基本信息
占玉林  男    中国科学院空天信息创新研究院
电子邮件: zhanyl@aircas.ac.cn
通信地址: 北京市海淀区邓庄南路9号
邮政编码:

招生信息

   
招生专业
070503-地图学与地理信息系统
招生方向
生态环境遥感,遥感产品时空一致性

教育背景

2002-09--2005-07   中国科学院大学   博士学位
1999-09--2002-07   南昌大学   硕士学位
1994-09--1998-07   南昌大学   学士学位

工作经历

   
工作简历
2023-04~现在, 中国科学院空天信息创新研究院, 研究员
2010-02~2023-04,中国科学院空天信息创新研究院, 副研究员
2005-08~2010-02,中国科学院空天信息创新研究院, 助理研究员
社会兼职
2020-10-23-2024-10-23,中国环境科学学会生态遥感监测与评估专业委员会, 委员

专利与奖励

   
奖励信息
(1) 多源自主卫星叶面积指数一体化反演技术与多区域应用, 二等奖, 省级, 2017
(2) 城市陆表环境遥感监测信息产品提取技术与应用, 一等奖, 省级, 2015
(3) 生态环境遥感应用处理与分析系统技术研究及应用示范, 一等奖, 省级, 2013
专利成果
[1] 郭红, 顾行发, 程天海, 杨健, 占玉林, 魏香琴, 陈德宝. 大气细粒子AOD反演方法、装置、电子设备及存储介质. CN: CN116698691A, 2023-09-05.
[2] 高敏, 顾行发, 刘艳, 占玉林, 李娟, 魏香琴. 一种受云雪影响数据的重建方法. CN: CN114998468A, 2022-09-02.
[3] 刘艳, 占玉林, 高敏, 李娟, 顾行发, 程天海, 魏香琴. 一种数据重构方法及装置. CN: CN114663325A, 2022-06-24.
[4] 占玉林, 顾行发, 陈昕然, 余涛, 刘艳, 杨健, 魏香琴. 确定区域热环境信息的方法. CN: CN114608706A, 2022-06-10.
[5] 占玉林, 陈昕然, 顾行发, 余涛, 杨健, 李娟. 一种城市集中供暖的热环境响应分析方法及装置. CN: CN113157802A, 2021-07-23.
[6] 占玉林, 陈昕然, 顾行发, 余涛, 杨健, 李娟. 一种城市集中供暖的热环境响应分析方法及装置. CN: CN113157802B, 2022-07-08.
[7] 孙震笙, 李娟, 顾行发, 余涛, 占玉林, 杨健, 刘苗, 王春梅, 魏香琴. 一种基于SAR影像的地物分类方法及装置. CN: CN112883898A, 2021-06-01.
[8] 王树东, 王语懿, 占玉林, 魏亮, 刘凯. 基于多源遥感数据估算常绿林地盖度的方法及系统. CN: CN112215098A, 2021-01-12.
[9] 米晓飞, 杨健, 占玉林, 余涛, 张周威, 柳鹏, 张雅洲, 刘苗, 孙震笙, 曹维佳. 基于多时相阴影差异的高压线塔高度提取方法. CN: CN107883917A, 2018-04-06.
[10] 顾行发, 王忠美, 余涛, 占玉林. 一种基于投影‑相似变换的无人机遥感影像拼接方法. CN: CN106447601A, 2017-02-22.
[11] 占玉林, 孟庆岩, 顾行发, 余涛, 王春梅, 李娟, 魏香琴, 刘苗, 李玲玲. 一种基于NDVI时间序列坐标转换的冬小麦识别方法. CN: CN105404873A, 2016-03-16.
[12] 占玉林, 刘苗, 顾行发, 余涛, 孟庆岩, 张周威, 王春梅. 一种结合树木阴影特征的遥感影像毛白杨识别方法. CN: CN105405148A, 2016-03-16.
[13] 占玉林, 杨闫君, 顾行发, 余涛, 孟庆岩, 牛铮. 一种基于NDVI时间序列曲线积分的冬小麦提取方法. CN: CN104951772A, 2015-09-30.
[14] 孟庆岩, 张佳晖, 占玉林, 王春梅, 吴俊, 金颖. 一种基于伞骨法与冠高比的树木冠层结构信息提取方法. CN: CN104463164A, 2015-03-25.
[15] 占玉林, 孟庆岩, 王春梅, 牛铮, 吴俊, 孙刚. 一种基于移动窗口的城市绿色空间遥感度量方法. CN: CN104463836A, 2015-03-25.
[16] 王春梅, 孟庆岩, 占玉林, 杨健, 吴俊, 刘苗. 一种适用于大范围多尺度卫星遥感数据反演的生态环境参数地面采样方法. CN: CN104462739A, 2015-03-25.

