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陈奕云

副教授 硕士生导师
性别：男
所在单位：资源与环境科学学院
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个人简介 陈奕云，男，1983年生，博士，副教授，硕士生导师，“351人才计划”珞珈青年****，中国人民大学土地政策与制度研究中心特聘研究员。2014年获得国家公派全额资助，赴剑桥大学地理系开展合作研究，在剑桥大学地理信息科学论坛GISc Forum和第五届全英博士生论坛闭幕式报告研究成果；2019年入选武汉大学青年拔尖人才出国（境）研修计划，赴剑桥大学发展研究中心开展合作研究，研究主题为人类世土壤安全与可持续发展。


陈奕云博士发表论文90篇，其中SCI论文60余篇，担任Remote Sensing of Environment、Land Degradation & Development、European Journal of Soil Science、Soil Science Society of America Journal等多个国际顶级学术期刊和土壤学报、湖泊科学、长江流域资源环境、光谱学与光谱分析、农业工程学报、地球信息科学学报、热带地理、植物科学学报等10多个中文核心期刊的评审专家。


陈奕云博士主持国家自然科学基金2项，国家重点研发子课题2项，中科院土壤与农业可持续发展国家重点实验室、地理国情监测国家测绘地理信息局重点实验室开放基金等课题多项。担任国家自然科学基金国际(地区)合作研究项目、比利时联邦科学政策办公室项目及深圳市科技进步奖等项目或奖励的评审专家。指导学生获得中国“互联网+”大学生创新创业大赛、中国高校地理科学展示大赛等多项竞赛奖励以及中国土壤遥感与信息专业委员会学术会议优秀论文一等奖和优秀报告奖。

Google Scholar：H index=20（Jan, 2020）
ResearchGate:Score=33.03 （Jan, 2020）



工作经历
2015.11～ ，武汉大学，资源与环境科学学院，副教授。
2019.09～2020.09，剑桥大学，发展研究中心，武汉大学“青年拔尖人才培养出国（境）研修计划”，合作导师：Shailaja Fennell博士。

2016.07～ ，土壤与农业可持续发展国家重点实验室(中国科学院南京土壤研究所)，客座项目研究员。
2017.01～ ，武汉大学深圳研究院，测绘遥感信息工程国家重点实验室深圳研发中心，副教授。


2014.07～2015.01，剑桥大学，地理系，国家公派访问****，合作导师：Bob Haining教授。

2014.01～2017.06，武汉大学苏州研究院，客座项目研究员。
2014.06～2015.06，卫星测绘技术与应用国家测绘地理信息局重点实验室（国家测绘地理信息局），客座项目研究员。
2011.09～2015.10，武汉大学，资源与环境科学学院，讲师。
2011.09～2013.08，武汉大学，地理学博士后流动站，师资博士后，合作导师：林爱文教授。


学习经历
2008.09～2011.06，武汉大学，土地资源管理，管理学博士，导师：刘耀林教授。
2006.09～2008.06，武汉大学，土地资源管理，管理学硕士，导师：刘艳芳教授。
2006.09～2008.03，荷兰ITC，自然资源管理系（NRM），地理信息科学与遥感应用，硕士（MSc），导师：Mannaerts, C.M.博士。
2002.09～2006.06，武汉大学，资源环境与城乡规划管理，理学学士。


研究兴趣

综合人文与自然地理，理解人类行为决策的生态环境效应；
借助空间分析与高光谱传感，揭示土壤属性的时空变化规律与谱学特征；
追随人工智能潮流，发展其在医学图像诊断和自然资源资产管理中的应用。


招生方向
招收人文地理学、自然地理学及测绘工程（含非全日制）专业硕士研究生

研究生培养方向：

1、地理信息科学与可持续发展研究；
2、土壤近地传感与数字土壤制图；
3、土地利用与景观生态、村土地利用规划。
欢迎具备良好中英文阅读、写作及沟通能力的相关专业学生及具备数学建模、化学实验分析能力的跨专业学生咨询联系。


