PRIDE

Polyu remote sensing intelligence for dynamic Earth (Pride)

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Publications

2024-05-22 30 min read

Articles in refereed journals (Total: 114)

#my supervised students, RAs and Postdocs, *corresponding author

2024 (11)

  • Liu, D.#, Zhu, X.*, Holgerson, M., Bansal, S., & Xu, X. (2024). Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series. Remote Sensing of Environment, 315, 114484.
  • Cao, R., Li, L., Liu, L., Liang, H., Zhu, X. Shen, M., ... & Chen, J. (2024). A spatiotemporal shape model fitting method for within-season crop phenology detection. ISPRS Journal of Photogrammetry and Remote Sensing, 217, 179-198.
  • Huang, Y., Qiu, B., Yang, P., Wu, W., Chen, X., Zhu, X., ... & Chen, C. (2024). National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series. Computers and Electronics in Agriculture, 221, 109018.
  • Shen, M., Zhao, W., Jiang, N., Liu, L., Cao, R., Yang, W., ... & Zhang, X. (2024). Challenges in remote sensing of vegetation phenology. The Innovation Geoscience, 2(2), 100070.
  • Xu, F.#, Zhu, X.*, Chen, J., & Zhan, W. (2024). A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images. Remote Sensing of Environment, 306, 114141.
  • Qiu, B., Jian, Z., Yang, P., Tang, Z., Zhu, X., Duan, M., ... & Zhao, Z. (2024). Unveiling grain production patterns in China (2005–2020) towards targeted sustainable intensification. Agricultural Systems, 216, 103878.
  • Lin, Z., Cheng, K. H., Yang, D., Xu, F., Song, G., Meng, R., ... & Wu, J. (2024). Ecoregion-wise fractional mapping of tree functional composition in temperate mixed forests with sentinel data: Integrating time-series spectral and radar data. Remote Sensing of Environment, 304, 114026.
  • Qiu, B., Yu, L., Yang, P., Wu, W., Chen, J., Zhu, X., & Duan, M. (2024). Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China. The Crop Journal.
  • Peng, Y., Qiu, B., Tang, Z., Xu, W., Yang, P., Wu, W., ... & Li, Z. (2024). Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images. Remote Sensing of Environment, 303, 114016.
  • Tian, J.#, Zhu, X.*, Shen, M., Chen, J., Cao, R., Qiu, Y., & Xu, Y. N. (2024). Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring: A Simulation Study. Journal of Remote Sensing, 4, 0118.
  • Liu, L., Chen, J., Shen, M.*, Chen, X., Cao, R., Cao, X., Cui, X., Yang, W., Zhu, X., Li, L., & Tang, Y. A remote sensing method for mapping alpine grasslines based on graph‐cut. Global Change Biology, 2024, 30(1), p.e17005.

2023 (13)