出版信息

   
发表论文
[1] Debao Chen, Xingfa Gu, Hong Guo, Tianhai Cheng, Jian Yang, Yulin Zhan, Qiming Fu. Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model. Science of the Total Environment[J]. 2024, 914(169801): 1-12, https://doi.org/10.1016/j.scitotenv.2023.169801.
[2] 刘奇鑫, 顾行发, 王春梅, 杨健, 占玉林. 不同尺度的土壤含水量主被动微波联合反演方法研究. 地学前缘[J]. 2024, 
[3] Debao Chen, Hong Guo, Xingfa Gu, Jian Yang, Yulin Zhan, Xiangqin Wei. A Spatial Neighborhood Deep Neural Network Model for PM2.5 Estimation Across China. IEEE Transactions on Geoscience and Remote Sensing[J]. 2023, 61(4105815): 1-15, 10.1109/TGRS.2023.3317905.
[4] Chen, Xinran, Gu, Xingfa, Liu, Peizhuo, Wang, Dakang, Mumtaz, Faisal, Shi, Shuaiyi, Liu, Qixin, Zhan, Yulin. Impacts of inter-annual cropland changes on land surface temperature based on multi-temporal thermal infrared images. INFRARED PHYSICS & TECHNOLOGY[J]. 2022, 122: http://dx.doi.org/10.1016/j.infrared.2022.104081.
[5] Liu, Yan, Gu, Xingfa, Cheng, Tianhai, Zhan, Yulin, Zhang, Hu, Li, Juan, Wei, Xiangqin, Gao, Min, Zhang, Qian, Zhang, Yuzhen. Temporal Shape-Based Fusion Method to Generate Continuous Vegetation Index at Fine Spatial Resolution. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING[J]. 2022, 60: 
[6] Chen, Xinran, Gu, Xingfa, Zhan, Yulin, Wang, Dakang, Zhang, Yazhou, Mumtaz, Faisal, Shi, Shuaiyi, Liu, Qixin. The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images. REMOTE SENSING[J]. 2022, 14(10): http://dx.doi.org/10.3390/rs14102327.
[7] Liu, Yuyan, Shi, Fei, Liu, Xuan, Zhao, Zihui, Jin, Yongtao, Zhan, Yulin, Zhu, Xia, Luo, Wei, Zhang, Wenhao, Sun, Yuefang, Li, Xuqing, Wang, Yancang. Influence of Different Meteorological Factors on the Accuracy of Back Propagation Neural Network Simulation of Soil Moisture in China. SUSTAINABILITY[J]. 2022, 14(24): 
[8] Zhang, Junmao, Lin, Tao, Sun, Caige, Lin, Meixia, Zhan, Yulin, Chen, Yuan, Ye, Hong, Yao, Xia, Huang, Yiyi, Zhang, Guoqin, Liu, Yuqin. Long-Term Spatiotemporal Characteristics and Impact Factors of Land Surface Temperature of Inhabited Islands with Different Urbanization Levels. REMOTE SENSING[J]. 2022, 14(19): http://dx.doi.org/10.3390/rs14194997.
[9] Liu, Qixin, Gu, Xingfa, Chen, Xinran, Mumtaz, Faisal, Liu, Yan, Wang, Chunmei, Yu, Tao, Zhang, Yin, Wang, Dakang, Zhan, Yulin. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization. SENSORS[J]. 2022, 22(4): http://dx.doi.org/10.3390/s22041611.
[10] Gao, Min, Gu, Xingfa, Liu, Yan, Zhan, Yulin, Wei, Xiangqin, Yu, Haidong, Liang, Man, Weng, Chenyang, Ding, Yaozong. An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information. SENSORS[J]. 2022, 22(21): http://dx.doi.org/10.3390/s22218524.
[11] Wang, Dakang, Yu, Tao, Liu, Yan, Gu, Xingfa, Mi, Xiaofei, Shi, Shuaiyi, Ma, Meihong, Chen, Xinran, Zhang, Yin, Liu, Qixin, Mumtaz, Faisal, Zhan, Yulin. Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature. REMOTE SENSING[J]. 2021, 13(2): https://doaj.org/article/059f8465570443c9a997a421625911d2.
[12] Mi, Xiaofei, Cao, Weijia, Yang, Jian, Li, Zhenghuan, Zhang, Yazhou, Li, Qianjing, Sun, Zhensheng, Zhan, Yulin. Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique. JOURNAL OF APPLIED REMOTE SENSING[J]. 2020, 14(3): http://dx.doi.org/10.1117/1.JRS.14.032607.