发表论文
2020年：
[1] L. Guo, P. Fu, T. Shi, Y. Chen, H. Zhang, R. Meng, S. J. S. Wang, T. Research, 2020. Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images. Soil and Tillage Research. 196, 104477.
2019年：
[1] H. Cheng, R. Shen, Y. Chen*, Q. Wan*, T. Shi, J. Wang, Y. Wan, Y. Hong, X. Li, 2019. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma. 336, 59-67.
[2] Y. Hong, Y. Liu, Y. Chen*, Y. Liu*, L. Yu, Y. Liu, H. Cheng, 2019. Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy. Geoderma. 337, 758-769.
[3] Y. S. Hong, R. L. Shen, H. Cheng, Y. Y. Chen*, Y. Zhang, Y. L. Liu, M. Zhou, L. Yu, Y. Liu, Y. F. Liu*, 2019. Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest. Science of the Total Environment. 651, 1969-1982.
[4] Y. Hong, S. Chen, Y. Liu, Y. Zhang, L. Yu, Y. Chen*, Y. Liu*, H. Cheng, Y. Liu, 2019. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. CATENA. 174, 104-116.
[5] Y. Liu, Y. Liu, Y. Chen*, Y. Zhang*, T. Shi, J. Wang, Y. Hong, T. Fei, Y. Zhang, 2019. The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy. Remote Sensing. 11(4), 450.
[6] Y. Zhang, Y. Liu*, M. Jin, Y. Jing, Y. Liu, Y. Liu, W. Sun, J. Wei, Y. Chen*, 2019. Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data. Sensors. 19(3), 743.
[7] L. Guo, H. Zhang, Y. Chen, J. Qian, 2019. Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques. Chinese Geographical Science. 29(2), 258-269.
[8] L. Guo, M. Linderman, T. Z. Shi, Y. Y. Chen, L. J. Duan, H. T. Zhang, 2018. Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling. Remote Sensing. 10(6).
[9] X. Zhang, X. Zhang, C. Luo, Z. Liu, Y. Chen, S. Dong, C. Jiang, S. Yang, F. Wang, X. Xiao, Volume-Enhanced Raman Scattering Detection of Viruses. Small. 0(0), **. https://doi.org/10.1002/smll.
[10] L. Guo, H. T. Zhang, T. Z. Shi,Y. Y. Chen, Q. H. Jiang, M. Linderman, 2019. Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma. 337, 32-41.
[11] L. Guo, T. Z. Shi, M. Linderman, Y. Y. Chen, H. T. Zhang, P. Fu, 2019. Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging. Remote Sensing. 11(9), 16.
[12] Y. C. S. Zhang, L. Guo, Y. Y. Chen, T. Z. Shi, M. Luo, Q. L. Ju, H. T. Zhang, S. Q. Wang, 2019. Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. Remote Sensing. 11(14), 26.
[13] Z. Wu, B. Wang, J. Huang, Z. An, P. Jiang, Y. Chen*, Y. Liu*, 2019. Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model. Soil and Tillage Research. 195, 104381.
[14] Y. Hong, R. Shen, H. Cheng, S. Chen, Y. Chen*, L. Guo, J. He, Y. Liu*, L. Yu, Y. Liu, 2019. Cadmium concentration estimation in peri-urban agricultural soils: Using reflectance spectroscopy, soil auxiliary information, or a combination of both? Geoderma. 354, 113875.
[15] C. Cheng, Y. L. Liu, Y. Y. Chen, Y. F. Liu, Y. Zhang, S. S. Shen, R. F. Yang, Z. B. Xu, Y. S. Hong, 2019. Diagnosing cropland's allowable range and spatial allocation in China's typical mountainous plateau area: An evaluation framework based on ecological carrying capacity. Science of the Total Environment. 685, 1255-1268.
[16] 陈方圆, 周鑫,陈奕云, 王奕涵, 刘会增, 王俊杰, 邬国锋, 2019. 不同核函数支持向量机和可见-近红外光谱的多种植被叶片生化组分估算. 光谱学与光谱分析. 39(02), 428-434.
