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Full-time Academic Staff

Dr Han MA
馬晗 研究助理教授

ma han_edited.jpg

Room 10.01
391 72832
mahan@hku.hk

Research Assistant Professor

PhD, MS (Beijing Normal University); BS (Shandong University of Science and Technology)

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Dr. Ma received the B.S. degree from Shandong University of Science and Technology, M.S. and Ph.D. degree from Beijing Normal University. She was a Joint Ph.D. Student with the Department of Geographical Sciences, University of Maryland. She was working at Wuhan University prior to joining HKU. Her main research interests include radiative transfer modeling, development of land surface and atmosphere variables retrieval algorithms using data assimilation and machine learning techniques, to support global environmental change applications and sustainable development.

Research Interests

  • Land-atmosphere parameters retrieval from multiple satellite observations

  • Land and atmospheric radiative transfer modeling

  • Data assimilation and machine learning methodology

  • Global high-resolution seamless satellite biophyscial products development

Selected Publications

  2022  

  • Ma, H., Liang, S., Xiong, C., Wang, Q., Jia, A., & Li, B. (2022). Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021. Earth Syst. Sci. Data, 14(12), 5333-5347. https://doi.org/10.5194/essd-14-5333-2022
     

  • Ma, H., & Liang, S. (2022). Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sensing of Environment, 273, 112985. https://doi.org/https://doi.org/10.1016/j.rse.2022.112985

     

  • Ma, H., Liang, S., Zhu, Z., & He, T. (2021). Developing a Land continuous Variable Estimator to generate daily land products from Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19.
     

  • Ma, H., Xiong, C., Liang, S., Zhu, Z., Song, J., Zhang, Y., & He, T. (2022). Determining the accuracy of the landsat-based land continuous Variable Estimator. Science of Remote Sensing, 5, 100054. https://doi.org/https://doi.org/10.1016/j.srs.2022.100054
     

  • Zhang, G., Ma, H.*, Liang, S., Jia, A., He, T., & Wang, D. (2022). A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations. Remote Sensing of Environment, 279, 113132. https://doi.org/https://doi.org/10.1016/j.rse.2022.113132
     

  • Zhang, G., Ma, H.*, & Liang, S. (2022). Estimating 250-m Land Surface and Atmospheric Variables From MERSI Top-of-Atmosphere Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16. https://doi.org/10.1109/TGRS.2021.3089232
     

  • Ding, A., Ma, H.*, Liang, S., & He, T. (2022). Extension of the Hapke model to the spectral domain to characterize soil physical properties. Remote Sensing of Environment, 269, 112843.
     

  • Ding, A., Liang, S., Jiao, Z., Ma, H*., Kokhanovsky, A. A., & Peltoniemi, J. (2022). Improving the asymptotic radiative transfer model to better characterize the pure snow hyperspectral bidirectional reflectance. IEEE Transactions on Geoscience and Remote Sensing, 1-1. https://doi.org/10.1109/TGRS.2022.3144831
     

  • Xu, J., Liang, S., Ma, H., & He, T. (2022). Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks. Remote Sensing of Environment, 280, 113223. https://doi.org/https://doi.org/10.1016/j.rse.2022.113223
     

  • Zhang, Y., Liang, S., Zhu, Z., Ma, H., & He, T. (2022). Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 32-47. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2022.01.005
     

  • Ma, R., Xiao, J., Liang, S., Ma, H., He, T., Guo, D., Liu, X., & Lu, H. (2022). Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data. Geosci. Model Dev., 15(17), 6637-6657. https://doi.org/10.5194/gmd-15-6637-2022
     

  • Jin, H., Li, A., Liang, S., Ma, H., Xie, X., Liu, T., & He, T. (2022). Generating high spatial resolution GLASS FAPAR product from Landsat images. Science of Remote Sensing, 6, 100060.

  2021  

  • Ma, H., Liang, S., Shi, H., & Zhang, Y. (2021). An Optimization Approach for Estimating Multiple Land Surface and Atmospheric Variables From the Geostationary Advanced Himawari Imager Top-of-Atmosphere Observations. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 2888-2908. https://doi.org/10.1109/TGRS.2020.3007118
     

  • Chen, Y., Liang, S., Ma, H.*, Li, B., He, T., & Wang, Q. (2021). An all-sky 1km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data. Earth Syst. Sci. Data, 13(8), 4241-4261. https://doi.org/10.5194/essd-13-4241-2021
     

  • Li, B., Liang, S., Liu, X., Ma, H.*, Chen, Y., Liang, T., & He, T. (2021). Estimation of all-sky 1 km land surface temperature over the conterminous United States. Remote Sensing of Environment, 266, 112707. https://doi.org/https://doi.org/10.1016/j.rse.2021.112707
     

  • Liu, X., Liang, S., Li, B., Ma, H.*, & He, T. (2021). Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods. Remote Sensing, 13(13), 2592.
     

  • Jia, A., Ma, H., Liang, S., & Wang, D. (2021). Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method. Remote Sensing of Environment, 263, 112566.

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