  • Gu, Z., Chen, J., Chen, Y., Qiu, Y., Zhu, X., & Chen, X.* Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes. Remote Sensing of Environment, 2023, 299, p.113874.
  • Zhou, Y., Lai, W. W.*, & Zhu, X. Superpixel-based change detection for GPR time-lapse slices using fuzzy c-means and the Markov random field method. Tunnelling and Underground Space Technology, 2023, 141, 105369.
  • Zhao, S.#, Zhu, X.*, Liu, D., Xu, F., Wang, Y., Lin, L., ... & Yuan, Q. A hyperspectral image denoising method based on land cover spectral autocorrelation. International Journal of Applied Earth Observation and Geoinformation, 2023, 123, 103481.
  • Tan, X.#, Chen, R., Zhu, X.*, Li, X., Chen, J., Wong, M. S., Xu, S., & Xu, Y. N.# Spatial heterogeneity of uncertainties in daily satellite nighttime light time series. International Journal of Applied Earth Observation and Geoinformation, 2023, 123, 103484.
  • Tan, X.#, & Zhu, X.* CRYSTAL: A novel and effective method to remove clouds in daily nighttime light images by synergizing spatiotemporal information. Remote Sensing of Environment, 2023, 295, 113658.
  • Lou, Z., Wang, F., Peng, D.*, Zhang, X., Xu, J., Zhu, X., Wang, Y.#, Shi, Z., Yu, L., Liu, G., & Xie, Q. Combining shape and crop models to detect soybean growth stages. Remote Sensing of Environment, 2023, 298, p.113827.
  • Wang, Q., Yang, B., Li, L., Liang, H., Zhu, X., & Cao, R.* Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data. Remote Sensing, 2023, 15(20), p.5043.
  • Zhang, X., Shen, R., Zhu, X., Pan, B., Fu, Y., Zheng, Y., Chen, X., Peng, Q., & Yuan, W.* Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery. Frontiers in Environmental Science, 2023, 11, 1207882.
  • Li, L., Zhan, W.*, Ju, W., Peñuelas, J., Zhu, Z., Peng, S., Zhu, X., Liu, Z., Zhou, Y., Li, J., & Lai, J. Competition between biogeochemical drivers and land-cover changes determines urban greening or browning. Remote Sensing of Environment, 2023, 287, p.113481.
  • Helmer, E.H.*, Kay, S., Marcano-Vega, H., Powers, J.S., Wood, T.E., Zhu, X., Gwenzi, D., & Ruzycki, T.S. Multiscale predictors of small tree survival across a heterogeneous tropical landscape. Plos One, 2023, 18(3), p.e0280322.
  • Jiang, N., Shen, M.*, Chen, J., Yang, W., Zhu, X., Wang, X., & Peñuelas, J. Continuous advance in the onset of vegetation green-up in the Northern Hemisphere, during hiatuses in spring warming. npj Climate and Atmospheric Science, 2023, 6(1), p.7.
  • Chen, R., Tan, X.#, Zhang, Y., Chen, H., Yin, B., Zhu, X., & Chen, J.* Monitoring rainfall events in desert areas using the spectral response of biological soil crusts to hydration: Evidence from the Gurbantunggut Desert, China. Remote Sensing of Environment, March 2023, 286, 113448.
  • Xu, S.#, Zhu, X.*, Chen, J., Zhu, X., Duan, M.#, Qiu, B., Wan, L.#, Tan, X.#, Xu, Y. N.#, & Cao, R. A robust index to extract paddy fields in cloudy regions from SAR time series. Remote Sensing of Environment, February 2023, 285, 113374.

2022 (17)

  • Adeniran, I. A., Zhu, R., Yang, J., Zhu, X., Wong, M. S.* Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong, Remote Sensing, September 2022, 14, 4444.
  • Ma, X.*, Zhu, X., Xie, Q., Jin, J., Zhou, Y., Luo, Y., Liu, Y., Tian, J.#, Zhao, Y. Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications, Global Change Biology, September 2022, 28,7186–7204.
  • Wang, J., Lee, C., Zhu, X., Cao, R., Gu, Y., Wu, S., Wu, J.* A new object-class based gap-filling method for PlanetScope satellite image time series, Remote Sensing of Environment, October 2022, 280, 113136.
  • Qiu, B.*, Lin, D., Chen, C., Yang, P., Tang, Z., Jin, Z., Ye, Z., Zhu, X., Duan, M., Huang, H., Zhao, Z., Xu, W., Chen, Z. From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2, International Journal of Applied Earth Observation and Geoinformation, September 2022, 113, 103006.
  • Qiu, B.*, Hu, X., Chen, C., Tang, Z., Yang, P., Zhu, X., Yan, C., Jian, Z. Maps of cropping patterns in China during 2015–2021, Scientific Data, August 2022, 9, 479.
  • Liu, S., Zhou, J., Qiu, Y., Chen, J.*, Zhu, X., Chen, H. The FIRST model: Spatiotemporal fusion incorporating spectral autocorrelation, Remote Sensing of Environment, 2022, 279, 113111.
  • Li, M., Cao, S.*, Zhu, X., Xu, Y. Techno-economic analysis of the transition towards the large-scale hybrid wind-tidal supported coastal zero-energy communities. Applied Energy, 2022, 316, 119118.
  • Wang, Q., Wang, L., Zhu, X., Ge, Y., Tong, X.*, Atkinson, P. Remote sensing image gap filling based on spatial-spectral random forests. Science of Remote Sensing, 2022, 5, 100048.
  • Shu, H.#, Jiang, S., Zhu, X.*, Xu, S.#, Tan, X.#, Tian, J.#, Xu, Y. N.#, Chen, J. Fusing or filling: Which strategy can better reconstruct high-quality fine-resolution satellite time series? Science of Remote Sensing, 2022, 5, 100046.
  • Zhu, X., Zhan, W., Zhou, J., Chen, X., Liang, Z.#, Xu, S.#, Chen, J.* A novel framework to assess all-round performances of spatiotemporal fusion models. Remote Sensing of Environment, 2022, 274, 113002.
  • Huang, Y., Qiu, B.*, Chen, C., Zhu, X., Wu, W., Jiang, F., Lin, D., Peng, Y. Automated soybean mapping based on canopy water content and chlorophyll content using Sentinel-2 images. International Journal of Applied Earth Observation and Geoinformation, 2022, 109, 102801.
  • Zhao, D., Hou, Y.*, Zhang, Z., Wu, Y., Zhang, X., Wu, L., Zhu, X., Zhang, Y. Temporal resolution of vegetation indices and solar-induced chlorophyll fluorescence data affects the accuracy of vegetation phenology estimation: A study using in-situ measurements. Ecological Indicators, 2022, 136, 108673.
  • Yu, Z.#, Zhu, X., Liu, X.* Characterizing metro stations via urban function: Thematic evidence from transit-oriented development (TOD) in Hong Kong. Journal of Transport Geography, 2022, 99, 103299.
  • Zhu, X.*, Tan, X.#, Liao, M., Liu, T., Su, M., Zhao, S.#, Xu, Y. N.#, Liu, X. Assessment of a New Fine-Resolution Nighttime Light Imagery From the Yangwang-1 (“Look up 1”) Satellite. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 1-5.
  • Tan, X.#, Zhu, X.*, Chen, J., Chen, R. Modeling the direction and magnitude of angular effects in nighttime light remote sensing. Remote Sensing of Environment, 2022, 269, 112834.
  • Shen, M., Zhu, X., Peng, D., Jiang, N.*, Huang, Y.*, Chen, J., Wang, C., Zhao, W. Greater temperature sensitivity of vegetation greenup onset date in areas with weaker temperature seasonality across the Northern Hemisphere. Agricultural and Forest Meteorology, 2022, 313, 108759.
  • Tan, X.#, Zhu, X., Li, Q.*, Li, L., Chen, J. Tidal phenomenon of the dockless bike-sharing system and its causes: the case of Beijing. International Journal of Sustainable Transportation, 2022, 16, 287-300.

