Doktorin der Philosophie

Research:
My current research mainly focuses on the irrigation in land surface models, participating in the sub-project B05 ‘Towards a dynamic representation of irrigation in land surface models’ in the ‘CRC1502 Regional Climate Change: Disentangling the Role of Land Use and Water Management - 2022 to 2025’ project.

Our main task is to develop dynamic irrigation extent of Europe using process-based model simulation and remote sensing approach. We will better quantify differences in water use between dry and wet years by considering differences in the irrigated area and the irrigated crops in land surface models. The understanding of the dynamics in irrigation processes will be improved, and the comprehensively quantification human influences on water and energy flows will be carried out.

Previous work is mainly about the application of remote sensing in agriculture, including crop phenotyping using multi-source UAV remote sensing data; optimization of UAV monitoring schemes for crop growth, such as design optimal UAV sensor combinations and UAV flight mission schemes; diagnosis of environmental stresses of crop growth via the integration of UAV and satellite remote sensing.


Selected publications:
Chen, B., Wang, W., You, Y., Zhu, W., Dong, Y., Xu, Y., Chang, M. & X. Wang, 2023. Influence of rooftop mitigation strategies on the thermal environment in a subtropical city, Urban Climate 49, 101450.
DOI: 10.1016/j.uclim.2023.101450

Li, B., Wu, L., Zhu, W., Zhang, J. & W. He, 2023. Influence of Crop Residue Management and Soil Tillage Method on Reducing the Carbon Footprint of Winter Wheat Production in the Salt-Affected Arable Land in the North China Plain, Agronomy 13, 1018.
DOI: 10.3390/agronomy13041018

Wang, J., Yang, T., Zhu, K., Shao, C., Zhu, W., Hou, G. & Z. Sun, 2023. A novel retrieval model for soil salinity from CYGNSS: Algorithm and test in the Yellow River Delta, Geoderma 432, 116417.
DOI: 10.1016/j.geoderma.2023.116417

Eyshi Rezaei, E., Vargas Rojas, L., Zhu, W. & D., Cammarano, 2022. The potential of crop models in simulation of barley quality traits under changing climates: A review, Field Crops Research 286, 108624.
DOI: 10.1016/j.fcr.2022.108624

Peng, J., Eyshi Rezaei, E., Zhu, W., Wang, D., Li, H., Yang, B. & Z., Sun, 2022. Plant Density Estimation Using UAV Imagery and Deep Learning, Remote Sensing 14, 5923.
DOI: 10.3390/rs14235923

Yang, B., Zhu, W., Eyshi Rezaei, E., Li, J., Sun, Z. & J., Zhang, 2022. The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing, Remote Sensing 14, 1559.
DOI: 10.3390/rs14071559

Yu, D., Zha, Y., Sun, Z., Li, J., Jin, X., Zhu, W., Bian, J., Ma, L., Zeng, Y. & Z., Su, 2022. Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: A comparison with traditional machine learning algorithms, Precision Agriculture 24, 92–113.
DOI: 10.1007/s11119-022-09932-0

Zhu, W., Eyshi Rezaei, E., Nouri, H., Sun, Z., Li, J., Yu, D. & S. Siebert, 2022. UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases, Field Crops Research 284, 108582.
DOI: 10.1016/j.fcr.2022.108582

Chen, B., Wang, W., Dai, W., Chang, M., Wang, X., You, Y., Zhu, W. & C., Liao, 2021. Refined urban canopy parameters and their impacts on simulation of urbanization-induced climate change. Urban Climate 100847.
DOI: 10.1016/j.uclim.2021.100847

Zhu, W., Eyshi Rezaei, E., Nouri, H., Yang, T., Li, B., Gong, H., Lyu, Y., Peng, J. & Z. Sun, 2021. Quick detection of field-scale soil comprehensive attributes via the integration of UAV and Sentinel-2B remote sensing data, Remote Sensing 13, 4716.
DOI: 10.3390/rs13224716

