Remote sensing-based agriculture monitoring, specifically big Earth data for cropland and cropping intensity mapping, crop classification using multi-source remote sensing imagery and crop area estimates, and remote sensing-based agricultural monitoring and food security assessment. 

§   2009-2014, Ph.D. Cartography and Geographical Information System, University of Chinese Academy of Sciences

§   2005-2009, Ph.D. Geographical Information System, Peking University

§   2020-now, Associate Professor, Aerospace Information Research Institute of Chinese Academy of Sciences

§   2017-2019, Associate Professor, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences

§   2014-2017, Assistant Professor, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences 

§   Google Scholar H-index 31, totally cited by 3511 papers (Google Scholar link: https://scholar.google.com/citations?user=ZPXkGx8AAAAJ&hl=en).

§   I got more than 100 publications recent ten years. My research outputs span across various disciplines, including remote sensing, agronomy and food security & disaster assessment.

§   Some key publications are listed below:

[1]       Zhao, H., Wu, B., Zhang, M.*, Long, J., Tian, F., Xie, Y., ... & Li, J. (2025). A large-scale VHR parcel dataset and a novel hierarchical semantic boundary-guided network for agricultural parcel delineation. ISPRS Journal of Photogrammetry and Remote Sensing, 221, 1-19.

[2]       Zhang, M.; Bingfang Wu, et al. (2022). GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery, Earth System Science Data, 13(10), 4799-4817.

[3]       Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., ... & Loupian, E. (2023). Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4), nwac290.

[4]       Li, Y., Zeng, H., Zhang, M., Wu, B., Zhao, Y., Yao, X., ... & Wu, F. (2023). A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering. International Journal of Applied Earth Observation and Geoinformation, 118, 103269.

[5]       Wu, B., Tian, F., Zhang, M., Piao, S., Zeng, H., Zhu, W., ... & Lu, Y. (2022). Quantifying global agricultural water appropriation with data derived from earth observations. Journal of Cleaner Production, 358, 131891.

[6]       Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., & Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sensing, 12(6), 1024.

[7]       Bofana, J.; Zhang, M.*; Nabil, M.; et al. How long did crops survive from floods caused by Cyclone Idai in Mozambique detected with multi-satellite data. Remote Sensing of Environment, 2022, 269:112808.

[8]       Liu, Chong, Qi Zhang, Shiqi Tao, …, Zhang, Miao*, et al. A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication. Remote Sensing of Environment, 2020, 251:112095.

[9]       Mohsen Nabil, Miao Zhang*, José Bofana, et al. Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: A case study in Africa. International Journal of Applied Earth Observation and Geoinformation 2020: 102010.

[10]   Bofana, J.; Zhang, M.*; Nabil, M.; et al. Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin. Remote Sens. 2020, 12, 2096.

[11]   Xin Zhang, Miao Zhang, Yang Zheng et al. 2016. Crop Mapping Using Proba-V Time Series Data at the Yucheng and Hongxing Farm in China. Remote Sensing, 8(11), 915.

[12]   Yang Zheng, Miao Zhang, Xin Zhang, et al. 2016. Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products, Remote Sensing 8(10), 824.

[13]   Zhang Miao, Wu Bingfang, Meng Jihua, 2014. Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data. International Journal of Applied Earth Observation and Geoinformation, 2014，32: 105-113.

[14]   Zhang Miao, Wu Bingfang, Yu Mingzhao, et al., 2014. Crop condition assessment with adjusted NDVI using uncropped arable land ratio, Remote Sensing, 6 (6), 5774-5794.

[15]   Hu, Y., Zeng, H., Tian, F., Zhang, M., Wu, B., Gilliams, S., ... & Yang, H. (2022). An interannual transfer learning approach for crop classification in the Hetao Irrigation district, China. Remote Sensing, 14(5), 1208.

[16]   Wu, B., Tian, F., Nabil, M., Bofana, J., Lu, Y., Elnashar, A., Zhang M. & Zhu, W. (2023). Mapping global maximum irrigation extent at 30m resolution using the irrigation performances under drought stress. Global Environmental Change, 79, 102652.

[17]   Wu F , Wu B , Zhang M , et al. Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network[J]. Sensors, 2021, 21(4):1165.

[18]   Zeng, H., Wu, B., Zhang, M., Zhang, N., Elnashar, A., Zhu, L., ... & Liu, W. (2021). Dryland ecosystem dynamic change and its drivers in Mediterranean region. Current Opinion in Environmental Sustainability, 48, 59-67.

