生态环境遥感，时间序列遥感

（1）学术论文

[1]Liu, Q.; Gu, X.; Chen, X.; Mumtaz, F.; Liu, Y.; Wang, C.; Yu, T.; Zhang, Y.; Wang, D.; Zhan, Y*. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization. Sensors 2022, 22, 1611.

[2]Zhulin Chen, Kun Jia, Xiangqin Wei, Yan Liu, Yulin Zhan, Mu Xia, Yunjun Yao, Xiaotong Zhang. Improving leaf area index estimation accuracy of wheat by involving leaf chlorophyll content information. Computers and Electronics in Agriculture, 2022, 196, 106902.

[3][3]Chen, X.; Gu, X.; Zhan, Y.; Wang, D.; Zhang, Y.; Mumtaz, F.; Shi, S.; Liu, Q. The Impact of Central Heating on the Urban Thermal Environment Based on MultiTemporal Remote Sensing Images. Remote Sens. 2022, 14, 2327.

[4]Xinran Chen, Xingfa Gu, Peizhuo Liu, Dakang Wang, Faisal Mumtaz, Shuaiyi Shi, Qixin Liu, Yulin Zhan. Impacts of inter-annual cropland changes on land surface temperature based on multi-temporal thermal infrared images. Infrared Physics & Technology, 2022, 122, 104081.

[5]Wang, D.; Yu, T.; Liu, Y.; Gu, X.; Mi, X.; Shi, S.; Ma, M.; Chen, X.; Zhang, Y.; Liu, Q.; Mumtaz, F.; Zhan, Y*. Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature. Remote Sens. 2021, 13, 225. 

[6]Chen, X.; Zhan, Y.*; Liu, Y.; Gu, X.; Yu, T.; Wang, D.; Liu, Q.; Zhang, Y.; Zhang, Y. Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices. Remote Sens. 2020, 12, 3202.

[7]Chunmei Wang, Qiuxia Xie, Xingfa Gu, Tao Yu, Qingyan Meng, Xiang Zhou, Leran Han & Yulin Zhan (2020) 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, 41:18, 7018-7041.

[8]Wang, D., Y. Liu, T. Yu, Y. Zhang, Q. Liu, X. Chen, and Y. Zhan*, 2020: A Method of Using WRF-Simulated Surface Temperature to Estimate Daily Evapotranspiration. J. Appl. Meteor. Climatol., 59, 901–914.

[9]Zhang, J.; Lin, T.; Sun, C.; Lin, M.; Zhan, Y.; Chen, Y.; Ye, H.; Yao, X.; Huang, Y.; Zhang, G.; et al. Long-Term Spatiotemporal Characteristics and Impact Factors of Land Surface Temperature of Inhabited Islands with Different Urbanization Levels. Remote Sens. 2022, 14, 4997.

[10]Liu, Y.; Gu, X.;Cheng, T.; Zhan, Y; Zhang, H. et al. Temporal Shape-Based Fusion Method to Generate Continuous Vegetation Index at Fine Spatial Resolution. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 4414514

[11]Xiaofei Mi, Weijia Cao, Jian Yang, Zhenghuan Li, Yazhou Zhang, Qianjing Li, Zhengsheng Sun, Yulin Zhan (2020).Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique.Journal of Applied Remote Sensing,14(03), 032607.

[12]Wang, D.; Zhan, Y.; Yu, T.; Liu, Y.; Jin, X.; Ren, X.; Chen, X.; Liu, Q. Improving Meteorological Input for Surface Energy Balance System Utilizing Mesoscale Weather Research and Forecasting Model for Estimating Daily Actual Evapotranspiration. Water 2020, 12, 9.

[13]Qin, Yuchu and Wu, Yunchao and Li, Bin and Gao, Shuai and Liu, Miao and Zhan, Yulin. Semantic Segmentation of Building Roof in Dense Urban Environment with Deep Convolutional Neural Network: A Case Study Using GF2 VHR Imagery in China. Sensors 2019, 19(5), 1164; doi: 10.3390/s19051164

[14]Xiaofei Mi, Tao Yu, Jian Yang, Jibao Lai, Zhouwei Zhang, Yazhou Zhang, Yulin Zhan (2019). Estimating Pylon Height Using Differences in Shadows Between GF-2 Images. Journal of the Indian Society of Remote Sensing, (2019). https://doi.org/10.1007/s12524-018-0928-2

[15]Lingling Li, Tao Yu, Limin Zhao, Yulin Zhan, Fengjie Zheng, Yazhou Zhang, Faisal Mumtaz, Chunmei Wang (2019). Characteristics and trend analysis of the relationship between land surface temperature and nighttime light intensity levels over China. Infrared Physics & Technology, 97: 381-390.

[16]Pengyu Hao, Mingquan Wu, Zheng Niu, Li Wang and Yulin Zhan, Estimation of different data compositions for early-season crop type classification. PeerJ 6:e4834; DOI 10.7717/peerj.4834

[17]Xinyu Ren, Yulin Zhan, Tao Yu, Xingfa Gu, Yazhou Zhang, Dakang Wang, Xinran Chen, "Identifying ginkgo trees using spectrum and texture time series from very high resolution satellite data," J. Appl. Remote Sens. 12(2), 026020 (2018), doi: 10.1117/1.JRS.12.026020.

