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杨琳，教授，博士生导师，入选国家级青年人才项目。研究方向为关键地表要素时空模拟及其驱动分析。提取了易用的人类活动环境因子，创立了低成本、高精度的多等级代表性采样方法，和基于少量代表性样点的土壤环境关系构建方法，发展了利用代表性样点进行高精度土壤制图的方法体系，基于因果分析探索了自然和人为对土壤碳和植被等的定量驱动作用。所创建的土壤采样和制图方法已被国内外同行所采用，并受到美国农作物、土壤及环境科学联盟（ACESS）数字图书馆报道。
近年来在国内外知名期刊发表学术论文100余篇。其中，第一/通讯作者SCI论文50篇（含中科院一区论文30篇），发表在Science Advances、ISPRS-J、ESSD、Environmental Research Letters、Geoderma、Catena、Soil & Tillage research、Atmospheric Chemistry and Physics 、Environmental pollution 和International Journal of Geographical Information Science等期刊。主持国家自然科学基金4项、重点基金子课题2项、863项目子课题等多项。获中国地理学会学术年会青年优秀论文奖、《土壤学报》年度优秀论文奖、江苏省高校微课教学比赛二等奖、“高校GIS创新人物”奖、特码 魅力导师奖等。
2014年当选国际土壤科学联合会土壤计量学会副主席，现任土壤计量学会评奖委员会委员。任中国自然资源学会资源制图专业委员会副主任委员，中国地理学会、中国地理信息产业协会和中国土壤学会等下属专业委员会委员。任一区SCI期刊Geoderma编委、《地理科学》编委和Frontier in Soil Science, the Pedometrics section期刊副主编，任全球数字土壤制图计划东亚节点技术专家、FAO“亚洲土壤伙伴计划”国际培训班授课讲师、第三次全国土壤普查技术指导专家等。
欢迎地理科学、自然地理、GIS、遥感、生态、数学等相关专业的学生报考硕士/博士研究生。
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一作/通作
Liu, J., Yang, L.*, Adams, J.M., Zhang, L., Wang, J., Wei, R., Zhou, C.H. Divergent biotic-abiotic mechanisms of soil organic carbon storage between bulk and rhizosphere soils of rice paddies in the Yangtze River Delta. Journal of Environmental Management. 2025， 389, 126179.
Cui, W.K., Yang, L.*, Zhang, L., Yang, C.C.H., Zhu, CX., Zhou, C.H. A Novel Approach of Generating Pseudo Revisited Soil Sample Data Based on Environmental Similarity for Space-Time Soil Organic Carbon Modelling. International Journal of Applied Earth Observation and Geoinformation, 2025, 139, 104542.
Zhang, L., Yang, L.*, Crowther, T. W., Zohner, C. M., Doetterl, S., Heuvelink, G. B. M., Wadoux, A. M. J.-C., Zhu, A.-X., Pu, Y., Shen, F., Ma, H., Zou, Y., and Zhou, C.H*. Mapping global distributions, environmental controls, and uncertainties of apparent top- and subsoil organic carbon turnover times, Earth System Science Data 2025, 17, 2605-2623. //doi.org/10.5194/essd-17-2605-2025.
Yang, C.C.H., Yang, L.*, Zhang, L., Shen, F.X., Li, S.F., Chen, Z.Q., Zhou, C. H. Applicability of three remote sensing based soil moisture variables for mapping soil organic matter in areas with different vegetation densities. Journal of Hydrology. 2025, 655, 132980.
Guo, M., Yang, L.*, Zhang, L., Shen, F.X., Meadows, M.E., Zhou, C. H. Hydrology, Vegetation, and Soil Properties as Key Drivers of Soil Organic Carbon in Coastal Wetlands: A High-Resolution Study. Environmental Science and Ecotechnology. 2025, 23, 100482.
Pu, Y., Yang, L.*, Zhang, L., Huang, H.L., Zhang, G.L., Zhou, C. H. Major contributions of agricultural management practices to topsoil organic carbon distribution and accumulation in croplands of East China over three decades. Agriculture, Ecosystems and Environment. 2024, 359, 108749.
Wang, W.Q., Guo, Y.P., Yang, L.*, Adams, J.M.*. Methanogen-methanotroph community has a more consistent and integrated structure in rice rhizosphere than in bulk soil and rhizoplane. Molecular Ecology. 2024. 00, e17416. //doi.org/10.1111/mec.17416.