[19]   Tian, F., Wu, B., Zeng, H., ... Zhang M, et al. (2020). Identifying the Links Among Poverty, Hydroenergy and Water Use Using Data Mining Methods. Water Resources Management, 34(5), 1725-1741.

[20]   Wu, B., Tian, F., Zhang, M., Zeng, H., & Zeng, Y. (2020). Cloud services with big data provide a solution for monitoring and tracking sustainable development goals. Geography and Sustainability, 1(1), 25-32.

[21]   Mrinal Singha, Bingfang Wu, Miao Zhang, 2017. Object-based paddy rice mapping using HJ-1A/B data and temporal features extracted from time series MODIS NDVI data. Sensors, 17(1), 10, https://doi.org/10.3390/s17010010.

[22]   M Singha, B Wu, M Zhang, 2016. An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India, Remote Sensing 8(6), doi:10.3390/rs8060479.

[23]   Y Zheng, B Wu, M Zhang, et al 2016. Crop Phenology Detection using High Spatial-Temporal Resolution Data Fused from SPOT5 and MODIS Products. Sensors 16(12), 2099; https://doi.org/10.3390/s16122099.

[24]   Bingfang Wu, Rene Gommes, Miao Zhang, et al., 2015. Global Crop Monitoring: A Satellite-Based Hierarchical Approach, Remote Sensing 7 (4), 3907-3933.

[25]   Xingzhi You, Jihua Meng, Miao Zhang, Bingfang Wu, 2013. Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method. Remote sensing, 5: 3190-3211.

[26]   Meng Jihua, Wu Bingfang, Zhang Miao, 2013. Estimating regional winter wheat leaf N concentration with MERIS by integrating a field observation-based model and histogram matching. Transactions of the ASABE, 56(4): 1589-1598.

[27]   Zhang, X., Wu, B., Ponce-Campos, G., Zhang, M., Chang, S., & Tian, F. 2018. Mapping up-to-date paddy rice extent at 10 m resolution in China through the integration of optical and synthetic aperture radar images. Remote Sensing, 10(8), 1200, ttps://doi.org/10.3390/rs10081200.

[28]   Wu, B., Zeng, H., Yan, N. and Zhang, M., 2018. Approach for Estimating Available Consumable Water for Human Activities in a River Basin. Water Resources Management, 32(7), pp.2353-2368.

[29]   F Waldner, D De Abelleyra, SR Verón, M Zhang, B Wu, et al., 2016. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity, International Journal of Remote Sensing 37(14), 3196-3231.

[30]   Li, Q., Cao, X., Jia, K., Zhang, M., & Dong, Q. 2014. Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data. International Journal of Remote Sensing, 35(16): 6076-6088. (SCI)

[31]   Waldner, F., Schucknecht, A., Lesiv, M., ... Zhang M., et al., 2019. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote sensing of environment, 221, 235-246.

[32]   Defourny, P., Bontemps, S., Bellemans, N., ... Zhang M., et al., 2019. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote sensing of environment, 221, 551-568.

[33]   Bingfang Wu, Jihua Meng, Qiangzi Li, Nana Yan, Xin Du, Miao Zhang, 2014. Remote sensing-based global crop monitoring: experiences with China’s CropWatch system. International Journal of Digital Earth, 7(2): 113-137. DOI: 10.1080/17538947.2013.821185.

[34]   Brus, D. J., Boogaard, H., Ceccarelli, T., Orton, T. G., Traore, S., Zhang, M. 2018. Geostatistical disaggregation of polygon maps of average crop yields by area-to-point kriging. European Journal of Agronomy, 97, 48-59.

[35]   Bingfang Wu, Miao Zhang, Hongwei Zeng, Guoshui Liu, Sheng Chang, René Gommes, 2014. New indicators for global crop monitoring in CropWatch-case study in North China Plain[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2014, 17(1): 012050.

[36]   Huanxue Zhang, Qiangzi Li, Miao Zhang, 2014. The Effects of Spatial Resolution on the Maize acreage estimation by Remote Sensing[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 17(1): 012052.

[37]   Cao, X., Li, Q., Du, X., Zhang, M., & Zheng, X. 2014. Exploring effect of segmentation scale on orient-based crop identification using HJ CCD data in Northeast China[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 17(1): 012047.

[38]   Bingfang Wu, Zeng Hongwei, Yan Nana, Zhang Miao, et al., 2018. Promoting Resilient Agriculture Practices for B&R Countries with Remote Sensing, Bulletin of Chinese Academy of Sciences, 32(3): 184-189.