2021 (13)

  • Zhu, X., Helmer, E.H.*, Gwenzi, D., Collin, M., Fleming, S., Tian, J.#, Marcano-Vega, H., Meléndez-Ackerman, E.J., Zimmerman, J.K. Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series. Remote Sensing. 2021, 13, 4736.
  • Guo, X., Cao, S.*, Xu, Y., Zhu, X. The Feasibility of Using Zero-Emission Electric Boats to Enhance the Techno-Economic Performance of an Ocean-Energy-Supported Coastal Hotel Building. Energies. 2021, 14, 8465.
  • Tian, J.#, Zhu, X.*, Wan, L.#, Collin, M. Impacts of Satellite Revisit Frequency on Spring Phenology Monitoring of Deciduous Broad-Leaved Forests Based on Vegetation Index Time Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14, 10500-10508.
  • Gao, H., Zhu, X., Guan, Q.*, Yang, X., Yao, Y., Zeng, W., Peng, X. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units. IEEE Transactions on Geoscience and Remote Sensing. 2021, in press, doi: 10.1109/TGRS.2021.3080384.
  • Xu, S.#, Zhu, X.*, Helmer, H., Tan, X.#, Tian, J.#, Chen, X. The damage of urban vegetation from super typhoon is associated with landscape factors: Evidence from Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation. 2021, 104, 102536.
  • Tian, J.#, Zhu, X.*, Chen, J., Wang, C., Shen, M., Yang, W., Tan, X.#, Xu, S.#, Li, Z. Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency. ISPRS Journal of Photogrammetry and Remote Sensing. 2021, 180, 29-44.
  • Yu, Z.#, Zhu, X., Liu, X., Wei, T., Yuan, H., Xu, Y., Zhu, R., He, H., Wang, H., Wong, M.S., Jia, P., Guo, S., Shi, W., Chen, W.* Reopening International Borders without Quarantine: Contact Tracing Integrated Policy against COVID-19. International Journal of Environmental Research and Public Health. 2021, 18, 7494.
  • Wang, J., Yang, D., Chen, S., Zhu, X., Wu, S., Bogonocich, M., Guo, Z., Zhu, Z., Wu, J.* Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images. Remote Sensing of Environment. 2021, 264, 112604.
  • Zhou, Y.#, Wei, T.*, Zhu, X., Collin, M. A Parcel-Based Deep-learning Classification to Map Local Climate Zones from Sentinel-2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, doi: 10.1109/JSTARS.2021.3071577.
  • Zhuo, L., Zhang, C., Zhu, X.*, Huang, T., Hu, Y., Tao, H. iSEAM: improving the blooming effect adjustment for DMSP-OLS nighttime light images by considering spatial heterogeneity of blooming distance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, doi: 10.1109/JSTARS.2021.3065399.
  • Chen, X., Zhang, A.#, Wang, H., Gallaher, A., Zhu, X.* Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships. International Journal of Geographical Information Science. 2021, 35, 446-465.
  • Kong, J., Ryu, Y.*, Huang, Y., Dechant, B., Hounorg, R., Guan, K., Zhu, X. Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape. Agricultural and Forest Meteorology. 2021, 297, 108255.
  • Zhou, J., Chen, J., Chen, X.*, Zhu, X., Qiu, Y., Song, H., Rao, Y., Zhang, C., Cao, X., Cui, X. Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction. Remote Sensing of Environment. 2021, 252, 112130.

