Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

Lingling Bian; Juanle Wang; Bing Guo; Kai Cheng; Haishuo Wei

doi:10.11873/j.issn.1004-0323.2020.1.0211

Remote Sensing Technology and Application 鈥衡�� 2020, Vol. 35 鈥衡�� Issue (1): 211-218.DOI: 10.11873/j.issn.1004-0323.2020.1.0211

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Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

Lingling Bian^1,²(),Juanle Wang^2,⁴(),Bing Guo¹,Kai Cheng^2,³,Haishuo Wei^1,²

^1. School of Architecture Engineering, Shandong University of Technology, Zibo 255000, China
^2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^3. University of Chinese Academy of Sciences, Beijing 100049
^4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2018-12-04 Revised:2019-01-07 Online:2020-04-01 Published:2020-02-20
Contact: Juanle Wang

鍩轰簬鐗瑰緛绌洪棿鐨勯粍娌充笁瑙掓床鍨﹀埄鍘垮湡澹ょ洂鍒嗛仴鎰熸彁鍙�

杈圭幉鐜�^1,²(),鐜嬪嵎涔�^2,⁴(),閮叺¹,绋嬪嚡^2,³,榄忔捣纭�^1,²

^1. 灞变笢鐞嗗伐澶у寤虹瓚宸ョ▼瀛﹂櫌锛屽北涓� 娣勫崥 255049
^2. 涓浗绉戝闄㈠湴鐞嗙瀛︿笌璧勬簮鐮旂┒鎵�璧勬簮涓庣幆澧冧俊鎭郴缁熷浗瀹堕噸鐐瑰疄楠屽锛屽寳浜� 100101
^3. 涓浗绉戝闄㈠ぇ瀛︼紝鍖椾含 100049
^4. 姹熻嫃鐪佸湴鐞嗕俊鎭祫婧愬紑鍙戜笌鍒╃敤鍗忓悓鍒涙柊涓績锛屾睙鑻� 鍗椾含 210023

閫氳浣滆��: 鐜嬪嵎涔�
浣滆�呯畝浠�:杈圭幉鐜�(1994-锛夛紝濂筹紝灞变笢娴庡崡浜猴紝纭曞＋鐮旂┒鐢�,涓昏浠庝簨鍦熷湴璧勬簮涓庨仴鎰熷簲鐢ㄧ爺绌躲�侲?mail:bianll@lreis.ac.cn銆�
鍩洪噾璧勫姪:
涓浗绉戝闄㈡垬鐣ユ�у厛瀵肩鎶�涓撻」锛圓绫伙級璧勫姪(XDA19040501);闃茬伨鍑忕伨鐭ヨ瘑鏈嶅姟绯荤粺(CKCEST?2018?2?8);涓浗绉戝闄⑩�滃崄涓変簲鈥濅俊鎭寲涓撻」绉戝澶ф暟鎹伐绋嬮」鐩�(XXH13505?07)

Abstract

Abstract:

Soil salinization is an important challenge to achieve sustainable use of land resources. The appropriate method for remote sensing quantitative inversion in the coastal Yellow River Delta region of China can provide technical reference for regional salinization monitoring and prevention. Utilizing Landsat 8 OLI image and field measured data, we extracted key surface characteristic parameters, quantitatively discussed the law and relationship between soil salinity and surface biophysical parameters and established a soil salinity inversion model. The results show that the inversion precisions of Albedo-MSAVI, SI-Albedo and SI-NDVI feature space are 83.4%, 88.8% and 80.6% respectively. The analysis shows the SI-Albedo model is suitable for the inversion of salinization level in Binhai areas. For Albedo-MSAVI and SI-NDVI models, they have certain reference significance for salinization information extraction in inland arid and semi-arid areas. Based on the inversion of the SI-Albedo feature space with the highest accuracy, the level of salinization in Kenli County is generally high-low-high trends from the east to the west, which is consistent with the formation mechanism of salt accumulation in this area.

Key words: Salinization, Feature space, Remote sensing inversion, Yellow River Delta, Kenli County

鎽樿锛�

鍦熷￥鐩愭笉鍖栨槸瀹炵幇鍦熷湴璧勬簮鍙寔缁埄鐢ㄦ墍闈复鐨勯噸瑕佹寫鎴橈紝鍦ㄦ垜鍥芥花娴风殑榛勬渤涓夎娲插尯鍩熼仴鎰熷畾閲忓弽婕旈�傚疁鏂规硶鍙负鍖哄煙鐩愭笉鍖栫洃娴嬩笌闃叉不鎻愪緵鎶�鏈柟娉曞弬鑰冦�傜爺绌朵互Landsat 8 OLI鏁版嵁鍜岄噹澶栧疄娴嬫暟鎹负鍩虹锛屾彁鍙栧叧閿湴琛ㄧ壒寰佸弬閲忥紝瀹氶噺鍖栨帰璁ㄥ湡澹ょ洂鍒嗕笌鍦拌〃鐢熺墿鐗╃悊鍙傛暟涔嬮棿鐨勮寰嬪強鍏崇郴锛屽缓绔嬮粍娌充笁瑙掓床鍦熷￥鐩愬垎鏈�浼樺弽婕旀ā鍨嬨�傜粨鏋滆〃鏄�:Albedo-MSAVI銆丼I-Albedo銆丼I-NDVI鍙嶆紨绮惧害鍒嗗埆涓�83.4%銆�88.8%鍜�80.6%銆傚垎鏋愯涓篠I-Albedo妯″瀷鏈�閫傜敤浜庢花娴峰湴鍖虹洂娓嶅寲绋嬪害鍙嶆紨锛屽婊ㄦ捣鍦板尯鍦熷￥鐩愬垎鐨勯娴嬭兘鍔涜緝寮猴紱Albedo-MSAVI銆丼I-NDVI妯″瀷瀵瑰唴闄嗗共鏃便�佸崐骞叉棻鍦板尯鐨勭洂娓嶅寲淇℃伅鎻愬彇鍏锋湁涓�瀹氱殑鍙傝�冩剰涔夈�傚熀浜庣簿搴︽渶楂樼殑SI-Albedo鎵�鍙嶆紨鐨勭粨鏋滄潵鐪嬶紝鍨﹀埄鍘跨洂娓嶅寲绋嬪害鑷笢鍚戣タ鎬讳綋鍛堥珮浣庨珮璧板悜锛屼笌璇ュ尯鍩熺洂鍒嗙Н鑱氱殑鎴愬洜鏈虹悊鐩哥銆�

鍏抽敭璇�: 鐩愭笉鍖�, 鐗瑰緛绌洪棿, 閬ユ劅鍙嶆紨, 榛勬渤涓夎娲�, 鍨﹀埄鍘�

CLC Number:

TP753

Lingling Bian,Juanle Wang,Bing Guo,Kai Cheng,Haishuo Wei. Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space[J]. Remote Sensing Technology and Application, 2020, 35(1): 211-218.

杈圭幉鐜�,鐜嬪嵎涔�,閮叺,绋嬪嚡,榄忔捣纭�. 鍩轰簬鐗瑰緛绌洪棿鐨勯粍娌充笁瑙掓床鍨﹀埄鍘垮湡澹ょ洂鍒嗛仴鎰熸彁鍙朳J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(1): 211-218.

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Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

鍩轰簬鐗瑰緛绌洪棿鐨勯粍娌充笁瑙掓床鍨﹀埄鍘垮湡澹ょ洂鍒嗛仴鎰熸彁鍙�

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