Guo, Y.P., Kuzyakov, Y.,Li, N., Song, B., Liu, Z.H.,Adams, J.M.*, Yang, L.*Rice rhizosphere microbiome is more diverse but less variable along environmental gradients compared to bulk soil. Plant and Soil. 2024. //doi.org/10.1007/s11104-024-06728-1
Zhang, L.,Heuvelink, G.B.M.,Mulder,V.L.,Chen,S.C.,Deng, X.F.,Yang, L.*Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time. Science of the Total Environment. 2024, 922, 170778.
Yang, C.C.H., Yang, L.*, Zhang, L.,Zhou, C. H. Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables. Geoderma. 2023, 437, 116571.
Shen, F.X., Yang, L.*, Zhang, L., Guo, Mao., Huang, H.L., Zhou, C. H. Quantifying the direct effects of long-term dynamic land use intensity on vegetation change and its interacted effects with economic development and climate change in Jiangsu, China. Journal of Environmental Management. 2023, 325, 116562
Huang, H.L., Yang, L.*, Zhang, L., Pu, Y., Yang, C.C.H., Wu, Q., Cai, Y.Y., Shen, F.X., Zhou, C. H. A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect. Environmental Research Letters. 2022. DOI:10.1088/1748-9326/aca41e.
Shen, F.X., Yang, L.*, Zhang, L., Guo, Mao., Huang, H.L., Zhou, C. H. Quantifying the direct effects of long-term dynamic land use intensity on vegetation change and its interacted effects with economic development and climate change in Jiangsu, China. Journal of Environmental Management. 2023, 325, 116562
Guo, Y.P., Song, B., Li, A.Q., Wu, Q., Huang, H.L., Li, N., Yang, Y., Adams, J.M.*, Yang, L.*. Higher pH is associated with enhanced co-occurrence network complexity, stability and nutrient cycling functions in the rice rhizosphere microbiome. Environmental Microbiology. 2022, 1-20, //doi.org/10.1111/1462-2920.16185.
Zhang, L., Cai, Y.Y., Huang, H.L., Li, A.Q., Yang, L.*, Zhou, C. H. A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables. Remote Sensing, 2022, 14(18):4441.
Zhang, L., Yang, L.*, Zohner, C.M., Crowther, T.W., Li, M., Shen, F.X., Guo, M., Qin, J., Yao, L., Zhou, C. H.* Direct and indirect impacts of urbanization on vegetation growth across the world’s cities. Science Advances, 2022, 8, eabo0095. DOI: 10.1126/sciadv.abo0095
Guo, M., Yang, L.*, Shen, F.X., Zhang, L., Li, A.Q., Cai, Y.Y., Zhou, C.H. Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China. Geospatial Health, 2022, 17(s1).
Shao, S.S., Su, B.W., Zhang, Y.L., Gao, C., Zhang, M., Zhang, H.,Yang, L.*Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing. Geoderma, 2022, 413, 115749.
Wu, Q., Miao, S.Q., Huang, H.L., Guo, M., Zhang, L.,Yang, L.*, Zhou, C.H.Quantitative Analysis on CoastlineChanges of Yangtze River Deltabased on High Spatial ResolutionRemote Sensing Images. Remote Sensing, 2022, 14, 310. //doi.org/10.3390/rs14020310
Zhang, L., Yang, L.*, Cai, Y.Y., Huang, H.L., Shi, J.J., Zhou, C.H. A multiple soil properties oriented representative sampling strategy for digital soil mapping. Geoderma, 2022, 406, 115531.
Yang, L.,Cai, Y.Y., Zhang, L., Guo, M., Li, A.Q., Zhou, C.H*. A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables. International Journal of Applied Earth Observation and Geoinformation, 2021, 102 (6): 102428.
Shao, S.S., Zhang, H., Fan, M.M., Su, B.W., Wu, J.T., Zhang, M., Yang, L*, Gao, C.*Spatial-variability-based sample size allocation for stratified sampling. Catena, 2021, 206, 105509.
He, X.L., Yang, L*, Zhang, L., Li, A.Q., Shen, F.X., Cai, Y.Y., Zhou, C.H. Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images. Catena, 2021, 205, 105442.
Zhang, L., Yang, L*, Ma, T.W.,Shen, F.X.,Cai, Y.Y.,Zhou, C.H. A self-training semi-supervised machine learning method for predictive mapping of soil classes with limited sample data. Geoderma, 2021, 384, 114809.