2020 (13)

  • Tian, J.#, Zhu, X.*, Shen, Z., Wu, J., Xu, S., Liang, Z., Wang, J. Investigating the urban-induced microclimate effects on winter wheat spring phenology using Sentinel-2 time series. Agricultural and Forest Meteorology. 2020, 294, 108153.
  • Shen, M., Jiang, N., Peng, D., Rao, Y., Huang, Y., Fu, Y., Yang, W., Zhu, X., Cao, R., Chen, X., Chen, J., Miao, C., Wu, C., Wang, T., Liang, E., Tang, Y. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agricultural and Forest Meteorology. 2020, 291, 108077.
  • Cao, R.*, Chen, Y., Chen, J., Zhu, X., Shen, M. Thick cloud removal in Landsat images based on autoregression of Landsat time-series data. Remote Sensing of Environment. 2020, 249, 112001.
  • Guo, D., Shi, W.*, Hao, M., Zhu, X. FSDAF 2.0: Improved the performance of retrieving land cover change and preserving spatial details. Remote Sensing of Environment. 2020, 248, 111973.
  • Chen, X., Wang, W., Chen, J.*, Zhu, X., Shen, M., Gan, L., Cao, X. Does any phenological event defined by remote sensing deserve particular attention? An examination of spring phenology of winter wheat in Northern China. Ecological Indicators. 2020, 116, 106456.
  • Chen, Y., Cao, R.*, Chen, J., Zhu, X., Zhou, J., Wang, G., Shen, M., Chen, X., Yang, W. A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion. IEEE Transactions on Geoscience and Remote Sensing. 2020, 58, 5179-5194.
  • Wei, J., Huang, W., Li, Z.*, Sun, L., Zhu, X., Yuan, Q., Liu, L., Cribb, M. Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches. Remote Sensing of Environment. 2020, 248, 112005.
  • Hossain, M.P., Junus, A., Zhu, X., Jia, P., Wen, T., Pfeiffer, D., Yuan, H.* The effects of border control and quarantine measures on the spread of COVID-19. Epidemics. 2020, 32, 100397.
  • Tian, J.#, Zhu, X.*, Wu, J., Shen, M., Chen, J. Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology. Remote Sensing. 2020, 12, 117.
  • Hassan, S., Shi, W.*, Zhu, X., Abbas, S., Khan, H.U.A. Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data. Sustainability. 2020, 12, 4350.
  • Faridatul, M., Wu, B.*, Zhu, X., Wang, S. Improving remote sensing based evapotranspiration modelling in a heterogeneous urban environment. Journal of Hydrology. 2020, 581, 124405.
  • Williams, T. K.#, Wei, T., Zhu, X.* Mapping Urban Slum Settlements Using Very High-Resolution Imagery and Land Boundary Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, doi: 10.1109/JSTARS.2019.2954407.
  • Hasan, S., Shi, W.*, Zhu, X. Impact of land use land cover changes on ecosystem service value–A case study of Guangdong, Hong Kong, and Macao in South China. PLoS One. 2022, 15, e0231259.

