Dr. Wei Guo

Raum: IA E6/55
Tel.: +49 (0)234 32-23438
Abteilung: Interdisziplinäre Geoinformationswissenschaften



Wei Guo ist Gastwissenschaftler in der Arbeitsgruppe Interdisziplinäre Geoinformationswissenschaften.

Wei Guo


Research experience

2019.6- China University of Mining & Technology, Beijing, Assistant Professor

2016.5-2019.5 Institute of remote sensing and digital earth, Chinese Academy of Sciences, Post-doctoral

2013.8-2015.2 Michigan state university, Center for Global Change & Earth Observations, Visiting scholar

2009.7-2011.9. Chinese Academy of Surveying & Mapping, Joint training of graduate student


Ph.D, 2015, Wuhan University, Geographic Information System

Master of Engineering, 2011, Shandong University of Science and Technology, Photogrammetry and Remote Sensing

Bachelor of Engineering, 2008, Shandong University of Science and Technology, Geomatics




Guo W, Teng Y, Li J, Yan Y, Zhao C, Li Y., & Li X. (2023) A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China. Science of The Total Environment, 912, 169088

Chen, Y., He, C., Guo, W*., Zheng, S., & Wu, B. (2023). Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 7919-7931

Guo, W., Liu, J., Zhao, X., Hou, W., Zhao, Y., Li, Y., Sun, W., & Fan, D. (2023). Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method. International Journal of Digital Earth, 16, 2704-2723

Ji, X., Sun, Y., Guo, W*., Zhao, C., & Li, K. (2023). Land use and habitat quality change in the Yellow River Basin: A perspective with different CMIP6-based scenarios and multiple scales. Journal of Environmental Management, 345, 118729

Ji, X., Wu, D., Yan, Y., Guo, W*., & Li, K. (2023). Interpreting regional ecological security from perspective of ecological networks: a case study in Ningxia Hui Autonomous Region, China. Environmental Science and Pollution Research, 30, 65412-65426

Li, Y., Guo, W*., Li, P., Zhao, X., & Liu, J. (2023). Exploring the Spatiotemporal Dynamics of CO2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data. Sustainability, 15

Guo, W., Zhang, J., Zhao, X., Li, Y., Liu, J., Sun Wenbin., & Fan, D. (2023). Combining Luojia1-01 nighttime light and Points-of-interest data for fine mapping of population spatialization based on the zonal classification method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1589-1600.

Guo, W., Li, Y., Li, P., Zhao, X., & Zhang, J. (2022). Using a combination of nighttime light and MODIS data to estimate spatiotemporal patterns of CO2 emissions at multiple scales. Science of the Total Environment, 848, 157630

Guo, W., Teng, Y., Yan, Y., Zhao, C., Zhang, W., & Ji, X. (2022).Simulation of land use and carbon storage evolution in Multi-scenario: a case study in Beijing-Tianjin-Hebei urban agglomeration, China. Sustainability, 14 (20)

Yuan, D., Jiang, H., Guo, W., Cui, X., Wu, L., Wu, Z., & Wang, H. (2021). Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints. Sensors (Basel), 21

Guo, W., Zhao, C., Zhang, Y., & Gao, S. (2021). Mapping Impervious Surface Distribution and Dynamics in an Arid/Semiarid Area-A Case Study in Ordos, China. IEEE Access, 9, 19659-19673

Li, G., Li, L., Lu, D., Guo, W., & Kuang, W. (2020). Mapping impervious surface distribution in China using multi-source remotely sensed data. GIScience & Remote Sensing, 57, 543-552

Guo, W., Zhang, Y., & Gao, L. (2018b). Using VIIRS-DNB and landsat data for impervious surface area mapping in an arid/semiarid region. Remote Sensing Letters, 9, 587-596

Guo, W., Li, G., Ni, W., Zhang, Y., & Lu, D. (2018a). Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data. GIScience & Remote Sensing, 55, 699-717

Guo, W., Lu, D., & Kuang, W. (2017). Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data. Remote Sensing, 9

Guo, W., Lu, D., Wu, Y., & Zhang, J. (2015). Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data. Remote Sensing, 7, 12459-12477


  • Datenverarbeitung und Anwendung der nächtlichen Stadtbeleuchtung 
  • Forschung zur nächtlichen Lichtverschmutzung 
  • Bewertung der Ziele für nachhaltige Entwicklung (SDGs)
  • Die ökologischen Auswirkungen der Landveränderung