[17] 王雯倩, 万其进,陈奕云*, 王新智, 陈敏, 余珮珩, 万远, 2019, 典型场地有机污染物多环芳烃的红外光谱特征. 红外技术. 41(10), 982-988.
[18] 江颂, 蒙吉军,陈奕云, 2019. 黑河中游土地利用与景观格局的水文效应分析. 中国水土保持科学. 17(01), 64-73.
2018年：
[1] J. Wang, Y. Chen, F. Chen, T. Shi, G. Wu, 2018. Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration. Agricultural and Forest Meteorology. 248, 306-315.
[2] Y. Hong, L. Yu, Y. Chen*, Y. Liu*, Y. Liu, Y. Liu, H. Cheng, 2018. Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture. Remote Sensing. 10(1), 28. (doi:10.3390/rs**)
[3] J. Wan, Y. Liu*,Y. Chen*, J. Hu, Z. Wang, 2018. A Tale of North and South: Balanced and Sustainable Development of Primary Education in Ningxia, China. Sustainability. 10(2), 559.
[4] T. Shi, L. Guo, Y. Chen, W. Wang, Z. Shi, Q. Li, G. Wu, 2018. Proximal and remote sensing techniques for mapping of soil contamination with heavy metals. Applied Spectroscopy Reviews, 1-23.
[5] Y. Hong, Y. Chen*, L. Yu, Y. Liu, Y. Liu, Y. Zhang, Y. Liu, H. Cheng, 2018. Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy. Remote Sensing. 10(3), 479.
[6] Y. S. Hong, S. C. Chen, Y. Zhang, Y. Y. Chen*, L. Yu, Y. F. Liu*, Y. L. Liu, H. Cheng, Y. Liu, 2018. Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine. Science of the Total Environment. 644, 1232-1243.
[7] Y. Zhang, Y. F. Liu, Y. Zhang, Y. Liu, G. X. Zhang, Y. Y. Chen*, 2018. On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China. Science of the Total Environment. 637, 780-790.
[8] Y. S. Hong, Y. Y. Chen*, Y. Zhang, Y. F. Liu*, Y. L. Liu, L. Yu, Y. Liu, H. Cheng, 2018. Transferability of Vis-NIR models for Soil Organic Carbon Estimation between Two Study Areas by using Spiking. Soil Science Society of America Journal. 82(5), 1231-1242.
[9] T. Shi, Z. Hu, Z. Shi, L. Guo, Y. Chen, Q. Li, G. Wu, 2018. Geo-detection of factors controlling spatial patterns of heavy metals in urban topsoil using multi-source data. Science of The Total Environment. 643, 451-459
[10] L. Guo, M. Linderman, T. Z. Shi, Y. Y. Chen, L. J. Duan, H. T. Zhang, 2018. Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling. Remote Sensing. 10(6).
[11] Y. L. Liu, Z. H. Wu*, Y. Y. Chen*, B. Z. Wang, 2018. SOIL CARBON MAPPING IN LOW RELIEF AREAS WITH COMBINED LAND USE TYPES AND PERCENTAGES. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. IV-3, 285-292.
[12] Y. Liu, Z. Shi, G. L. Zhang, Y. Y. Chen*, S. Li, Y. S. Hong, T. Z. Shi, J. J. Wang, Y. L. Liu*, 2018. Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library. Remote Sensing. 10(11).
[13] 吴子豪, 刘艳芳, 陈奕云*, 郭龙, 姜庆虎, 王少辰, 2018. 综合土地利用及空间异质性的土壤有机碳空间插值模型. 应用生态学报, 29(1), 238-246
[14] 程航, 万远, 陈奕云*, 万其进*, 石铁柱, 申锐莉, 郭 凯, 胡家蒙, 2018. 部分土壤重金属可见-近红外反射光谱特征及机理研究. 光谱学与光谱分析, 38(3), 771-778
[15] 王凤, 刘艳芳, 孔雪松, 陈奕云*, 潘佳威, 2018. 中国县域粮食产量时空演变及影响因素变化. 经济地理(05), 142-151.
[16] 唐名阳, 陈奕云, 刘艳芳*, 陈龙, 张翔晖, 2018. 宁波市基本教育设施均等化研究——以公立小学为例. 现代城市研究(05), 24-32.
[17] 温久民, 韩健, 陈奕云, 2018. 喀斯特山区无人机摄影测量技术研究. 测绘地理信息. 43(05), 32-34.
2017年：
[1] 陈奕云, 齐天赐, 黄颖菁, 万远, 赵瑞瑛, 亓林, 张超, 费腾, 2017. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法. 农业工程学报 33(06), 107-114.