2019 (9)

  • Zhu, X., Liu, D.* Investigating the Impact of Land Parcelization on Forest Composition and Structure in Southeastern Ohio Using Multi-Source Remotely Sensed Data. Remote Sensing. 2019, 11, 2195.
  • Hassan, S., Shi, W.*, Zhu, X., Abbas, S. Monitoring of land use/land cover and socioeconomic changes in south China over the last three decades using Landsat and nighttime light data. Remote Sensing. 2019, 11, 1658.
  • Faridatul, M., Wu, B.*, Zhu, X. Assessing long-term urban surface water changes using multi-year satellite images: A tale of two cities, Dhaka and Hong Kong. Journal of Environmental Management. 2019, 243, 287-298.
  • Liu, M., Yang, W., Zhu, X., Chen, J.*, Chen, X., Yang, L., Helmer, E.H. An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series. Remote Sensing of Environment. 2019, 227, 74-89.
  • Piao, S.*, Liu, Q., Chen, A., Janssens, I.A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., Zhu, X. Plant phenology and global climate change: Current progresses and challenges. Global Change Biology. 2019, 25, 1922-1940.
  • Zhu, X.*, Leung, K.#, Li, W.#, Cheung, L.# Monitoring interannual dynamics of desertification in Minqin county, China, using dense Landsat time series. International Journal of Digital Earth. 2019, available online (DOI: 10.1080/17538947.2019.1585979).
  • Cao, X., Hu, Y., Zhu, X.*, Shi, F., Zhuo, L., Chen, J. A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images. Remote Sensing of Environment. 2019, 224, 401-411.
  • Shen, Z.#, Zhu, X.*, Cao, X., Chen, J. Measurement of blooming effect of DMSP-OLS nighttime light data based on NPP-VIIRS data. Annals of GIS. 2019, 25, 153-165.
  • Wang, Y., Zhu, X., Wu, B.* Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier. International Journal of Remote Sensing. 2019, 40, 7356-7370.

2018 (6)

  • An, S., Zhu, X., Shen, M*., Wang Y., Cao, R., et al. Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000‐2016 on the Tibetan Plateau, Global Change Biology, 2018, 24, 5411-5425.
  • Zhu, X., Helmer, E.H., G. An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions, Remote Sensing of Environment, 2018, 214, 135-153.
  • Kwan, C*., Zhu, X., Gao, F., Chou, B., Perez, D., Li, J., Shen, Y., Koperski, K., Marchisio, G. Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images. Sensors, 2018, 18(4), 1051.
  • Zhu, X., Cai, F.#, Tian, J.#, Williams, T.# Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions. Remote Sensing, 2018, 10(4), 527.
  • Kwan, C*., Budavari, B., Gao, F., Zhu, X. A hybrid color mapping approach to fusing MODIS and Landsat images for forward prediction. Remote Sensing, 2018, 10(4), 520.
  • Chen, X., Liu, M., Zhu, X., Chen, J*., Zhong, Y. “Blend-then-Index” or “Index-then-Blend”: A Theoretical Analysis for Generating High-resolution NDVI time series by STARFM. Photogrammetric Engineering & Remote Sensing, 2018, 84(2), 65-73.

2017 and earlier (32)

