Introduction

CAI, Zhanzhang

Biography

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Time-series analysis, ecosystem modelling, Quantitative Environmental Remote Sensing, Carbon uptake estimation

EMAIL

caizhanzhang@cuhk.edu.cn

EDUCATION BACKGROUND

PhD in Geobiosphere Science, Lund University, Sweden

Bachelor of Science, Nanjing University, China

BIOGRAPHY

He is a Research Scientist at the Artificial Intelligence Large Model Center, Shenzhen Research Institute of Big Data, and a Researcher at the Department of Physical Geography and Ecosystem Science at Lund University, Sweden. His research focuses on ecosystem modelling, quantitative environmental remote sensing, big data analytics, and ecosystem carbon estimation, with a particular expertise in vegetation phenology and water quality monitoring. He has led the development of several remote sensing algorithms based on time series analysis and has contributed to major scientific projects supported by the European Space Agency and the European Environment Agency. His work has been widely applied to climate change monitoring, land degradation assessment, and the analysis of ecosystem functions.

ACADEMIC PUBLICATIONS

  1. Ye, N., Morgenroth, J., Xu, C., & Cai, Z. (2022). Improving neural network classification of indigenous forest in New Zealand with phenological features. Journal of Environmental Management, 314, 115134.
  2. Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., Smets, B., Hoolst, R., Bonte, K., Ivits, E., Tong, X., Ardö, J., & Eklundh, L. (2021). Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment.
  3. Cai, Z., Junttila, S., Holst, J., Jin, H., Ardö, J., Ibrom, A., Peichl, M., Mölder, M., Jönsson, P., Rinne, J., & Karamihalaki, M. (2021). Modelling daily gross primary productivity with Sentinel-2 data in the Nordic region – comparison with data from MODIS. Remote Sensing, 13(3), 469.
  4. Jönsson, P., Cai, Z., Melaas, E., Friedl, M., & Eklundh, L. (2018). A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sensing, 10, 635.
  5. Cai, Z., Jönsson, P., Jin, H., & Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sensing, 9, 1271.