Zhu, K., Sun, Z., Zhao, F., Yang, T., Tian, Z., Lai, J., Zhu, W. & B., Long, 2021. Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress. Remote Sensing 13, 250.
DOI: 10.3390/rs13020250

Zhu, W., Sun, Z., Huang, Y., Yang, T., Li, J., Zhu, K., Zhang, J., Yang, B., Shao, C., Peng, J., Li, S., Hu, H. & X. Liao, 2021. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping, Precision Agriculture 22, 1768-1802.
DOI: 10.1007/s11119-021-09811-0

Zhu, W., Sun, Z., Li, B., Yang, T., Liu, Z., Peng, J., Zhu, K., Li, S., Lou, J., Hou, R., Li, J., Yu, W., Wang, Y., Zhang, F., Liu, X. & Z. Ouyang, 2021. Analysis of Spatial Heterogeneity for Soil Attributes and Spectral Indices-based Diagnosis of Coastal Saline-Alkaline Farmland Stress Using UAV Remote Sensing, Journal of Geo-information Science 23, 536-549. (In Chinese with English abstract).
DOI: 10.12082/dqxxkx.2021.200144

Zhu, W., Sun, Z., Yang, T., Li, J., Peng, J., Zhu, K., Li, S., Gong, H., Lyu, Y., Li, B. & X. Liao, 2020. Estimating leaf chlorophyll content of crops based on a comprehensive framework for UAV remote sensing monitoring, Computers and Electronics in Agriculture 178, 105786.
DOI: 10.1016/j.compag.2020.105786

Zhu, W., Sun, Z., Huang, Y., Lai, J., Li, J., Zhang, J., Yang, B., Li, B., Li, S., Zhu, K., Li, Y. & X. Liao, 2019. Improving field-scale crop LAI retrievals based on UAV remote sensing observations and optimized VI-LUT, Remote Sensing 11, 2456.
DOI: 10.3390/rs11202456

Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B. & X. Liao, 2019. Estimating Maize Above-ground Biomass using 3D point clouds of multi-source UAV data at multi-spatial scales, Remote Sensing 11, 2678.
DOI: 10.3390/rs11222678

Zhu, W., Li, S., Zhang, X., Li, Y. & Z. Sun, 2018. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing, Transactions of the Chinese Society of Agricultural Engineering 34, 78-86. (In Chinese with English abstract).
DOI: 10.11975/j.issn.1002-6819.2018.11.010


Conference Contributions:
Zhu, W. and Siebert, S.: Towards a dynamic representation of irrigation in land surface models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8885.
DOI: 10.5194/egusphere-egu23-8885

Sun, Z., Zhu, W., Eyshi Rezaei, E., Peng, J., Yu, D., and Siebert, S.: Multi-source UAV remote sensing and AI for crop growth monitoring, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12111.
DOI: 10.5194/egusphere-egu23-12111

Li, S., Sun, Z., Zhang, X., Zhu, W & Y. Li.: An improved threshold method to detect the phenology of winter wheat. 7th International Conference on Agro-Geoinformatics. IEEE, Hangzhou, China, 2018.
DOI: 10.1109/Agro-Geoinformatics.2018.8476090

Zhu, W., Huang, Y. & Z. Sun. Mapping Crop Leaf Area Index from Multi-Spectral Imagery Onboard an Unmanned Aerial Vehicle. 2018 7th International Conference on Agro-Geoinformatics. IEEE, Hangzhou, China, 2018, pp. 1–5.
DOI: 10.1109/Agro-Geoinformatics.2018.8475985


Journal article reviewing:
• Agronomy
• European Journal of Remote Sensing
• IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• International Journal of Applied Earth Observation and Geoinformation
• Italian Journal of Agronomy
• Marine Pollution Bulletin
• Nature Reviews Earth & Environment
• Remote Sensing
• Scientific Report


Additional information:
Research Gate: https://www.researchgate.net/profile/Wanxue_Zhu
Google scholar: https://scholar.google.com/citations?user=eXGPKx8AAAAJ&hl=en