Yang, L.,Shen, F.X., Zhang, L., Cai, Y.Y., Yi, F.X.*, Zhou, C.H. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: a case study in Jiangsu, China,Journal of Cleaner Production, 2021, 280, Part 2, 124330, DOI: 10.1016/j.jclepro.2020.124330.
Yang, L., Li, X.M., Yang, Q.Y., Zhang, L., Zhang, S.J., Wu, S.H.*, Zhou, C.H. Extracting knowledge from legacy maps to delineate eco-geographical regions, International Journal of Geographical Information Science, 2020, 35(1): 1-23, DOI: //doi.org/10.1080/13658816.2020.1806284.
Yang, L., Li, X.M., Shi, J.J., Shen, F.X., Gao, B.B.,Feng, Q., Chen, Z.Y., Zhu, A.X., Zhou C.H. Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method. Geoderma, 2020, 369, 114337.
Li, X.M., Li, D., Qin, C. Z., Zhu, A. X., Yang, L*. An Automatic Method for Drainage Basin Spatial Range Delineation Using DEMs. In book: Sustainable Development of Water and Environment, 2020. （EI）
Shen, F.X., Yang, L.*, He, X.L., Zhou, C.H., Adams, J.M. Understanding the spatial–temporal variation of human footprint in Jiangsu Province, China, its anthropogenic and natural drivers and potential implications. Scientific Reports, 2020, 10: 13316, DOI: 10.1038/s41598-020-70088-w.
Gao, H., Zhang, X.Y., Wang, L.J., He X.L., Shen, F.X.,Yang, L.*. Selection of training samples for updating conventional soil map based on spatial neighborhood analysis of environmental covariates. Geoderma, 2020, 366, 114244, //doi.org/10.1016/j.geoderma.2020.114244.
Yang, L., He X.L., Shen, F.X., Zhou C.H., Zhu, A.X., Gao, B.B., Chen, Z.Y.*, Li, M.C.*.Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data.Soil & Tillage Research, 2020, 196, 104465, //doi.org/10.1016/j.still.2019.104465.
Liu, X.Q., Zhu, A.X., Yang, L.*, Pei, T., Liu, J.Z., Zeng, C.Y., Wang, D.S. Agradedproportionmethodoftrainingsampleselectionforupdatingconventionalsoilmaps. Geoderma, 2020, 357, 113939. //doi.org/10.1016/j.geoderma.2019.113939.
Chen, Z.Y., Li, R.Y., Chen, D.L., Zhuang, Y., Gao, B.B., Yang, L.*, Li, M.C.* Understanding the causal influence of major meteorological factors on ground ozone concentrations across China. Journal of Cleaner Production, 2020, 242: 118498. //doi.org/10.1016/j.jclepro.2019.118498.
Yang, L., Song,M., Zhu, A.X., Qin, C.Z., Zhou, C.H., Qi, F., Li, X.M., Chen, Z.Y.*, Gao, B.B. Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables.Geoderma, 2019, 340: 289-302.
Shi. J.J.,Yang, L.*,Zhu, A.X., Qin, C.Z., Liang, P., Zeng, C.Y., Pei, T. Machine-Learning variables atdifferent scales vs. knowledge-based variables formapping multiple soil properties, Soil Science Society of America Journal, 2018,82(3): 645-656.
Yang, L., Brus, D.J. *, Zhu, A.X., Li, X.M., Shi, J.J. Accounting for access costs in validation of soil maps: a comparison of design-based sampling strategies. Geoderma. 2018, 315: 160–169.
An, Y.M., Yang, L. *, Zhu, A.X., Qin, C.Z., Shi, J.J. Identification of representative samples from existing samples for digital soil mapping.Geoderma. 2018, 311: 109-119.
Zeng, C.Y., Yang, L.*, Zhu, A.X.*Construction of membership functions for soil mapping using partial dependence of soil on environmental covariates calculated by random forest. Soil Science Society of America Journal,2017,81(2):341-353.
Yang, L., Zhu, A.X.*, Zhao, Y.G., Li, D.C., Zhang, G.L., Zhang, S.J., Band, L.E. Regional soil mapping using multi-grade representative sampling and a fuzzy membership based mapping approach. Pedosphere, 2017, 27(2): 344-357.
Yang, L., Qi, F.,Zhu, A.X.*,Shi, J.J., An, Y.M. Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping.Soil Science Society of America Journal, 2016, 80(3): 637-651.