[18]Zhan Y, Shakir M, Hao P, Niu Z, The effect of EVI time series density on crop classification accuracy, Optik - International Journal for Light and Electron Optics (2018)

[19]Zhang, Y., Zhan, Y., Yu, T., & Chen, X. (2018). The impact of thermal image spatial enhancement on the estimation of the urban green cooling effect. Infrared Physics & Technology, 88: 206-211

[20]Zhang, Y., Zhan, Y., Yu, T., & Ren, X. (2017). Urban green effects on land surface temperature caused by surface characteristics: A case study of summer Beijing metropolitan region. Infrared Physics & Technology, 86: 35-43

[21]Wei, X. Q., Gu, X. F., Meng, Q. Y., Yu, T., Jia, K., Zhan, Y. L., & Wang, C. M. (2017). Cross-Comparative Analysis of GF-1 Wide Field View and Landsat-7 Enhanced Thematic Mapper Plus Data. Journal of Applied Spectroscopy, 84(5):829-836.

[22]Hao, P. Y., Wang, L., Zhan, Y. L., Wang, Y. L., Niu, Z., & Wu, M. Q. (2016). Crop classification using crop knowledge of previous-year: Case study in Southwest Kansas, USA. European Journal of Remote Sensing. 

[23]Pengyu Hao, Li Wang, Yulin Zhan and Zheng Niu. 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 Xinjiang, China. ISPRS International Journal of Geo-Information. 2016, 67(5).

[24]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. 2015, 41(6): 536-546

[25]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 - International Journal for Light and Electron Optics, 2016, 127(3): 1071-1077.

[26]Pengyu Hao, Zheng Niu, Yulin Zhan*, Yunchao Wu, Li Wang & Yonghong Liu. Spatiotemporal changes of urban impervious surface area and land surface temperature in Beijing from 1990 to 2014, GIScience & Remote Sensing, 2016, 53(1):63-84.

[27]Pengyu Hao, Yulin Zhan*, Li Wang, Zheng Niu and Muhammad Shakir. Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA, Remote Sensing, 2015(7) 5347-5369.

[28]Ni Huang, Zheng Niu, Yulin Zhan* et al. Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems. Agricultural and Forest Meteorology, 2012 (160).

[29]Juan Li, Xing-Fa Gu, Tao Yu, Yu-Lin Zhan, Zhi Liu, Xing Lv, Ling-Ling Li , Chun-Mei Wang. Binary Darboux transformation for a variable-coefficient nonisospectral modified Kadomtsev–Petviashvili equation with symbolic computation, Nonlinear Dynamics, 2016, 83(3):1463-1468.

[30]Chunmei Wang;Qingyan Meng, Zewei Miao, Xingfa Gu, Tao Yu, Yulin Zhan, Miao Liu, Lijuan Zheng, Qiyue Liu. Variability and sensitivity analyses of spring wheat evapotranspiration measurements in Northwest China, Environmental Earth Sciences, 2015, 74(6): 5443-5452.

Chunmei Wang, Qingyan Meng, Yulin Zhan et al. Ground Sampling Methods for Surface Soil Moisture in Heterogeneous Pixels, Environmental Earth Sciences, 2015, 73(10): 6427-6436.

[31]Hao P, Wang L, Zhan Y, Niu Z, Wu M. Using historical NDVI time series to classify crops at 30m spatial resolution: A case in Southeast Kansas. InGeoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International 2016 Jul 10 (pp. 6316-6319). IEEE.

[32]Yang Yanjun, Zhan Yulin*, Tian Qingjiu, Wang Lei1, Wang Peiyan1, Zhang Wenmin. WINTER WHEAT EXTRACTION USING CURVILINEAR INTEGRAL OF GF-1 NDVI TIME SERIES, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 3174-3177.doi: 10.1109/IGARSS.2016.7729821

[33]王春梅，占玉林*，魏香琴等. 非均质中低分辨率像元土壤含水量地面采样方法研究进展[J]. 中南大学学报（自然科学版），2016，47(4)：1414-1419. 

[34]杨闫君，占玉林*，田庆久，顾行发，余涛，王磊. 基于GF-1/WFV NDVI 时间序列数据的作物分类[J]. 农业工程学报，2015，31(24)：155-161.

[35]Yulin Zhan, Qingyan Meng, Chunmei Wang et al. Fractional vegetation cover estimation over large regions using GF-1 satellite data, Proc. SPIE 9260, Land Surface Remote Sensing II, 92604B (November 8, 2014) doi:10.1117/12.2069845

（2）专利

[1]一种城市集中供暖的热环境响应分析方法及装置, 发明专利, 2021, 专利号: CN113157802A

[2]一种基于SAR影像的地物分类方法及装置, 发明专利, 2021, 专利号: CN112883898A

[3]基于多源遥感数据估算常绿林地盖度的方法及系统, 发明专利, 2021, 专利号: CN112215098A

[4]基于多时相阴影差异的高压线塔高度提取方法, 专利授权, 2018, 专利号: CN107883917A

[5]一种基于投影？相似变换的无人机遥感影像拼接方法, 专利授权, 2017, 专利号: CN106447601A

[6]一种基于NDVI时间序列坐标转换的冬小麦识别方法, 发明专利, 2016, 专利号: CN105404873A

[7]一种结合树木阴影特征的遥感影像毛白杨识别方法, 发明专利, 2016, 专利号: CN105405148A

[8]一种基于NDVI时间序列曲线积分的冬小麦提取方法, 发明专利, 2015, 专利号: CN104951772A

[9]一种基于伞骨法与冠高比的树木冠层结构信息提取方法, 发明专利, 2015, 专利号: CN104463164A

[10]一种基于移动窗口的城市绿色空间遥感度量方法, 发明专利, 2015, 专利号: CN104463836A

[11]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, 发明专利, 2015, 专利号: CN104462739A

（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，中国地理信息科技进步奖二等奖，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，中国地理信息科技进步奖一等奖，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，中国地理信息科技进步奖一等奖，2013