[13] Wang, Dakang, Liu, Yan, Yu, Tao, Zhang, Yin, Liu, Qixin, Chen, Xinran, Zhan, Yulin. A Method of Using WRF-Simulated Surface Temperature to Estimate Daily Evapotranspiration. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY[J]. 2020, 59(5): 901-914, http://dx.doi.org/10.1175/JAMC-D-19-0287.1.
[14] Wang, Chunmei, Xie, Qiuxia, Gu, Xingfa, Yu, Tao, Meng, Qingyan, Zhou, Xiang, Han, Leran, Zhan, Yulin. Soil moisture estimation using Bayesian Maximum Entropy algorithm from FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei province, China. INTERNATIONAL JOURNAL OF REMOTE SENSING[J]. 2020, 41(18): 7018-7041, https://www.webofscience.com/wos/woscc/full-record/WOS:000544782400001.
[15] Wang, Dakang, Zhan, Yulin, Yu, Tao, Liu, Yan, Jin, Xiaomei, Ren, Xinyu, Chen, Xinran, Liu, Qixin. Improving Meteorological Input for Surface Energy Balance System Utilizing Mesoscale Weather Research and Forecasting Model for Estimating Daily Actual Evapotranspiration. WATER[J]. 2020, 12(1): http://dx.doi.org/10.3390/w12010009.
[16] Chen, Xinran, Zhan, Yulin, Liu, Yan, Gu, Xingfa, Yu, Tao, Wang, Dakang, Liu, Qixin, Zhang, Yin, Zhang, Yunzhou. Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices. REMOTE SENSING[J]. 2020, 12(19): https://doaj.org/article/66fa88bae54a4b0794170b70bb4691fc.
[17] Mi, Xiaofei, Yu, Tao, Yang, Jian, Lai, Jibao, Zhang, Zhouwei, Zhang, Yazhou, Zhan, Yulin. Estimating Pylon Height Using Differences in Shadows Between GF-2 Images. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING[J]. 2019, 47(2): 279-288, http://dx.doi.org/10.1007/s12524-018-0928-2.
[18] Li, Lingling, Yu, Tao, Zhao, Limin, Zhan, Yulin, Zheng, Fengjie, Zhang, Yazhou, Mumtaz, Faisal, Wang, Chunmei. Characteristics and trend analysis of the relationship between land surface temperature and nighttime light intensity levels over China. INFRARED PHYSICS & TECHNOLOGY[J]. 2019, 97: 381-390, http://dx.doi.org/10.1016/j.infrared.2019.01.018.
[19] 王春梅, 顾行发, 余涛, 周翔, 占玉林, 韩乐然, 谢秋霞. 被动微波土壤水分产品真实性检验研究进展. 浙江农业学报[J]. 2019, 31(5): 846-854, http://lib.cqvip.com/Qikan/Article/Detail?id=7001928069.
[20] Zhan, Yulin, Muhammad, Shakir, Hao, Pengyu, Niu, Zheng. The effect of EVI time series density on crop classification accuracy. OPTIK[J]. 2018, 157: 1065-1072, http://dx.doi.org/10.1016/j.ijleo.2017.11.157.
[21] Ren, Xinyu, Zhan, Yulin, Yu, Tao, Gu, Xingfa, Zhang, Yazhou, Wang, Dakang, Chen, Xinran. Identifying ginkgo trees using spectrum and texture time series from very high resolution satellite data. JOURNAL OF APPLIED REMOTE SENSING[J]. 2018, 12(2): 
[22] 杨闫君, 田庆久, 占玉林, 陶波, 徐凯健. 空间分辨率与纹理特征对多光谱遥感分类的影响. 地球信息科学学报[J]. 2018, 20(1): 99-107, http://lib.cqvip.com/Qikan/Article/Detail?id=674411350.
[23] Zhang, Yazhou, Zhan, Yulin, Chen, Xinran, Li, LingLing, Yu, Tao. The impact of thermal image spatial enhancement on the estimation of the urban green cooling effect. INFRARED PHYSICS & TECHNOLOGY[J]. 2018, 88: 206-211, http://dx.doi.org/10.1016/j.infrared.2017.11.027.