Zeng, C.Y., Yang, L.*, Zhu, A.X., Rossiter, D.G., Liu, J., Qin, C.Z., Wang, D.S. Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma, 2016,281: 69-82.
Yang, L., Huang, C.*, Liu, G.H., Liu, J., Zhu, A.X. Mapping soil salinity using a similarity-based prediction approach: a case study in Huanghe River Delta, China. Chinese Geographical Science, 2015, 25(3): 283-294.
Wen, W., Wang, Y.F.*, Yang, L.*, Liang, D.Chen, L.D., Liu, J., Zhu, A.X. Mapping soil organic carbon using auxiliary environmental covariates in a typicalwatershed in the Loess Plateau of China: a comparative study based on three krigingmethods and a soil land inference model (SoLIM).Environmental Earth Sciences, 2015, 73(1):239-251.
Yang, L., Zhu, A.X.*, Qi, F., Qin, C.Z., Li, B.L., Pei, T. An integrative hierarchical stepwise sampling strategy and its application in digital soil mapping. International Journal of Geographical Information Science, 2013, 27(1): 1-23.
Yang, L., Jiao, Y., Fahmy, S., Zhu, A.X.*, Hann, S., Burt, J.E., Qi, F. Updating Conventional Soil Maps through Digital Soil Mapping. Soil Science Society of America Journal, 2011, 75(3): 1044-1053.
Zhu, A.X., Yang, L.*, Li, B.L., Qin, C.Z., Pei, T., Liu, B.Y. Construction of membership functions for predictive soil mapping under fuzzy logic, Geoderma, 2010, 155(3-4): 164-174.
李安琪, 杨琳*，蔡言颜,张磊,黄海莉,吴琪,王雯琪.基于递归特征消除-随机森林模型的江浙沪农田土壤肥力属性制图.地理科学. 2024.
朱阿兴，杨琳*，樊乃卿，曾灿英，张甘霖. 数字土壤制图研究综述与展望.地理科学进展, 2018, 37(1): 66-78.
史静静, 杨琳*, 曾灿英, 朱阿兴, 秦承志, 梁朋. 土壤制图中多目标属性的环境因子及其尺度选择 ――以黑龙江鹤山农场为例.地理研究, 2018, 37 (3): 635-646.
张磊，朱阿兴，杨琳*，秦承志，刘雪琦. 基于分融策略的土壤采样设计方法.土壤学报， 2017, 54(5): 1079-1090.
宋敏，杨琳*，朱阿兴，秦承志. 轮作模式在农耕区土壤有机质推测制图中的应用. 土壤通报, 2017, 48(4): 778-785.
缪亚敏, 朱阿兴, 杨琳*. 滑坡危险度制图精度评价指标的有效性研究.自然灾害学报，2017,26(2): 115-122.
刘雪琦, 朱阿兴, 杨琳*, 缪亚敏, 曾灿英. 土壤图更新中基于土壤类型面积分级的训练样点选择方法.土壤学报,2017,54(1):36-47.
缪亚敏, 朱阿兴, 杨琳*, 白世彪, 曾灿英. 滑坡危险度制图中一种新型的负样本采样方法.地理与地理信息科学,2016,32(4): 61-67+127.
杨琳, 朱阿兴*, 张淑杰, 安艺明. 土壤制图中多等级代表性采样与分层随机采样的对比研究. 土壤学报, 2015, 52(1): 28-37.
杨琳, 朱阿兴, 秦承志, 李宝林, 裴韬. 一种基于样点代表性等级的土壤采样设计方法. 土壤学报, 2011, 48(5): 938-946.
杨琳, Fahmy Sherif, Jiao You, Hann Sheldon, 朱阿兴, 秦承志, 徐志刚. 基于土壤－环境关系知识提取的传统土壤图更新研究. 土壤学报. 2010, 47(6): 11-21.
杨琳, 朱阿兴, 秦承志, 李宝林, 裴韬, 邱维理, 徐志刚. 基于典型点的目的性采样设计方法及其在土壤制图中的应用. 地理科学进展. 2010, 29(3): 279-286.
杨琳, 朱阿兴, 秦承志, 李宝林, 裴 韬, 刘宝元. 运用模糊隶属度进行土壤属性制图的研究——以黑龙江鹤山农场研究区为例. 土壤学报, 2009, 46(1): 9-15.