[2] 陈奕云, 赵瑞瑛, 齐天赐, 亓林, 张超, 2017. 结合光谱变换和 Kennard-Stone 算法的水稻土全氮光谱估算模型校正集构建策略研究. 光谱学与光谱分析37(7), 2133-2139
[3] 刘艳芳, 宋玉玲, 郭龙, 陈奕云*, 卢延年, 刘以, 2017. 结合高光谱信息的土壤有机碳密度地统计模型. 农业工程学报(02), 183-191.
[4] 曾晨, 柯新利, 王鹏, 马才学, 陈奕云, 2017. 多学科背景下一流土地资源管理本科专业的建设. 中国地质教育(02), 16-19.
[5] X. Wang, Y. Chen*, L. Guo, L. Liu*, 2017. Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter. Remote Sensing. 9(3), 201.
[6] T. Shi, H. Liu, Y. Chen, T. Fei, J. Wang, G. Wu, 2017. Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils. Sensors. 17(5), 1036.
[7] K. Guo, X. Kuai, Y. Chen, L. Qi, L. Zhang, Y. Liu, 2017. Risk assessment of land ecology on a regional scale: Application of the relative risk model to the mining city of Daye, China. Human and Ecological Risk Assessment. 23(3), 550-574.
[8] H. Liu, T. Shi, Y. Chen, J. Wang, T. Fei, G. Wu, 2017. Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression. Remote Sensing. 9(1), 29.
[9] L. Guo, C. Zhao, H. Zhang, Y. Chen, M. Linderman, Q. Zhang, Y. Liu, 2017. Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology. Geoderma. 285, 280-292.
[10] Y. Liu, Y. Jing* , E. Cai, J. Cui, Y. Zhang, Y. Chen*, 2017. How Leisure Venues Are and Why? A Geospatial Perspective in Wuhan, Central China. Sustainability. 10(1), 28.
[11] L. Guo, Y. Chen, T. Shi, C. Zhao, Y. Liu, S. Wang, H. Zhang, 2017. Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density. ISPRS International Journal of Geo-Information. 6(10), 308
2016年：
[1] 刘艳芳, 卢延年, 郭龙, 肖丰涛, 陈奕云*, 2016. 基于地类分层的土壤有机质光谱反演校正样本集的构建. 土壤学报53(02), 332-341.
[2] 陈奕云, 唐名阳, 王淑桃, 王奇, 詹皖欣, 黄冠, 2016. 基于文献计量的中国农田土壤重金属污染评价. 土壤通报46(06), 669-775.
[3] 李时雨, 刘艳芳, 孔雪松, 陈奕云, 刘格格, 2016. 武汉市生态用地景观格局的时空演化特征分析. 测绘地理信息 41(03), 68-73.
[4] 刘会增, 石铁柱, 王俊杰, 陈奕云, 邬国锋, 2016. 利用区域土壤光谱库研究土壤有机碳反演模型传递性. 武汉大学学报(信息科学版) 41(07), 889-895.
[5] 张梦珂, 刘艳芳, 安子豪, 危小建, 陈奕云, 2016. 武汉城市化进程对湖泊景观格局的空间非平稳性影响分析. 地理信息世界23(03), 53-59.
[6] Q. Jiang, Y. Chen*, L. Guo, T. Fei, K. Qi, 2016. Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy. Remote Sensing. 8(9), 755.
[7] X. Wang, Y. Liu, Y. Chen, Y. Liu, 2016. An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns. ISPRS International Journal of Geo-Information. 5(11), 205.
[8] T. Shi, H. Liu, Y. Chen, J. Wang, G. Wu, 2016. Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. Journal of Hazardous Materials. 308, 243-252.
[9]陈奕云,齐天赐,黄颖菁,万远,赵瑞瑛,亓林,张超, 2016.土壤有机质可见-近红外光谱反演模型校正集构建,中国地理信息科学理论与方法学术年会,深圳，广东.
2015年：
[1] Y. Liu, L. Guo, Q. Jiang, H. Zhang, Y. Chen*, 2015. Comparing geospatial techniques to predict SOC stocks. Soil & Tillage Research. 148, 46-58.
[2] T. Shi, Y. Chen, Y. Liu, G. Wu, 2014. Response to “Visible and near-infrared reflectance spectroscopyis of limited practical use to monitor soil contamination by heavy metals” by Philippe C. Baveye. Journal of Hazardous Materials. 285(0), 207.
[3] P. Zhou, Y. F. Liu, Y. Y. Chen, C. Zeng, Z. Y. Wang, 2015. Prediction of the spatial distribution of high-rise residential buildings by the use of a geographic field based autologistic regression model. Journal of Housing and the Built Environment. 30(3), 487-508.
[4] 陈奕云, 漆锟, 刘耀林, 何建华, 姜庆虎, 2015. 顾及土壤湿度的土壤有机质高光谱预测模型传递研究. 光谱学与光谱分析. 35(6), 1705-1708.