  • Gao, L., Zhan, W., Huang, F., Zhu, X., Zhou, J., Quan, J., Du, P., Li, M. Disaggregation of remotely sensed land surface temperature: A simple yet flexible index (SIFI) to assess method performances. Remote Sens. Environ. 2017, 200, 206–219.
  • Gwenzi, D., Helmer, E.H., Zhu, X., Lefsky, M.A., Marcano-Vega, H. Predictions of tropical forest biomass and biomass growth based on stand height or canopy area are improved by Landsat-scale phenology across Puerto Rico and the U.S. Virgin Islands. Remote Sens. 2017, 9(2), 1-18.
  • Wang, Q., Zhang, Y., Onojeghuo, A.O., Zhu, X., Atkinson, P.M. Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10(9), 1–8.
  • Zhan, W., Huang, F., Quan, J., Zhu, X., Gao, L., Zhou, J., and Ju, W. Disaggregation of remotely sensed land surface temperature: A new dynamic methodology. Journal of Geophysical Research: Atmospheres, 2016, 121, 10538-10554.
  • Zhu, X., Helmer E., Liu, D., Chen, J., Gao, F., and Lefsky M. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 2016, 172, 165-177.
  • Gao F., Hilker T., Zhu, X., Anderson M., Masek J., Wang P., and Yang Y. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geosciecne and Remote Sensing Magazine, 2015, 3, 47-60.
  • Rao Y., Zhu, X., Chen J., and Wang J. An improved method for producing high-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and a Landsat TM/ETM+ image. Remote Sensing, 2015, 7, 7865-7891.
  • Tewes, A., Thonfeld, F., Schmidt, M., Oomen, R., Zhu, X., Dubovyk O., Menz, G., and Schellberg, J. Using RapidEye and MODIS data fusion to monitor vegetation dynamics in semi-arid rangelands in South Africa, Remote Sensing, 2015, 7, 6510-6534.
  • Zhu, X., and Liu, D. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102, 222-231.
  • Wang, H., Liu, D., Lin, H., Alvaro, M., and Zhu, X. NDVI and Vegetation Phenology Dynamics under the Influence of Sunshine Duration on the Tibetan Plateau, International Journal of Climatology, 2015, 35, 687-698.
  • Michishita, R., Chen, L., Chen, J., Zhu, X., and Xu, B. Spatiotemporal reflectance blending in a wetland environment, International Journal of Digital Earth, 2015, 8, 364-382.
  • Zhu, X., and Liu, D. Accurate mapping of forest types using dense Landsat time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96, 1-11.
  • Zhang F., Zhu, X., and Liu, D. Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 2014, 35(9), 3237-3253.
  • Chen, Y., Zhan, W., Quan, J., Zhou, J., Zhu, X., and Sun, H. Disaggregation of remotely sensed land surface temperature: a generalized paradigm, IEEE Transactions on Geoscience and Remote Sensing, 2014, 52, 5952-5965.
  • Zhu, X., and Liu, D. MAP-MRF Approach to Landsat ETM+ SLC-off Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2014, 52,1131-1141.
  • Zhou, Y., Chen, J., Guo, Q., Cao, R., and Zhu, X. Restoration of Information Obscured by Mountainous Shadows through Landsat TM/ETM+ Images without the Use of DEM Data: A New Method. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52, 313-328.
  • Fu, D., Chen, B., Wang, J., Zhu, X., and Hilker, T. An improved image fusion approach based on enhanced spatial and temporal adaptive reflectance fusion model, Remote Sensing, 2013, 5, 6346-6360.
  • Zhou, Y., Chen, J., Chen, X., Cao, X., and Zhu, X. Two important indicators with potential to identify Caragana microphylla in xilin gol grassland from temporal MODIS data. Ecological Indicators, 2013, 34, 520-527.
  • Cui, X., Guo, L., Chen, J., Chen, X. and Zhu, X. Estimating tree-root biomass in different depths using ground-penetrating radar: evidence from a controlled experiment. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51, 3410-3423.
  • Zhu, X., Liu, D. and Chen, J. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images, Remote Sensing of Environment, 2013, 124, 49-60.
  • Liu, D., Zhu, X. An enhanced physical method for downscaling thermal infrared radiance. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4), 690-694.
  • Zhu, X., Gao, F., Liu, D. and Chen, J. A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images. IEEE Geoscience and Remote Sensing Letters, 2012, 9(3), 521-525.
  • Zhan, W., Chen, Y., Zhou, J., Wang, J., Liu, W., Voogt, J., Zhu, X., Quan, J. and Li, J. Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 2013, 131, 119-139.
  • Chen, J., Zhu, X., Vogelmann, J. E., Gao, F. and Jin, S. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 2011, 115, 1053-1064.
  • Shen, M., Tang, Y., Chen, J., Zhu, X. and Zheng, Y. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agriculture and Forest Meteorology, 2011, 151, 1711-1722.
  • Cui, X., Chen, J., Shen, J., Cao, X., Chen, X. and Zhu, X. Modeling tree root diameter and biomass by ground-penetrating radar. Science China Earth Sciences, 2011, 54(5), 711-719.
  • Zhu, X., Chen, J., Gao, F. and Masek, J. G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 2010, 114, 2610-2623.
  • Chen, J., Zhu, X., Imura, H. and Chen, X. Consistency of accuracy assessment indices for soft classification: simulation analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65, 156-164.
  • Shen, M., Chen, J., Zhu, X., Chen, X. and Tang, Y. Do flowers affect biomass estimate accuracy from NDVI and EVI? Evidence from an alpine meadow on the Tibetan Plateau. International Journal of Remote Sensing, 2010, 31, 2139-2149.
  • Shen, M., Chen, J., Zhu, X. and Tang, Y. Yellow Flower can Decrease NDVI and EVI Values – Evidence from a Field Experiment in an Alpine Meadow, Canadian Journal of Remote Sensing, 2009, 35, 99-106.
  • Chen, J., Shen, M., Zhu, X. and Tang, Y. Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan Plateau, Ecological Indicators, 2009, 9, 818-823.
  • Zhu, X., Li, Q., Shen, M., Chen, J. and Wu, J. A methodology for multiple cropping index extraction based on NDVI time-series. Journal of Natural Resources, 2008, 23, 534-544 (In Chinese).

