Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin

Aynur Matnuri; Mamattursun Eziz; Marhaba Turgun; Xinguo Li

doi:10.11873/j.issn.1004-0323.2021.2.0362

Abstract

Abstract:

As a product of the development of modern industry and mining锛� soil heavy metal lead pollution has gradually invaded agricultural production and agricultural products. Hyperspectral technology has become an important method for monitoring heavy metals in soil due to its macroscopic锛� rapid and efficient characteristics. This study takes the Pb element of vineyard soil in Xinjiang Turpan Basin as the research object锛� analyzes the relationship between soil spectral reflectance data and soil Pb content under 15 spectral transformations including the original soil spectrum锛� and constructs a partial least square regression of soil Pb content 锛� PLSR锛� model and geographic weighted re-regression 锛圙WR锛� model锛� comparative analysis and discussion of the feasibility of using soil hyperspectral to estimate the vineyard soil Pb content. The results show that the original spectral reflectance of the soil can effectively enhance the spectral characteristics of the vineyard soil Pb element and the estimation accuracy of the model through spectral transformation. Among them锛� the SRSD transformation PLSR model and GWR model have the best estimation capabilities. The GWR model is better than the PLSR model to explain the hyperspectral estimation of the heavy metal Pb content in vineyard soil. From the perspective of model stability and accuracy锛� in the SRSD differential transformation锛� the GWR model R² is increased from 0.262 of the PLSR model to 0.866锛� and the RMSE is reduced by 1.009. Using GWR model can effectively improve the accuracy of estimating the Pb content of vineyard soil. This study provides a useful reference for the study of soil heavy metal pollution and soil environmental safety in Chinese vineyard bases.

Key words: Soil, Heavy metal Pb, Hyperspectral, Spectral transformation, Turpan Basin

鎽樿锛�

鍦熷￥閲嶉噾灞為搮姹℃煋浣滀负鐜颁唬宸ョ熆涓氬彂灞曠殑浜х墿锛屽凡閫愭笎鍏ヤ镜鍒板啘涓氱敓浜у拰鍐滀骇鍝佷腑銆傞珮鍏夎氨鎶�鏈敱浜庡叿鏈夊畯瑙傘�佸揩閫熴�侀珮鏁堢殑鐗圭偣宸叉垚涓哄湡澹ら噸閲戝睘鐩戞祴鐨勯噸瑕佹墜娈点�備互鏂扮枂鍚愰瞾鐣泦鍦拌憽钀勫洯鍦熷￥Pb鍏冪礌涓虹爺绌跺璞★紝鍒嗘瀽鍦熷￥鍘熷鍏夎氨鍦ㄥ唴鐨�15绉嶅厜璋卞彉鎹笅鐨勫湡澹ゅ厜璋卞弽灏勭巼鏁版嵁涓庡湡澹b鍚噺鐨勫叧绯伙紝鏋勫缓鍦熷￥Pb鍚噺鍋忔渶灏忎簩涔樺洖褰掞紙PLSR锛夋ā鍨嬪拰鍦扮悊鍔犳潈閲嶅洖褰掞紙GWR锛夋ā鍨嬶紝瀵规瘮鍒嗘瀽骞舵帰璁ㄨ繍鐢ㄥ湡澹ら珮鍏夎氨浼扮畻钁¤悇鍥湡澹b鍚噺鐨勫彲琛屾�с�傜粨鏋滆〃鏄庯細鍦熷￥鍘熷鍏夎氨鍙嶅皠鐜囬�氳繃鍏夎氨鍙樻崲鑳芥湁鏁堝寮鸿憽钀勫洯鍦熷￥Pb鍏冪礌鐨勫厜璋辩壒寰佸強妯″瀷浼扮畻绮惧害锛屽叾涓紝骞虫柟鏍逛簩闃跺井鍒嗭紙SRSD锛夊彉鎹㈢殑PLSR妯″瀷鍜孏WR妯″瀷浼扮畻鑳藉姏鏈�浼樸�傞噰鐢℅WR妯″瀷姣擯LSR妯″瀷鏇村ソ鐨勮В閲婅憽钀勫洯鍦熷￥閲嶉噾灞濸b鍚噺楂樺厜璋变及绠椼�備粠妯″瀷绋冲畾鎬у拰绮剧‘鎬ф潵鐪嬶紝鍦ㄥ钩鏂规牴浜岄樁寰垎鍙樻崲涓璆WR妯″瀷R²浠嶱LSR妯″瀷鐨�0.262鎻愰珮鑷�0.866锛� 骞虫柟鏍硅宸噺灏戜簡1.009銆傞噰鐢℅WR妯″瀷鍙湁鏁堟彁楂樹及绠楄憽钀勫洯鍦熷￥Pb鍚噺鐨勭簿搴︼紝涓轰腑鍥借憽钀勫洯鍩哄湴鍦熷￥閲嶉噾灞炴薄鏌撲互鍙婂湡澹ょ幆澧冨畨鍏ㄧ爺绌舵彁渚涙湁鐩婂�熼壌銆�

鍏抽敭璇�: 鍦熷￥, 閾呭厓绱�, 楂樺厜璋�, 鍏夎氨鍙樻崲, 鍚愰瞾鐣泦鍦�

CLC Number:

TP79

Aynur Matnuri,Mamattursun Eziz,Marhaba Turgun,Xinguo Li. Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin[J]. Remote Sensing Technology and Application, 2021, 36(2): 362-371.

闃夸緷鍔皵路楹︽彁鍔棩null,楹﹂害鎻愬悙灏旈�娐疯壘鍒欏瓬null,楹﹀皵鍝堝反路鍥惧皵璐ull,鏉庢柊鍥�. 鍚愰瞾鐣泦鍦拌憽钀勫洯鍦熷￥閲嶉噾灞為搮鍚噺楂樺厜璋变及绠梉J]. 閬ユ劅鎶�鏈笌搴旂敤, 2021, 36(2): 362-371.

References

1	Chen Xiaoli锛� Chen Dan. Consumer Behavior Analysis of Characteristic Fruit and Forest Products鈥擜 Case of Xinjiang 锛籎锛�. Rural Economy锛�2017锛�3锛夛細55-60.
1	闄堟檽涓斤紝闄堝饯. 鐗硅壊鏋楁灉浜у搧娑堣垂鑰呰涓哄垎鏋愨�斾互鏂扮枂涓轰緥锛籎锛�. 鍐滄潙缁忔祹锛�2017锛�3锛夛細55-60.
2	Eziz Mamattursun 锛� Mamut Ajigul 锛� Mohammad Anwar 锛� et al. Assessment of Heavy Metal Pollution and Its Potential Ecological Risks of Farmland Soils of Oasis in Bosten Lake Basin锛籎锛�. Acta Geographica Sinica锛� 2017锛� 72锛�9锛夛細1680鈥�1694.
2	楹﹂害鎻愬悙灏旈�娐疯壘鍒欏瓬锛� 闃垮悏鍙や附路椹湪鎻愶紝鑹惧凹鐡﹀皵路涔颁拱鎻愶紝绛�. 鍗氭柉鑵炬箹娴佸煙缁挎床鍐滅敯鍦熷￥閲嶉噾灞炴薄鏌撳強娼滃湪鐢熸�侀闄╄瘎浠� 锛籎锛�. 鍦扮悊瀛︽姤锛� 2017锛� 72锛�9锛夛細1680鈥�1694.
3	Wang Feilong锛� Wang Fumin锛� Hu Jinghui锛� et al. Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates锛籎锛�. Remote Sensing Technology and Application锛�2020锛�35锛�2锛夛細 458-468.
3	鐜嬮榫欙紝鐜嬬姘戯紝鑳℃櫙杈夛紝绛�.鍩轰簬鐩稿鍏夎氨鍙橀噺鐨勬棤浜烘満閬ユ劅姘寸ɑ浼颁骇鍙婁骇閲忓埗鍥撅蓟J锛�.閬ユ劅鎶�鏈笌搴旂敤锛�2020锛�35锛�2锛夛細 458-468.
4	He Junliang锛� Cui Junli锛� Zhang Shuyuan锛� et al. Hyperspectral Estimation of Heavy Metal Cu Content in Soil based on Partial Least Square Method锛籎锛�. Remote Sensing Technology and Application锛� 2019锛� 34锛�5锛夛細 998-1004.
4	璐哄啗浜紝宕斿啗涓斤紝寮犳窇濯涳紝绛�. 鍩轰簬鍋忔渶灏忎簩涔樼殑鍦熷￥閲嶉噾灞為摐鍚噺楂樺厜璋变及绠楋蓟J锛�. 閬ユ劅鎶�鏈笌搴旂敤锛� 2019锛� 34锛�5锛夛細 998-1004.
5	Tan K锛� Ye Y Y锛� Du P J锛� et al. Estimation of Heavy Metal Concentrations in Reclaimed Mining Soils Using Reflectance Spectroscopy锛籎锛�. Spectroscopy and Spectral Analysis锛� 2014锛� 34锛�12锛夛細 3317-3322. doi锛�/10.3964/j.issn.1000-0593锛�2014锛�12-3317-06