[5] Y. Chen, T. Fei, L. Guo, T. Shi, Q. Jiang, Y. Liu, 2015. Spatial variation of soil reflectance spectra and its implication for digital soil mapping, The 4th Global Workshop Proximal Soil Sensing, Hangzhou, China.
2014年：
[1] Y. Liu, Q. Jiang, T. Fei, J. Wang, T. Shi, K. Guo, X. Li, Y. Chen*, 2014. Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes. Remote Sensing. 6(5), 4305-4322.
[2] Y. Liu, Q. Jiang, T. Shi, T. Fei, J. Wang, G. Liu, Y. Chen*, 2014. Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy. Acta Agriculturae Scandinavica, Section B–Soil & Plant Science 64(3), 267-281.
[3] T. Shi, Y. Chen, H. Liu, J. Wang, G. Wu, 2014. Soil Organic Carbon Content Estimation with Laboratory-Based Visible-Near-Infrared Reflectance Spectroscopy: Feature Selection. Applied Spectroscopy. 68(8), 831-837.
[4] T. Shi, Y. Chen, Y. Liu, G. Wu, 2014. Visible and near-infrared reflectance spectroscopy—An alternative for monitoring soil contamination by heavy metals. Journal of Hazardous Materials. 265(0), 166-176.
[5] J. Wang, L. Cui, W. Gao, T. Shi, Y. Chen, Y. Gao, 2014. Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma. 216(0), 1-9.
[6] X. Peng, T. Shi, A. Song, Y. Chen, W. Gao, 2014. Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods. Remote Sensing. 6(4), 2699-2717.
[7] T. Shi, H. Liu, J. Wang, Y. Chen, T. Fei, G. Wu, 2014. Monitoring Arsenic Contamination in Agricultural Soils with Reflectance Spectroscopy of Rice Plants. Environmental Science & Technology. 48(11), 6264-6272.
[8] K. Guo, Y. Liu, C. Zeng, Y. Chen, X. Wei, 2014. Global research on soil contamination from 1999 to 2012: A bibliometric analysis. Acta Agriculturae Scandinavica, Section B–Soil & Plant Science. 64(5), 377-391.
[9] Y. Gao, L. Cui, B. Lei, Y. Zhai, T. Shi, J. Wang, Y. Chen, H. He, G. Wu, 2014. Estimating soil organic carbon content with visible/near-infrared (Vis/NIR) spectroscopy. Applied Spectroscopy. 68(7), 712-722.
[10] X. Kong, Y. Liu, X. Liu, Y. Chen, D. Liu, 2013. Thematic maps for land consolidation planning in Hubei Province, China. Journal of Maps, 10(1), 26-34.
[11] 刘艳芳, 方佳琳, 陈晓慧, 陈奕云*, 2014. 基于确定性系数分析方法的秭归县滑坡易发性评价. 自然灾害学报. 23(6), 209-217.
[12] 卢延年, 刘艳芳, 陈奕云, 姜庆虎, 2014. 江汉平原土壤有机碳含量高光谱预测模型优选. 中国农学通报. 30(26), 127-133.
[13] 韩健, 陈奕云, 潘元进, 文鸿雁, 2014. 国土档案管理系统的开发与应用研究. 地理空间信息. 12(4), 177-179
2013年：
[1] Y. Liu, X. Kong, Y. Liu, Y. Chen, 2013. Simulating the Conversion of Rural Settlements to Town Land Based on Multi-Agent Systems and Cellular Automata. PloS one. 8(11), e79300.
[2] T. Shi, L. Cui, J. Wang, T. Fei, Y. Chen, G. Wu, 2013. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant and soil. 366(1), 363-375.
[3] Y. Chen, Y. Liu*, T. Fei, Q. Jiang, J. Wang, T. Shi, 2013. Statistical Understanding on the Pre-processing of VNIR Spectra Data from Soil Samples with Different Preparations In: F. HU (Editor), International Symposium on Satellite Mapping Technology and Application (ISSMTA2013), Nanjing, Jiangsu, China, pp. 32 - 37.
[4] 严金明, 张杨, 江平, 陈奕云, 2013. 论国土生态文明战略框架设计, 新疆师范大学学报（哲学社会科学版）, 34(6), 25-31.
