鍙嶆紨妞嶈鍚按閲忋�佸湴琛ㄧ矖绯欏害鍜屽湡澹ゆ按鍒嗐�傚弽婕旂畻娉曢鍏堝湪姘翠簯妯″瀷鐨勬鏋朵笅瀵规ā鍨嬩腑鐨勬琚�忚繃鐜囧洜瀛愶紙涓庢琚惈姘撮噺瀵嗗垏鐩稿叧锛夐噰鐢�3绉嶄笉鍚岀殑鍏夊閬ユ劅鎸囨暟鈥斺�斾慨姝ｇ殑鍦熷￥璋冭妭妞嶈鎸囨暟锛圡odified Soil Adjusted Vegetation Index, MSAVI锛夈�佸綊涓�鍖栨琚寚鏁帮紙Normalized Difference Vegetation Index, NDVI锛夊拰褰掍竴鍖栨按浣撴寚鏁帮紙Normalized Difference Water Index, NDWI锛夎繘琛屽弬鏁板寲浼拌锛岀敤浜庢牎姝ｆ琚眰鐨勬暎灏勮础鐚�傚湪姝ゅ熀纭�涓婏紝鏋勯�犲熀浜嶴AR瑙傛祴鍜孫h妯″瀷鐨勪唬浠峰嚱鏁帮紝鍒╃敤澶嶅瀷娲楃墝鍏ㄥ眬浼樺寲绠楁硶杩涜鍦熷￥姘村垎鍜屽湴琛ㄧ矖绯欏害鐨勮仈鍚堝弽婕斻�傞噰鐢⊿entinel-1 SAR鍜孡andsat 8澶氬厜璋辨暟鎹湪榛戞渤涓父寮�灞曚簡鍙嶆紨璇曢獙锛屽苟鍒╃敤鐩稿簲鐨勫湴闈㈣娴嬫暟鎹缁撴灉杩涜浜嗛獙璇併�傜粨鏋滆〃鏄庡弽婕旂粨鏋滀笌鍦伴潰瑙傛祴鍏锋湁鑹ソ鐨勪竴鑷存�э紝鍏朵腑鍩轰簬NDWI鐨勬琚惈姘撮噺鍙嶆紨鏁堟灉鏈�浣筹紝涓庡湴闈㈣娴嬫瘮杈冿紝鍦熷￥姘村垎鍐冲畾绯绘暟锛�R ²锛夊湪0.7浠ヤ笂锛屽潎鏂规牴璇樊锛圧MSE锛変负0.073 m³/m³锛涙琚惈姘撮噺R ²澶т簬0.9锛孯MSE涓�0.885 kg/m²锛岃〃鏄庤鏂规硶鑳藉杈冨噯纭湴浼拌鍦熷￥姘村垎銆傚悓鏃跺彂鐜版琚惈姘撮噺鐨勪及璁＄粨鏋滐紝浠ュ強妞嶈閫忚繃鐜囩殑鍙傛暟鍖栨柟妗堝鍦熷￥姘村垎鐨勫弽婕旂簿搴︽湁涓�瀹氱殑褰卞搷锛屽湪鏈潵鐨勭爺绌朵腑闇�瑕佽繘涓�姝ユ帰绱€��

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

涓浘鍒嗙被鍙�:

TP79

鐜嬫爲鏋�, 椹槬閿�, 璧垫辰鏂�, 榄忛緳. 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梉J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(1): 13-22.

Shuguo Wang, Chunfeng Ma, Zebin Zhao, Long Wei. Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery[J]. Remote Sensing Technology and Application, 2020, 35(1): 13-22.

鍙傝�冩枃鐚�

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[6]	琚佸惎杩�,璧垫灄,骞歌禐鍝�,閭瑰痉瀵�,璧靛缓濠�,鍒樹笘鍗�,妯婄. 闈掕棌宸ョ▼璧板粖娌跨嚎澶氬勾鍐诲湡鍖鸿瀺鍖栧楂樺垎杈ㄧ巼鍦拌〃鍦熷￥姘村垎鍙嶆紨[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1350-1363.
[7]	璋㈡枃鎵�,鍒樼,璧佃嫳鑺�. 鍩轰簬GF-1鍜孡andsat 8褰卞儚鐨勬柊鐤嗕笢澶╁北鏄屽悏鍦板尯宀╂�ц瘑鍒�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1423-1432.
[8]	寮犺秺,瀹夋枃闊�,璐哄嚡椋�,鏋楁槑妫�,寮犲竻褰�. 1绫矯-SAR-01涓氬姟鍗槦娴烽潰椋庡弽婕旇兘鍔涘垵姝ヨ瘎浼�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1485-1495.
[9]	绗﹂緳宕�,鏈卞缓鍐�,浠樻捣寮�,瑙ｆ竻鍗�,闊╂枃娑�. 闈㈠悜鍙岀珯SAR绯荤粺鐨勯暱娉olInSAR鐩爣鑷�傚簲鍒嗚В[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1017-1027.
[10]	鏌闃�,鑳＄ゥ绁�,鏄庣拹鐠�,鍒樺姝�,灏圭户閼�,鍒樺畤鑸�. 闈㈠悜鍦拌〃褰㈠彉楂樼簿搴︾洃娴嬬殑GNSS-InSAR铻嶅悎鏂规硶[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1028-1041.
[11]	鍚村技鏄�,閮缓鏂�,鍚撮樋涓�,鍒樹赴,鍐晱. 鍗楄喀宸寸摝宄板湴鍖哄湴琛ㄥ舰鍙樼殑InSAR鐩戞祴涓庡垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1042-1053.
[12]	鍚存槑鍫�,鎴夸簯宄�,娌堟湀,鎴村彲浜�,濮氫箟鎸�,闄堝缓寮�,鍐枃鍑�. 鍩轰簬鐭熀绾緿InSAR鐨勭櫧楣ゆ哗搴撳尯钃勬按鏈熸粦鍧￠殣鎮ｅ箍鍩熷揩閫熷姩鎬佽瘑鍒�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1054-1061.
[13]	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.
[14]	浠ｆ澃,濮滅埍杈�,钁ｆ槑鏈�,楂樼珛缇�,鐜嬪織浼�. 鍩轰簬SAR缁勫悎宸紓鍥剧殑娌冲崡鐪佹柊涔″競娲按娣规病鐩戞祴[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 479-486.
[15]	寮犲▍濞�,甯堣姼,鐜嬩匠杩�,棰夋槙瀹�,鐭抽緳榫�. 鏂扮枂浼婄妬鍦板尯婊戝潯闅愭偅InSAR璇嗗埆涓庡彂鑲茬壒寰佺爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 496-507.

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�

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

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