Doctor of Philosophy
Research:
My current research mainly focuses on the irrigation in land surface models, participating in the sub-project B05 鈥楾owards a dynamic representation of irrigation in land surface models鈥� in the 鈥楥RC1502 Regional Climate Change: Disentangling the Role of Land Use and Water Management - 2022 to 2025鈥� project ().
Our main task is to develop dynamic irrigation extent of Europe for the period of 1990 to 2020 using process-based model simulation. 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 peer-reviewed publications:
Gong, H., Li, J., Liu, Z., Hou, R., Zhang, Y., Xu, Y., Zhu, W., Yang, L. & Ouyang, Z. 2024. Linkages of soil and microbial stoichiometry to crop nitrogen use efficiency: Evidence from a long-term nitrogen addition experiment. Catena 240, 107961.
DOI:
Wang, J., Sun, Z., Yang, T., Wang, B., Dou, W.& Zhu, W. 2024. Quantifying the effect of salinity on dielectric-based soil moisture measurements using COSMOS records. Journal of Hydrology, 131925.
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Zhu, W., & Siebert, S. 2024. Climate-driven interannual variability in subnational irrigation areas across Europe, Communications & Earth Environment 5:554.
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Zhu, W., Rezaei, EE., Sun, Z., Wang, J. & Siebert, S. 2024. Soil-climate interactions enhance understanding of long-term crop yield stability, European Journal of Agronomy 161 (127386).
DOI:
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:
Peng, J., Wang, D., Zhu, W., Yang, T., Liu, Z., Rezaei, EE., Li, J., Sun, Z. & Xin X. 2023. Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features. International Journal of Applied Earth Observation and Geoinformation 124, 103494.
DOI:
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:
Zhu, W., Rezaei, EE., Nouri, H., Sun, Z., Li, J., Yu, D. & Siebert, S. 2023. UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
DOI:
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:
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:
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鈥�113.
DOI:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
Conference Contributions:
Zhu, W. and Siebert, S.: Towards a dynamic representation of irrigation in land surface models, EGU General Assembly 2023, Vienna, Austria, 24鈥�28 Apr 2023, EGU23-8885.
DOI:
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鈥�28 Apr 2023, EGU23-12111.
DOI:
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:
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鈥�5.
DOI:
Journal article reviewing:
鈥� Agronomy
鈥� Agriculture
鈥� Computers and Electronics in Agriculture
鈥� Environmental Monitoring and Assessment
鈥� European Journal of Agronomy
鈥� European Journal of Remote Sensing
鈥� Ecological Informatics
鈥� Ecological Indicators
鈥� Field Crops Research
鈥� Frontiers in Forests and Global Change
鈥� Geocarto International
鈥� Geo-spatial Information Science
鈥� GIScience & Remote Sensing
鈥� IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
鈥� International Journal of Applied Earth Observation and Geoinformation
鈥� Italian Journal of Agronomy
鈥� Marine Pollution Bulletin
鈥� Nature Reviews Earth & Environment
鈥� Remote Sensing
鈥� Scientific Report
鈥� Guest editor of Special Issue 鈥淚nternet of Things (IoT) and Agricultural Unmanned Aerial Vehicles in Smart Agriculture鈥� in Agriculture.
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