[5] 卢有赓, 陈奕云*, 柳登科, 陈鹏程, 2013. 滨湖农田土壤全氮含量可见近红外光谱反演. In: 史舟, 王秋兵 (Editors), 中国土壤学会土壤遥感与信息专业委员会/土壤发生、分类与土壤地理专业委员会学术研讨会, 山西太谷.
[6] 柳登科, 陈奕云*, 卢有赓, 宛子翔, 2013. 矿区土壤硫的高光谱遥感反演方法初探. In: 史舟, 王秋兵 (Editors), 中国土壤学会土壤遥感与信息专业委员会/土壤发生、分类与土壤地理专业委员会学术研讨会, 山西太谷.
2012年：
[1] Y. Chen, Y. Liu, Y. Liu, A. Lin, X. Kong, D. Liu, X. Li, Y. Zhang, Y. Gao, D. Wang, 2012. Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le’an River Floodplain, China. International Journal of Environmental Research and Public Health. 9(5), 1874-1886.
[2] Y. Liu, Y. Chen, 2012. Estimation of total iron content in floodplain soils using VNIR spectroscopy – a case study in the Le'an River floodplain, China. International Journal of Remote Sensing. 33(18), 5954-5972.
[3] Y. Liu, Y. Chen, 2012. Feasibility of Estimating Cu Contamination in Floodplain Soils using VNIR Spectroscopy—A Case Study in the Le’an River Floodplain, China. Soil & Sediment Contamination. 21(8), 951-969.
[4] Y. Liu, D. Liu, Y. Liu, J. He, L. Jiao, Y. Chen, X. Hong, 2012. Rural land use spatial allocation in the semiarid loess hilly area in China: Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques. Science China Earth Sciences. 55(7), 1166-1177.
[5] Y. Zhang, Y. Chen, Q. Ding, P. Jiang, 2012. Study on Urban Heat Island Effect Based on Normalized Difference Vegetated Index: A Case Study of Wuhan City. Procedia Environmental Sciences. 13, 574-581.
[6] X. Zhao, Y. Liu, X. Ma, Y. Chen, 2012. A knowledge-based approach for assessing the quality of Landsat water body mapping product. International Symposium on Geomatics for Integrated Water Resources Management (GIWRM), 2012 (1-5).




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陈奕云

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论文成果More>> [1]. Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le'an River Floodplain, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 9 (5). 2012.
[2]. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法. 农业工程学报. 33 (6). 2017.
[3]. Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis. 37 (7). 2017.
[4]. 基于文献计量的中国农田土壤重金属污染评价. 土壤通报. 47 (0564-3945). 2016.
[5]. Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le'an River Floodplain, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 9 (1660-4601). 2012.
[6]. 结合光谱变换和Kennard-Stone算法的水稻土全氮光谱估算模型校正集构建策略研究. 光谱学与光谱分析. (1000-0593). 2017.

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[1]. Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le'an River Floodplain, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 9 (5). 2012.
[2]. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法. 农业工程学报. 33 (6). 2017.
[3]. Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis. 37 (7). 2017.
[4]. 基于文献计量的中国农田土壤重金属污染评价. 土壤通报. 47 (0564-3945). 2016.
[5]. Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le'an River Floodplain, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 9 (1660-4601). 2012.
[6]. 结合光谱变换和Kennard-Stone算法的水稻土全氮光谱估算模型校正集构建策略研究. 光谱学与光谱分析. (1000-0593). 2017.
[7]. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法. 农业工程学报. (1002-6819). 2017.

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