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Dr Jianglei XU

Postdoctoral Research Fellow

Education:

Ph.D. in Photogrammetry and Remote Sensing, Wuhan University (09/2020-06/2023)

M.S. in Cartography and Geographic Information Sciences, Beijing Normal University (09/2017-06/2020)

B.S. in Geographic Information Sciences (09/2013-06/2017)


Research Interests:

  • Earth energy budget, inversion algorithms development

  • high-level satellite products generation

  • spatiotemporal analysis on variations of earth energy budget


Publications:

  • Xu, J., Liang, S., Ma, H., Chen, Y., Li, W., Ma, Y., Zhao, X., Jiang, B., Zhang, X., & Guan, S. (2026). Joint estimation of global daily 1 km surface radiation budget components from MODIS observations (2000−2023) using conservation-constrained deep neural networks. Remote Sensing of Environment, 333, 115135.

  • Xu, J., Liang, S., Ma, H., He, T., Zhang, Y., & Zhang, G. (2023). A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018. Remote Sensing of Environment, 290, 113550.

  • Xu, J., Liang, S., He, T., Ma, H., Zhang, Y., Zhang, G., et al. (2023). Variability and trends in land surface longwave radiation fluxes from six satellite and reanalysis products. International Journal of Digital Earth, 16(1), 2912–2940.

  • 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.

  • Xu, J., Liang, S., & Jiang, B. (2022). A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network. Earth System Science Data, 14, 2315–2341.

  • Xu, J., Jiang, B., Liang, S., Li, X., Wang, Y., Peng, J., Chen, H., Liang, H., & Li, S. (2020). Generating a high-resolution time-series ocean surface net radiation product by downscaling J-OFURO3. IEEE Transactions on Geoscience and Remote Sensing.

  • Xu, J., & Jiang, B. (2019). Downscaling ocean surface net radiation at global scales with random forest. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.

  • Ma, Y., Liang, S., Ma, H., He, T., Shi, X., Li, W., Cai, D., Xiao, X., Guan, S., Liu, W., Xu, J., Chen, Y., & Zhang, Y. (2026). An integrated atmospheric-topographic correction framework for land surface reflectance estimation using a spatial-spectral Attention U-Net model. Remote Sensing of Environment, 334, 115188.

  • Li, W., Liang, S., Chen, K., Chen, Y., Ma, H., Xu, J., Ma, Y., Zhang, Y., Guan, S., Fang, H., & Shi, Z. (2026). AgriFM: A multi-source temporal remote sensing foundation model for agriculture mapping. Remote Sensing of Environment, 334, 115234.

  • Fang, H., Liang, S., Li, W., Chen, Y., Ma, H., Xu, J., Ma, Y., He, T., Tian, F., Zhang, F., & Liang, H. (2026). Generating an annual 30 m rice cover product for monsoon Asia (2018–2023) using harmonized Landsat and Sentinel-2 data and the NASA-IBM geospatial foundation model. Remote Sensing of Environment, 335, 115256.

  • Liang, H., Liang, S., Jiang, B., He, T., Tian, F., Xu, J., … & Fang, H. (2025). Generation of global 1 km daily land surface–air temperature difference and sensible heat flux products from 2000 to 2020. Earth System Science Data, 17, 5571–5600.

  • Zhang, Y., Liang, S., Ma, H., He, T., Tian, F., Zhang, G., & Xu, J. (2025). A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model. Earth System Science Data, 17, 5181–5207.

  • Ma, H., Wang, Q., Li, W., Chen, Y., Xu, J., Ma, Y., Huang, J., & Liang, S. (2025). The first gap-free 20 m 5-day LAI/FAPAR products over China (2018–2023) from integrated Landsat-8/9 and Sentinel-2 Analysis Ready Data. Remote Sensing of Environment, 331, 115048.

  • Zhang, G., Liang, S., Ma, H., He, T., Yin, G., Xu, J., Liu, X., & Zhang, Y. (2024). Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network. Remote Sensing of Environment, 301, 113928.

  • Jiang, B., Liang, S., Jia, A., Xu, J., Zhang, X., Xiao, Z., Zhao, X., Jia, K., & Yao, Y. (2018). Validation of the surface daytime net radiation product from version 4.0 GLASS product suite. IEEE Geoscience and Remote Sensing Letters, 16(4), 509–513.

  • Zhang, G., Liang, S., Ma, H., He, T., Yin, G., Xu, J., et al. (2024). Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network. Remote Sensing of Environment, 301, 113928.

  • Liu, X., Liang, S., Ma, H., Li, B., Zhang, Y., Li, Y., He, T., Zhang, G., Xu, J., & Xiong, C. (2024). Landsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020. GIScience & Remote Sensing, 61(1), 2300214.

  • Xiong, C., Ma, H., Liang, S., He, T., Zhang, Y., Zhang, G., & Xu, J. (2023). Improved global 250 m 8-day NDVI and EVI products from 2000–2021 using the LSTM model. Scientific Data, 10(1), 800.

  • Zhang, Y., Liang, S., Ma, H., He, T., Wang, Q., Li, B., Xu, J., Zhang, G., et al. (2023). Generation of global 1-km daily soil moisture product from 2000 to 2020 using ensemble learning. Earth System Science Data Discussions, 2023, 1–37.

Dr Jianglei XU
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