6	Li Qiongqiong锛� Liu Yunlong. Soil Heavy Metals Estimation based on Hyperspectral in Urban Residential锛籎锛�.Remote Sensing Technology and Application锛�2019锛�34锛�3锛夛細540-546.
6	鏉庣惣鐞硷紝鏌充簯榫�. 鍩庡競灞呮皯鍖哄湡澹ら噸閲戝睘鍚噺楂樺厜璋卞弽婕旂爺绌讹蓟J锛�. 閬ユ劅鎶�鏈笌搴旂敤锛�2019锛�34锛�3锛夛細540-546.
7	Lian S锛� Jian J锛� Tan D J锛� et al. Estimate of Heavy Metals in Soil and Streams Using Combined Geochemistry and Field Spectroscopy in Wansheng Mining Area锛� Chongqing锛� China. International Journal of Applied Earth Observation and Geoinformation锛�2015锛�34锛�1-9. doi锛�/10.1016/j.jag.2014.06.013
8	Stazi S R锛孉ntonucci F锛孭allottino F锛宔t al. Hyperspectral Visible-near Infrared Determination of Arsenic Concentration in Soil锛籎锛�. Communications in Soil Science and Plant Analysis锛�2014锛�45锛�2911-2920. doi锛�/10.1080/00103624.2014.954716.
9	Lu Jiehui. Study on Hyperspectral Estimation of Chromium Content in Soil based on Grey Relational Analysis锛籇锛�. Tai'an锛� Journal of Shandong Agricultural University锛�2019.
9	璺澃鏅�. 鍩轰簬鐏拌壊鍏宠仈鍒嗘瀽鐨勫湡澹ら摤鍚噺楂樺厜璋变及娴嬬爺绌讹蓟D锛�.娉板畨锛氬北涓滃啘涓氬ぇ瀛︼紝2019.
10	Liu M L锛� Liu X N锛� Wu L锛� et al. Wavelet-based Detection of Crop Zinc Stress Assessment Using Hyperspectral Reflectance锛籎锛�. Computers and Geosciences锛�2011锛�37锛�1254-1263. doi锛�/ 10.1016/j.cageo.2010.11.019.
11	Jiang Zhenlan锛� Yang Yusheng锛� Sha Jinming. Application of GWR Model in Hyperspectral Prediction of Soil Heavy Metals锛籎锛�. Acta Geographica Sinica锛�2017锛�72锛�3锛夛細533-544.
11	姹熸尟钃濓紝鏉ㄧ帀鐩涳紝娌欐檵鏄�. GWR妯″瀷鍦ㄥ湡澹ら噸閲戝睘楂樺厜璋遍娴嬩腑鐨勫簲鐢蓟J锛�. 鍦扮悊瀛︽姤锛�2017锛�72锛�3锛夛細533-544.
12	Wang J J锛� Cui L J锛� Gao W X锛� et al. Prediction of Low Heavy Metal Concentrations in Agricultural Soils Using Visible and Near-infrared Reflectance Spectroscopy锛籎锛�. Geoderma锛�2014锛� 216锛� 1-9. doi锛�/10.1016/j.geoderma.2013.10.024.