[24] Hao, Pengyu, Wu, Mingquan, Niu, Zheng, Wang, Li, Zhan, Yulin. Estimation of different data compositions for early-season crop type classification. PEERJ[J]. 2018, 6: https://doaj.org/article/dfbb3aa7b1d748ae8067ce1acd74b79f.
[25] Zhang, Yazhou, Zhan, Yulin, Yu, Tao, Ren, Xinyu. Urban green effects on land surface temperature caused by surface characteristics: A case study of summer Beijing metropolitan region. INFRARED PHYSICS & TECHNOLOGY[J]. 2017, 86: 35-43, http://dx.doi.org/10.1016/j.infrared.2017.08.008.
[26] Hao, Pengyu, Niu, Zheng, Zhan, Yulin, Wu, Yunchao, Wang, Li, Liu, Yonghong. Spatiotemporal changes of urban impervious surface area and land surface temperature in Beijing from 1990 to 2014. GISCIENCE & REMOTE SENSING[J]. 2016, 53(1): 63-84, http://dx.doi.org/10.1080/15481603.2015.1095471.
[27] Hao, Pengyu, Wang, Li, Zhan, Yulin, Niu, Zheng. Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in ***, China. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION[J]. 2016, 5(5): http://www.irgrid.ac.cn/handle/1471x/1182999.
[28] 杨闫君, 占玉林, 田庆久, 顾行发, 余涛. 利用时序数据构建冬小麦识别矢量分析模型. 遥感信息[J]. 2016, 31(5): 53-59, http://lib.cqvip.com/Qikan/Article/Detail?id=670395077.
[29] Yang Yanjun, Zhan Yulin, Tian Qingjiu, Wang Lei, Wang Peiyan, Zhang Wenmin, IEEE. WINTER WHEAT EXTRACTION USING CURVILINEAR INTEGRAL OF GF-1 NDVI TIME SERIES. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). 2016, 3174-3177, 
[30] Hao, Pengyu, Wang, Li, Zhan, Yulin, Niu, Zheng, Wu, Mingquan, IEEE. USING HISTORICAL NDVI TIME SERIES TO CLASSIFY CROPS AT 30M SPATIAL RESOLUTION: A CASE IN SOUTHEAST KANSAS. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). 2016, 6316-6319, 
[31] Muhammad, Shakir, Zhan, Yulin, Wang, Li, Hao, Pengyu, Niu, Zheng. Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas. OPTIK[J]. 2016, 127(3): 1071-1077, http://dx.doi.org/10.1016/j.ijleo.2015.10.107.
[32] Pengyu Hao, Yulin Zhan, Li Wang, Zheng Niu, Muhammad Shakir. Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA. REMOTE SENSING[J]. 2015, 7(5): 5347-5369, https://doaj.org/article/ce67d5d12f584675a376e51781c50687.
[33] Wang, Chunmei, Meng, Qingyan, Zhan, Yulin, Peng, Jing, Wei, Xiangqin, Yang, Jian, Li, Juan. Ground sampling methods for surface soil moisture in heterogeneous pixels. ENVIRONMENTAL EARTH SCIENCES[J]. 2015, 73(10): 6427-6436, http://www.irgrid.ac.cn/handle/1471x/1047334.
[34] 杨闫君, 占玉林, 田庆久, 顾行发, 余涛, 王磊. 基于GF-1/WFVNDVI时间序列数据的作物分类. 农业工程学报[J]. 2015, 155-161, http://lib.cqvip.com/Qikan/Article/Detail?id=78897185504849535052485052.
[35] Wang, Chunmei, Meng, Qingyan, Miao, Zewei, Gu, Xingfa, Yu, Tao, Zhan, Yulin, Liu, Miao, Zheng, Lijuan, Liu, Qiyue. Variability and sensitivity analyses of spring wheat evapotranspiration measurements in Northwest China. ENVIRONMENTAL EARTH SCIENCES[J]. 2015, 74(6): 5443-5452, http://www.irgrid.ac.cn/handle/1471x/1047240.
[36] Muhammad, Shakir, Zhan, Yulin, Niu, Zheng, Wang, Li, Hao, Pengyu. Analyzing the Sensitivity of Crops Classification Accuracy Based on MODIS EVI Time Series and History Ground Reference Data. CANADIAN JOURNAL OF REMOTE SENSING[J]. 2015, 41(6): 536-546, http://www.irgrid.ac.cn/handle/1471x/1047179.

科研活动

   
科研项目
( 1 ) 中空间分辨率光谱地球研发与应用技术研究, 参与, 国家任务, 2020-12--2023-11