Book Chapters

  • Liu, D., Zhu, X. Dense Satellite Image Time Series Analysis: Opportunities, Challenges, and Future Directions. In Li, B. et al. (Eds.), New Thinking in GIScience. (pp. 233-242). 2022, Higher Education Press & Springer.
  • Wong, M., Zhu, X., Abbas, S., Kwok, C., Wang, M. Optical Remote Sensing. In Shi, W. et al. (Eds.), Urban Informatics. (pp. 315). 2021, Springer Singapore.
  • Zhu, X., Helmer, E.H., Chen, J., Liu, D. An Automatic System for Reconstructing High-Quality Seasonal Landsat Time Series. In Weng Q (Eds.), Remote Sensing Time Series Image Processing. (pp. 25-42). 2018, CRC Press.
  • Chen, J., Rao, Y., Zhu, X. Spatiotemporal Data Fusion to Generate Synthetic High Spatial and Temporal Resolution Satellite Images. In Weng Q (Eds.), Remote Sensing Time Series Image Processing. (pp. 43-68). 2018, CRC Press.

Conference proceedings

  • Zhu, X., Wu, J. and Chen, J. Extracting cropping index variations in northern china based on NDVI time-series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2008, Vol.XXXVII, 977-981.

Conference Presentations

  • Zhu, X. A Series of Automated Techniques to Generate Dense Satellite Time-Series with High Spatial Resolutions for Land Surface Monitoring. AOGS Annual Meeting, 2017, Singapore
  • Zhu, X. Spatiotemporal Fusion of Multi-Source Satellite Images. 3S conference, 2017, Nanjing, China
  • Zhu, X. and Helmer, E. The potential of using Landsat time-series to extract tropical dry forest phenology. AGU Fall Meeting, 2016, San Francisco, CA, USA.
  • Zhu, X., Helmer, E., and Lefsky, M. Reconstructing Seasonal Landsat Time-series to Detect Tropical Forest Phenology in Mona Island, Puerto Rico. AAG Annual Meeting, 2016, San Francisco, CA, USA.
  • Zhu, X. and Liu, D. Accurate Mapping of Forest Types in Southeastern Ohio Using Landsat time-series. AAG Annual Meeting, 2013, Los Angeles, CA, USA.
  • Zhu, X. and Liu, D. How to produce high-quality Landsat time-series? SSES Conference on Spatial and Environmental Statistics, 2012, Ohio State University, Columbus, USA.
  • Zhu, X. and Liu, D. A New Kriging Based Method for Filling Gaps in Landsat ETM+ SLC-off Images. AAG Annual Meeting,2012, New York, USA.
  • Zhu, X., Liu, D. and Cai, S. Mapping the forest distribution based on synthetic Landsat time-series in Appalachian Ohio. AAG Annual Meeting, 2011, Seattle, WA, USA.
  • Zhu, X., Wu, J. and Chen, J. Extracting cropping index variations in northern china based on NDVI time-series. Congress of ISPRS, 2008, Beijing, P. R. China.

Invited Talks

  • Invited talks at mainland Universities and institutions: China University of Geosciences, Capital Normal University, CAS Institute of Tibetan Plateau, and Nanjing University during 2017.
  • Generating High-Quality Landsat Image Time Series for Environmental Studies, China Agricultural University, 2016, November 25.
  • Reconstructing Seasonal Landsat Time Series to Detect Vegetation Phenology in Cloudy Areas, Beijing Normal University, 2016, October 21.
  • Integrated use of spatial and temporal data in ecological studies, California State University, Chico, 2016, February 8.
  • Challenges of applying optical images to tropical forests, University of Electronic Science and Technology of China, 2015, September 14.
  • How to use spatial and temporal information to generate high-quality time-series data? Beijing Normal Universit, 2015, September 11.
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