13	Jiehao L眉锛� Hao Ningyan锛� Cui Xiaolin. Inversion Model for Copper Content in Farmland of Tailing Area based on Visible-near Infrared Reflectance Spectroscopy锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering锛�2015锛�31锛�9锛夛細265-270.
13	鍚曟澃锛岄儩瀹佺嚂锛屽磾鏅撲复.鍒╃敤鍙鍏夎繎绾㈠鐨勫熬鐭垮尯鍐滅敯鍦熷￥Cu鍚噺鍙嶆紨锛籎锛�.鍐滀笟宸ョ▼瀛︽姤锛�2015锛�31锛�9锛夛細265-270.
14	Shi T Z锛� Chen Y Y锛� Liu Y L锛� et al. Visible and Near-infrared Reflectance Spectroscopy - An Alternative for Monitoring Soil Contamination by Heavy Metals锛籎锛�. Journal of Hazardous Materials锛�2014锛�265锛�166-176. doi锛�/10.1016/j.jhazmat.2013. 11.059
15	Fotheringham A S锛� Brunsdon C锛� Charlton M. Geographically Weighted Regression鈥擳he Analysis of Spatially Varying Relationships锛籑锛�. Chichester.UK锛� John Wiley & Sons Inc. 2002锛� 42鈥�46. doi锛�/ 10.1107/S0108270198099594.
16	Wang Yuanfei锛� He Honglin. Spatial Data Analysis Method锛籑锛�. Beijing锛� Science Publishing House锛� 2007锛� 132-141.
16	鐜嬭繙椋烇紝浣曟椽鏋�. 绌洪棿鏁版嵁鍒嗘瀽鏂规硶锛籑锛�. 鍖椾含锛� 绉戝鍑虹増绀撅紝 2007锛�132鈥�141.
17	Qu Mingkai锛� Li Weidong锛� Zhang Chuanrong锛� et al. Geographically Weighted Regression and Its Application Prospect in Soil and Environmental Sciences锛籎锛�. Soils锛� 2014锛� 46锛�1锛夛細 15鈥�22.
17	鐬挎槑鍑紝鏉庡崼涓滐紝寮犱紶鑽ｏ紝绛�. 鍦扮悊鍔犳潈鍥炲綊鍙婂叾鍦ㄥ湡澹ゅ拰鐜绉戝涓婄殑搴旂敤鍓嶆櫙锛籎锛�. 鍦熷￥锛� 2014锛� 46锛�1锛夛細 15鈥�22.
18	Liu Qiongfeng锛� Li Mingde锛� Duan Jiannan锛� et al. Geographically Weighted Regression Model Analysis of Factors Affecting Lead and Cadmium Content in Farmland Soils锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering锛� 2013锛� 29锛�3锛夛細 225鈥�234.
18	鍒樼惣宄帮紝鏉庢槑寰凤紝娈靛缓鍗楋紝绛�. 鍐滅敯鍦熷￥閾呫�侀晧鍚噺褰卞搷鍥犵礌鍦扮悊鍔犳潈鍥炲綊妯″瀷鍒嗘瀽锛籎锛�. 鍐滀笟宸ョ▼瀛︽姤锛� 2013锛� 29锛�3锛夛細 225鈥�234.
19	Liu Ning. Research on the GWR Model Inversion of Heavy Metal Content in Cultivated Land Soil in Mining Areas 锛籇锛�. Changsha锛欳hangsha University of Science and Technology锛� 2018.
