教育背景

2008.09-2012.06，中国地质大学（北京），地理信息系统，学士

2012.09-2015.06，中国地质大学（北京），测绘工程，硕士

2015.09-2018.06，中国地质大学（北京），测绘科学与技术，博士

工作经历

2018.07-2020.09，中国科学院空天信息创新研究院，博士后

2021.01-2023.12，北京师范大学，国土空间规划与不动产研究中心，特聘副研究员

2024.01-2024.7，北京师范大学，文理学院地理系，特聘副研究员

2024.7 至今 ，北京师范大学，文理学院地理系，副教授

研究方向

全球变化遥感、遥感时间序列分析方法与生态应用

科研项目

1.国家自然科学基金青年科学基金，北方林草交错带植被返青期遥感观测的尺度差异及其形成机制分析，2023-2025，主持；

2.中国博士后科学基金面上项目，生态交错区荒漠化过程特征及其景观结构依赖性遥感分析，2019-2020，主持；

3.国家自然科学基金面上项目，草地贪夜蛾适生区遥感提取与发生预测研究，2021-2024，参加；

4.中国科学院战略性先导科技专项（A类）子课题，全球和重点区域中高分辨率典型信息产品，2018-2022，参加；

5.国家重点研发计划课题，遥感立体协同观测与地表要素高精度反演，2016-2021，已结题，参加；

6.国家自然科学基金青年科学基金，基于谱-时特征分析的农作物重金属胁迫遥感识别方法研究，2014-2017，已结题，参加。

发表论文

1.Ding, C., 2024. A satellite-derived dataset on vegetation phenology across Central Asia from 2001 to 2023.  Data in Brief , 54, 110297. https://doi.org/10.1016/j.dib.2024.110297

2.Ding, C., Meng, Y., Huang, W., Xie, Q., 2023. Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests.  Science of the Total Environment , 899, 165650. https://doi.org/10.1016/j.scitotenv.2023.165650

3.Ding, C., Li, Y., Xie, Q.Y., Li, H., Zhang, B.W., 2023. Impacts of terrain on land surface phenology derived from Harmonized Landsat 8 and Sentinel-2 in the Tianshan Mountains, China.  GIScience & Remote Sensing , 60, 2242621. https://doi.org/10.1080/15481603.2023.2242621

4.Ding, C., Huang, W., Zhao, S., Zhang, B., Li, Y., Huang, F., Meng, Y., 2022. Greenup dates change across a temperate forest-grassland ecotone in northeastern China driven by spring temperature and tree cover.  Agricultural and Forest Meteorology , 314, 108780. https://doi.org/10.1016/j.agrformet.2021.108780

5.Ding, C., Huang, W., Liu, M., Zhao, S., 2022. Change in the elevational pattern of vegetation greenup date across the Tianshan Mountains in Central Asia during 2001–2020.  Ecological Indicators , 136, 108684. https://doi.org/10.1016/j.ecolind.2022.108684

6.Ding, C., Huang, W., Meng, Y., Zhang, B., 2022. Satellite-Observed Spatio-Temporal Variation in Spring Leaf Phenology of Subtropical Forests across the Nanling Mountains in Southern China over 1999–2019.  Forests , 13, 1486. https://doi.org/10.3390/f13091486

7.Ding, C., Huang, W., Li, Y., Zhao, S., Huang, F., 2020. Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series.  Sensors , 20, 3839. https://doi.org/10.3390/s20143839

8.Ding, C., Liu, X.N., Huang, F., Li, Y., Zou, X.Y., 2017. Onset of drying and dormancy in relation to water dynamics of semi-arid grasslands from MODIS NDWI.  Agricultural and Forest Meteorology , 234–235, 22–30. https://doi.org/10.1016/j.agrformet.2016.12.006

9.Ding, C., Liu, X., Huang, F., 2017. Temporal interpolation of satellite-derived leaf area index time series by introducing spatial-temporal constraints for heterogeneous grasslands.  Remote Sensing , 9, 968. https://doi.org/10.3390/rs9090968

10.Ding, C., Li, X., Liu, X., Zhao, L., 2015. Quartzose-mafic spectral feature space model: A methodology for extracting felsic rocks with ASTER thermal infrared radiance data.  Ore Geology Reviews , 66, 283–292. https://doi.org/10.1016/j.oregeorev.2014.11.006

11.Ding, C., Liu, X., Liu, W., Liu, M., Li, Y., 2014. Mafic-ultramafic and quartz-rich rock indices deduced from ASTER thermal infrared data using a linear approximation to the Planck function.  Ore Geology Reviews, 60, 161–173. https://doi.org/10.1016/j.oregeorev.2014.01.005

12.Liu, M., Wang, Y., Liu, R., Ding, C., Zhou, G., Han, L., 2023. How magnitude of PM 2.5 exposure disparities have evolved across Chinese urban-rural population during 2010 – 2019.  Journal of Cleaner Production , 382, 135333. https://doi.org/10.1016/j.jclepro.2022.135333

13.Meng, Y., Hou, B., Ding, C., Huang, L., Guo, Y., 2023. Spatiotemporal patterns of planted forests on the Loess Plateau between 1986 and 2021 based on Landsat NDVI time-series analysis.  GIScience & Remote Sensing , 60, 2185980. https://doi.org/10.1080/15481603.2023.2185980

14.Meng, Y., Liu, X., Wang, Z., Ding, C., Zhu, L., 2021. How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?  Ecological Indicators, 132, 108336. https://doi.org/10.1016/j.ecolind.2021.108336

15.Zhao, S., Liu, X.N., Ding, C., Liu, S.Y., Wu, C.S., Wu, L., 2020. Mapping Rice Paddies in Complex Landscapes with Convolutional Neural Networks and Phenological Metrics.  GIScience & Remote Sensing , 57, 37–48. https://doi.org/10.1080/15481603.2019.1658960

16.Meng, Y., Liu, X., Ding, C., Xu, B., Zhou, G., Zhu, L., 2020. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecological Informatics , 57, 101064. https://doi.org/10.1016/j.ecoinf.2020.101064

17.Liu, M., Liu, X., Zhang, B., Ding, C., 2016. Regional heavy metal pollution in crops by integrating physiological function variability with spatio-temporal stability using multi-temporal thermal remote sensing.  International Journal of Applied Earth Observation and Geoinformation , 51, 91–102. https://doi.org/10.1016/j.jag.2016.05.003

18.Liu, F., Liu, X., Ding, C., Wu, L., 2015. The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model.  Field Crops Research , 183, 225–234. https://doi.org/10.1016/j.fcr.2015.08.004

19.Liu, F., Liu, X., Zhao, L., Ding, C., Jiang, J., Wu, L., 2015. The Dynamic Assessment Model for Monitoring Cadmium Stress Levels in Rice Based on the Assimilation of Remote Sensing and the WOFOST Model.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 8, 1330–1338. https://doi.org/10.1109/JSTARS.2014.2371058

20.Liu, M., Liu, X., Liu, D., Ding, C., Jiang, J., 2015. Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm.  Computers and Geosciences , 75, 44–56. https://doi.org/10.1016/j.cageo.2014.10.016

21.Liu, M., Liu, X., Li, J., Ding, C., Jiang, J., 2014. Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm.  International Journal of Applied Earth Observation and Geoinformation , 33, 192–202. https://doi.org/10.1016/j.jag.2014.05.009

教学工作

本科生：地理信息系统原理

研究生：地理信息科学前沿技术、地理信息空间分析方法