杨琳, 朱阿兴, 李宝林, 秦承志, 裴 韬, 刘宝元, 李润奎, 蔡强国. 应用模糊c均值聚类获取土壤制图所需土壤－环境关系知识的方法研究. 土壤学报, 2007, 44 (5): 784-791.
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主持的项目
国家自然科学基金面上项目“耦合过程模型与深度学习的土壤时空制图方法”（42471468），2025.01-2028.12
深度融合PIE的土壤地理与3S交叉实践基地建设，教育部产学研合作协同育人项目，2024
第三次全国土壤普查北京市试点四区土壤类型制图工作，2023
关键地球物质循环前沿科学中心“GeoX”交叉项目：遥感与过程模型结合的区域土壤碳储量估算及及其全球变化响应，2023
国家重点研发计划项目“国土空间优化与系统调控理论与方法”-课题“国土空间多要素综合观测与感知关键技术”（2022YFC3800802）-子课题“农业与生态空间形流要素综合观测与智能感知技术”，2022.11-2025.10
国家自然科学基金面上项目“面向多土壤属性制图的多目标优化采样方法”（41971054），2020.01-2023.12；
国家自然科学基金面上项目“数字土壤制图中人类活动影响因子的定量刻画”（41471178），2015.01-2018.12；
国家自然科学基金青年项目“基于样点代表性等级的采样设计方法及其在土壤空间分布推测中的应用”（41001298），2011.01-2013.12；
国家自然科学基金重点项目“中国陆地表层自然地域系统动态及其驱动机制”子课题：基于人工智能和模糊逻辑的陆表地域系统单元界线定量识别，2016.01-2020.12；
国家自然科学基金重点项目“基于地理环境相似性的地理变量空间变化推测理论与方法研究”子课题基于地理变量空间不确定性的补样方法研究，2015.01-2019.12；
国家863主题项目“面向新型硬件架构的复杂地理计算平台”第六课题子课题：应用示范（2011AA120305-3）， 2011.01-2013.12；
水体污染控制与治理科技重大专项课题“南淝河流域农村有机废弃物及农田养分流失污染控制技术研究与示范”（2013ZX07103006-005）子任务：店埠河上游小流域有机废弃物污染综合治理情景分析应用示范，2013.01-2015.12；
资源与环境信息系统国家重点实验室青年人才培养基金项目：基于样点代表性等级的采样方法的参数敏感性分析及其与经典采样方法的对比研究，2011-2012
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Ø 第二十七届全国教师信息素养提升实践活动高等教育组微课“典型作品”奖，2024
Ø “领航杯”江苏省教师信息素养提升实践活动一等奖，2023
Ø 特码 “师德先进”青年教师奖，2023
Ø 第十一届高校GIS论坛“优秀教学成果奖”，1/7，2023
Ø 第四届全国高校GIS教学成果奖特等奖，6/10，2023
Ø 测绘地理信息创新巾帼人物，2023
Ø 高校GIS创新人物奖，2022
Ø 2021年特码 魅力导师奖
Ø 2020年江苏省高校微课教学比赛二等奖
Ø 《土壤学报》2015年度优秀论文奖
Ø 《土壤学报》2010年度优秀论文奖
Ø 中国地理学会2006年学术会青年优秀论文奖
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中国地理学会团体标准工作组成员，2024-2026
欧洲地平线玛丽居里行动（HORIZON-MSCA-DN-2022，D6S），Scientific Advisory Board
第三次全国土壤普查专家技术指导组专家
土壤科学顶级期刊Geoderma的编委
“中国国际影响力优秀学术期刊”《地理科学》编委
Frontier in Soil Science, the Pedometrics section 期刊副主编
国际土壤科学联合会土壤计量学学会（Pedometrics, Division 1 of IUSS），副主席、评奖委员会委员，2014-2018
国际土壤科学联合会土壤计量学分会（Pedometrics, Division 1 of IUSS），顾问委员会委员，2009-2013年
FAO亚洲土壤伙伴计划（Asia Soil Partnership），授课讲师，2012年
2017年国际土壤计量学大会（Pedometrics 2017），学术委员会委员
中国自然资源学会，资源制图专业委员会，副主任委员，2022-2026
中国地理学会，地理建模与地理信息分析专业委员会，委员，2018-2022
中国土壤学会，土壤发生、分类与土壤地理专业委员会，委员，2016-2020
中国地理信息产业协会理论与方法工作委员会，委员
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