19	鍒樺畞. 鐭垮尯鑰曞湴鍦熷￥閲嶉噾灞炲惈閲廏WR妯″瀷鍙嶆紨鐮旂┒锛籇锛�. 闀挎矙锛氶暱娌欑悊宸ュぇ瀛︼紝 2018.
20	Xiang M锛� Jiang H L锛� Hua T Z锛� et al. Zircon U-Pb Shrimp Ages from The Late Paleozoic Turpan - Hami Basin锛� NW China 锛籎锛�. Journal of Earth Science锛� 2014锛� 25锛�5锛夛細 924鈥�931. doi锛�/10.1007/S12583-014-0484-9.
21	Technical Rules for Monitoring of Environmental Quality of Farmland Soil锛� 锛籗锛�. Beijing锛� Agricultural Press of China锛� 2012.
21	鍐滅敯鍦熷￥鐜璐ㄩ噺鐩戞祴鎶�鏈鑼冿細锛籗锛�. 鍖椾含锛� 涓浗鍐滀笟鍑虹増绀撅紝 2012.
22	National Environmental Protection Agency. Soil Quality鈥擠etermination of Lead锛� Cadmium锛岋蓟S锛�. Beijing锛� Standards Press of China锛� 1997.鍥藉鐜淇濇姢灞�鍦熷￥璐ㄩ噺鈥斺�旈搮銆侀晧鐨勬祴瀹氾細锛籗锛�. 鍖椾含锛氫腑鍥芥爣鍑嗗嚭鐗堢ぞ锛� 1997.
23	Liu Ke锛� Zhao Wenji锛� Guo Xiaoyu锛� et al. Research on The Estimation of Total Nitrogen Content of Wetland Plants based on Ground-measured Spectra锛籎锛�. Spectroscopy and Spectral Analysis锛� 2012锛� 32锛�2锛夛細 465鈥�471.
23	鍒樺厠锛� 璧垫枃鍚夛紝閮�嶅畤锛� 绛�. 鍩轰簬鍦伴潰瀹炴祴鍏夎氨鐨勬箍鍦版鐗╁叏姘惈閲忎及绠楃爺绌讹蓟J锛�. 鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛� 2012锛� 32锛�2锛夛細 465鈥�471.
24	Kahal Yasenjiang 锛� Sawuti Rukya 锛� Kasmu Nigati 锛� et al. Optimization Estimation of Heavy Metal Content in Soil of Open-pit Coal Mine Area based on Spectral Index锛籎锛�. Spectroscopy and Spectral Analysis锛� 2019锛� 39锛�8锛夛細 2486鈥�2494.
24	浜氭．姹熉峰杸鍝堝皵锛岃尮鍏嬩簹路钀ㄥ惥鎻愶紝灏煎姞鎻惵峰崱鏂湪锛岀瓑. 浼樺寲鍏夎氨鎸囨暟鐨勯湶澶╃叅鐭垮尯鍦熷￥閲嶉噾灞炲惈閲忎及绠楋蓟J锛�. 鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛� 2019锛� 39锛�8锛夛細 2486鈥�2494.
25	Wang Xueshun锛� Qi Dawei锛� Huang Anmin. Research on Denoising of Wood Near Infrared Spectroscopy based on Wavelet Transform 锛籎锛�. Spectroscopy and Spectral Analysis锛� 2009锛� 29锛�8锛夛細 2059鈥�2062.
25	鐜嬪椤猴紝鎴氬ぇ浼燂紝榛勫畨姘�. 鍩轰簬灏忔尝鍙樻崲鐨勬湪鏉愯繎绾㈠鍏夎氨鍘诲櫔鐮旂┒锛籎锛�. 鍏夎氨瀛︿笌鍏夎氨鍒嗘瀽锛� 2009锛� 29锛�8锛夛細 2059鈥�2062.
