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.
