Xiuliang Jin

Xiuliang Jin

Head of Crop Phenomics

Center for Center of Crop Management and Farming System

Institute of Crop Sciences

No.12 Zhongguancun South Street

Beijing, 100081, China

Tel:86-10-82105097

Fax:86-10-82105097

Email:jinxiuliang@caas.cn

Educations

l  2012-2015, PhD in Agricultural Information, Beijing Research Center for Information Technology in Agriculture, Beijing, China

l  2013-2014, Exchanged PhD, Remote Sensing and Geographic Information System, University of Lethbridge, Lethbridge, Canada

l  2011-2015, PhD in Agricultural Information, Yangzhou University, Yangzhou, China

l  2008-2011, MS in Agricultural Information, Shihezi University, Xinjiang, China

l  2004-2008, BS in Forestry, College of Agriculture, Shihezi University, Xinjiang, China

Working Experience

l  2019.03-now, Professor, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences

l  2015.12-2019.02, Postdoc, Institute of Environnement Mditerranen et Modlisation des Agro-Hydrosystmes, French National Institute for Agricultural Research

l  2015.7-2015.11, Assistant Professor, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences

Research Interests

l  Crop Phenomics

l  Crop Phenotyping Platform Study and Application

l  Application and Development of Spectral Sensors

l  Image Processing and Analyzing

Academic Qualifications

l  Associate Editor, European Journal of Agronomy

l  Associate Editor, The Crop Journal

l  Associate Editor, Precision Agriculture

l  Associate Editor, The Journal of Agricultural Science

l  Associate Editor, Agronomy Journal

l  Board of Advisors, New Phytologist

l  Technical Committee on Agricultural Information Standards, Ministry of Agriculture and Rural Affairs

l  First Committee on Agricultural Modeling and Simulation, China Simulation Federation

l  First Committee on Crop Phenomics, Genetics Society of China

l  First Executive Committee on Digital Agriculture, China Computer Federation

l  First Committee Smart Agriculture, Crop Science Society of China

l  Standing Committee on the Youth Working, Chinese Society for Agricultural Machinery

Selected Publications (*corresponding authors)

1.     Jin, X.*, Liu, S., Baret, F., Hemerlé, M. and Comar, A., 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105-114. 

2.     Jin, X.*, Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G. and Wang, J., 2018. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141-152. 

3.     Jin, X.*, Zarco-Tejada, P.J., Schmidhalter, U., Reynolds, M.P., Hawkesford, M.J., Varshney, R.K., Yang, T., Nie, C., Li, Z., Ming, B. and Xiao, Y., 2021. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine, 9(1), 200-231. 

4.     Jin, X.*, Li, Z., Feng, H., Ren, Z. and Li, S., 2020. Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index. The Crop Journal, 8(1), 87-97. 

5.     Cheng, M., Jiao, X.*, Liu, Y., Shao, M., Yu, X., Bai, Y., Wang, Z., Wang, S., Tuohuti, N., Liu, S. and Shi, L., 2022. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agricultural Water Management, 264, 107530. 

6.     Liu, S., Jin, X.*, Nie, C., Wang, S., Yu, X., Cheng, M., Shao, M., Wang, Z., Tuohuti, N., Bai, Y. and Liu, Y., 2021. Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms. Plant Physiology, 187(3), 1551-1576. 

7.     Jin, X.*, Li, Z., Feng, H., Ren, Z. and Li, S., 2020. Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. Agricultural Water Management, 227, 105846. 

8.     Jin, X.*, Li, Z., Yang, G., Yang, H., Feng, H., Xu, X., Wang, J., Li, X. and Luo, J., 2017. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 24-37.

9.     Bai, Y. and Jin, X.*, 2024. Hyperspectral approaches for rapid and spatial plant disease monitoring. Trends in plant science, 29(6), 711-712.

10.   Cheng, M., Jiao, X., Li, B., Yu, X., Shao, M. and Jin, X.*, 2021. Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation. Earth System Science Data, 13(8), 3995-4017.