26	Jaber S M锛� Al-Qinna M I. Global and Local Modeling of Soil Organic Carbon Using Thematic Mapper Data in A Semi-arid Environment锛籎锛�. Arabian Journal of Geosciences锛� 2015锛� 8锛� 3159鈥�3169. doi锛�/ 10.1007/s12517-014-1370-6
27	Wang K锛� Zhang C R锛� Li W D锛� et al. Mapping Soil Organic Matter with Limited Sample Data Using Geographically Weighted Regression锛籎锛�. Journal of Spatial Science锛� 2014锛� 59锛� 91鈥�106. doi锛�/ 10.1080/14498596.2013.812024
28	Ministry of Ecological Environment. Soil Environmental Quality鈥擱isk Control Standard for Soil Contamination of Agricultural Land锛� 锛籗锛�. Beijing锛� Standards Press of China锛� 2018. 锛诲浗瀹剁敓鎬佺幆澧冮儴. 鍦熷￥鐜璐ㄩ噺鈥斿啘鐢ㄥ湴鍦熷￥姹℃煋椋庨櫓绠℃帶鏍囧噯锛� 锛籗锛�. 鍖椾含锛� 涓浗鏍囧噯鍑虹増绀撅紝 2018.锛�
29	Pu Ruiliang锛� Gong Peng. Hyperspectral Remote Sensing and Its Application锛籑锛�. Beijing锛� Higher Education Press锛� 2000锛� 23鈥�46.
29	娴︾憺鑹紝瀹箯. 楂樺厜璋遍仴鎰熷強鍏跺簲鐢蓟M锛�. 鍖椾含锛� 楂樼瓑鏁欒偛鍑虹増绀撅紝 2000锛� 23鈥�46.
30	Xie Xianli锛� Sun Bo锛� Hao Hongtao. Relationship between Visible-near Infrared Reflectance Spectroscopy and Heavy Metal of Soil Concentration锛籎锛�. Acta Pedologica Sinica锛� 2007锛� 44锛�6锛夛細 982-993.
30	瑙ｅ涓斤紝瀛欐尝锛� 閮濈孩娑�. 鍦熷￥鍙鍏夆�旇繎绾㈠鍙嶅皠鍏夎氨涓� 閲嶉噾灞炲惈閲忎箣闂寸殑鐩稿叧鎬э蓟J锛�. 鍦熷￥瀛︽姤锛� 2007锛� 44锛�6锛夛細 982-993.
31	Mao Haitao锛� Huang Qinghao锛� Long Shunjiang锛� et al. Design and Experiment of Protective Blanket for Soil Salinization Control锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering 锛圱ransactions of the CSAE锛夛紝2015锛�31锛�17锛夛細 121-127.
31	姣涙捣娑涳紝榛勫簡璞紝榫欓『姹燂紝绛�.鍦熷￥鐩愭笉鍖栨不鐞嗛槻鎶ゆ鐨勭爺鍙戝強璇曢獙锛籎锛�.鍐滀笟宸ョ▼瀛︽姤锛�2015锛�31锛�17锛夛細121-127.
32	Yu Lei锛� Hong Yongsheng锛� Geng Lei锛� et al. Hyperspectral Estimation of Soil Organic Matter Content based on Partial Least Squares Regression锛籎锛�. Transactions of the Chinese Society of Agricultural Engineering锛� 2015锛� 31锛�14锛夛細103-109.
32	浜庨浄锛� 娲案鑳滐紝鑰块浄锛岀瓑.鍩轰簬鍋忔渶灏忎簩涔樺洖褰掔殑鍦熷￥鏈夋満璐ㄥ惈閲忛珮鍏夎氨浼扮畻锛籎锛�. 鍐滀笟宸ョ▼瀛︽姤锛�2015锛�31锛�14锛夛細103-109.

[1]	Miao CHE, Hairong WANG, Xi XU, Chong SUN. PSO-DF: A Hyperspectral Model for Estimating Nitrogen Content in Rice Leaves [J]. Remote Sensing Technology and Application, 2024, 39(2): 280-289.
[2]	Mei LU, Jiatian LI, Wen LI, Mihong HU, Jiaxin YANG. Fusion of Multiscale Low-rank Representation and Two Way Recursive Filtering for Hyperspectral Image Classification [J]. Remote Sensing Technology and Application, 2024, 39(2): 393-404.
[3]	Shuwei WANG,Qingtai SHU,Xu MA,Jingnan XIAO,Wenwu ZHOU. Research Progress in Data Fusion of LiDAR and Hyperspectral Imaging Technology [J]. Remote Sensing Technology and Application, 2024, 39(1): 11-23.
[4]	Qing GUO, Lifu ZHANG, Wenchao Qi, Linshan ZHANG. The Hyperspectral Inversion Method of Main Ionic Compounds Content in Groundwater based on BP Neural Network [J]. Remote Sensing Technology and Application, 2024, 39(1): 149-159.
[5]	Nile WU,Yulong BAO,Rentuya BU,Buxinbayaer TU,Saixiyalatu TAO,Yuhai BAO,Eerdemutu JIN. Identification of Typical Grassland Degradation Indicator Species based on UAV Hyperspectral Remote Sensing [J]. Remote Sensing Technology and Application, 2024, 39(1): 248-258.
[6]	Yuxin TIAN,Zhenghai WANG,Peng XIE. Quantitative Hyperspectral Inversion of Soil Heavy Metals based on Feature Screening Combined with PSO-BPNN and GA-BPNN Algorithms [J]. Remote Sensing Technology and Application, 2024, 39(1): 259-268.
[7]	Xingwen LIU,Qiaoling LI,Shuhong SONG. Fusion and Downscaling of Multi-source Remote Sensing Soil Moisture based on 2D Triple-Collocation and Machine Learning Methods [J]. Remote Sensing Technology and Application, 2023, 38(6): 1317-1327.
[8]	Yanhong HANG,Mengning SONG,Jia DU,Huanjun LIU,Linghua MENG,Shuo WANG,Qiong LIU. Remote Sensing Estimation and Influencing Factors of Root Zone Soil Moisture of Typical Cultivated Land in Black Soil Region of Northeast China [J]. Remote Sensing Technology and Application, 2023, 38(6): 1328-1337.
[9]	Yuhui ZHANG,Chula SA,Fanhao MENG,Min LUO,Mulan WANG,Hui SUN. Response Characteristics of Vegetation Reforestation Period to Climate, Snow Cover and Soil Water in Mongolian Plateau [J]. Remote Sensing Technology and Application, 2023, 38(6): 1338-1349.
[10]	Qilian YUAN,Lin ZHAO,Zanpin XING,Defu ZOU,Jianting ZHAO,Shibo LIU,Lei FAN. Thawing Period High Resolution Surface Soil Moisture Retrieval over the Permafrost Region along Qinghai-Tibet Engineering Corridor [J]. Remote Sensing Technology and Application, 2023, 38(6): 1350-1363.
[11]	Li MA,Yunsheng LOU,Yong WANG,Xiaojun YANG,Jun LI,Yihao MA,Honge SHA,Rui LI,Zhen ZHANG. Effects of Sowing Dates on N₂O Emissions from Winter Wheat Fields under Different Shading Conditions [J]. Remote Sensing Technology and Application, 2023, 38(6): 1390-1401.
[12]	Xinyuan ZHANG,Xiaoying LI,Tianhai CHENG,Shuanghui LIU,Yuhang GUO. Review of NO₂ Retrieval of Satellite-borne Hyperspectral Sensors and Accuracy Assessment of Tropospheric NO₂ Column Density Products [J]. Remote Sensing Technology and Application, 2023, 38(5): 1148-1158.
[13]	Zhijun ZHANG,Ru WANG,Yue YAO,Chengyan DU,Qian SHEN. Retrieval Sudy of Total Suspended Matter Concentration in Qinghai Lake based on ZY1 02D Hyperspectral Satellite Images [J]. Remote Sensing Technology and Application, 2023, 38(5): 1159-1166.
[14]	Shiying DONG,Tianjun WU,Sijia JIAO. Geo-parcel Airborne Level Hyperspectral Remote Sensing Land Cover Mapping and Accuracy Assessment [J]. Remote Sensing Technology and Application, 2023, 38(2): 353-361.
[15]	Zhijun LIU,Lijuan CUI,Wei LI,Zhiguo DOU,Yinru LEI,Xu PAN,Jing LI,Xinsheng ZHAO,Xiajie ZHAI. Research on Hyperspectral Data of Ecological Stoichiometric Characteristics of Suaeda salsa in Liaohe Estuary based on Machine Model [J]. Remote Sensing Technology and Application, 2023, 38(1): 239-250.