研究方向

  • 遥感大数据与智慧水文
    （利用卫星与地面观测、机器学习和人工智能方法，重构与预测水循环关键过程）
  • 全球水循环关键要素定量遥感
    （发展微波遥感与数据融合方法，定量反演土壤水分、蒸散发、植被含水量等关键变量）
  • 地表水-热过程模拟与预报
    （揭示水-能量耦合机制，改进水文与陆面过程模拟，提升极端事件预测能力）
  • 陆面同化系统开发与应用
    （推动遥感数据与模型融合，提升全球和区域水文-气候-生态预报水平）
  • 水-碳循环相关自然灾害机理与预警
    （聚焦骤旱、森林火灾等典型灾害的发生机制，提出遥感监测与早期预警新方法）；

培养方向：遥感水文、生态水文，欢迎有志于从事上述研究的本科生、硕士、博士研究生及科研人员联系！

学历背景

  • 2008.09-2014.07  中国科学院 地理科学与资源研究所  博士
  • 2012.10-2014.03  美国农业部 水文与遥感实验室    访学博士
  • 2004.09-2008.06  中山大学 地理科学与规划学院    学士
  • 2006.08-2006.12  香港浸会大学 地理系        交换学习

工作经历

  • 2023 - 至今      中山大学 地理科学与规划学院             教授、博士生导师
  • 2017 - 2023     中山大学 地理科学与规划学院             副教授 、博士生导师
  • 2014 - 2016          中山大学 地理科学与规划学院        讲师 、硕士生导师

社会任职

  1. Remote Sensing 客座编辑；
  2. Frontiers in Remote Sensing 客座编辑；Frontiers in Big Data - Data-driven Climate Sciences 客座编辑；
  3. 《中国地理科学（英文版）》青年编委；
  4. 《福建师范大学学报（自然科学版）》首届青年编委；
  5. 《水利水电技术（中英文）》特邀编委； 2020、2021年度优秀审稿专家；2021年度优秀编委；
  6. 《水利水运工程学报》2021年度优秀审稿专家；
  7. 担任30+SCI期刊审稿人：Nature Climate Change, PNAS, Remote Sensing of Environment, Global Change Biology, Science Bulletin, ISPRS Journal of Photogrammetry and Remote Sensing, Hydrology and Earth System Sciences, GIScience Remote Sensing, Climate Dynamics, Science of The Total Environment, Environmental Research Letters, Journal of Hydrology, Journal of Hydrometeorology, International Journal of Digital Earth, Journal of Cleaner Production, Remote Sensing Applications: Society and Environment, IEEE Transactions on Geoscience and Remote Sensing, Agricultural Water Management, Journal of Hydrology: Regional Studies, International Journal of Remote Sensing, Hydrology Research, Marine and Freshwater Research, Frontiers of Earth Science, Meteorological Applications, Advances in Meteorology;
  8. 国际数字地球学会中国委员会数字水圈分会委员；
  9. 国际水文科学协会中国委员会陆气关系分会委员；
  10. 意大利教育部科技咨询专家 (基础研究项目评审专家)；
  11. 美国气象学会会员；IEEE 地学与遥感学会会员；
  12. 中国地理信息产业协会农业地理信息与遥感委员会委员；
  13. 中国农业绿色发展研究会农业防灾减灾专业委员会委员；
  14. 国家自然科学基金委通讯评委；
  15. 广东省农业厅项目评委；
  16. 广东省水利厅科学技术委员会基础专家库成员；
  17. 广州市第二届重大行政决策论证专家

课程教学

  1. 讲授《数量地理学》、《地学数据分析》、《地理学专业外语》等专业课程；
  2. 指导本科生获中国自然资源学会第二届大学生自然资源科技作品大赛 三等奖；
  3. 指导本科生获中国自然资源学会第三届大学生自然资源科技作品大赛 一等奖；
  4. 指导研究生获全国农业遥感应用大赛优胜奖（全国12强）；
  5. 多次指导本科生获中山大学优秀毕业论文奖（2016/2017/2019/2020/2021/2022 届）；
  6. 团队多名研究生获国家奖学金、校级优秀毕业生、院级优秀毕业生、87经地奖学金等荣誉；
  7. 2019年地理科学与规划学院“我心目中的良师”

论著专利

  1. Qiu J.*, C. He, X. Liu, L. Gao, C. Tan, X. Wang, D. Kong, J. P. Wigneron, D. Chen, and J. Xia, Projecting Dry-Wet Abrupt Alternation across China from the perspective of soil moisture, npj Climate and Atmospheric Science, 2024, 7(1), 269, https://doi.org/10.1038/s41612-024-00808-w.
  2. Feng S., L. Gao, J. Qiu*, X. Liu,  W. T. Crow, T. Zhao, C. Tan, S. Wang, and J. P. Wigneron, Can real-time NDVI observations better constrain SMAP soil moisture retrievals? Remote Sensing of Environment, 2025, 318, 114569, https://doi.org/10.1016/j.rse.2024.114569.
  3. Xiong Z., Z. Zhang, H. Gui, S. Hu, X. Chen, L. Gao, Y.  He, J. Qiu*, and Q. Xin*, A meteorology-driven Transformer network to predict soil moisture for agriculture drought forecasting, IEEE Transactions on Geoscience and Remote Sensing, 2025, 63, 1-18, 4405818, https://doi.org/10.1109/TGRS.2025.3543611.
  4. Pan Y., S. Hu, L. Gao, J. Qiu* , Z. Peng, L. Hu, T. Zhao, J. Dong, and C. Tan, Capabilities of microwave-based soil moisture products in capturing extreme hydrological conditions, Journal of Hydrology, 2025.
  5. Ren Y., J. Qiu*, Z. Zeng, X. Liu, S. Sitch, K. Pilegaard, T. Yang, S. Wang, W. Yuan, and A. K. Jain, Earlier spring greening in Northern Hemisphere terrestrial biomes enhanced net ecosystem productivity in summer, Communications Earth & Environment, 2024, 5(1), 122, https://doi.org/10.1038/s43247-024-01270-5 (ESI 高被引论文).
  6. Mai R., Q. Xin, J. Qiu*, Q. Wang, and P. Zhu, High spatial resolution soil moisture improves crop yield estimation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17, 19067–19077, https://doi.org/10.1109/JSTARS.2024.3417424.
  7. Zhang J., D. Kong, J. Li, J. Qiu, Y. Zhang, X. Gu, and M. Guo, Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation, Journal of Hydrology, 2024, 650, 132549, https://doi.org/10.1016/j.jhydrol.2024.132549.
  8. Xu F., Y. Qu, V. A. Bento, H. Song, J. Qiu, J. Qi, L. Wan, R. Zhang, L. Miao, X. Zhang, and Q. Wang, Understanding climate change impacts on drought in China over the 21st century: a multi-model assessment from CMIP6, npj Climate and Atmospheric Science, 2024, 7(1), 32, https://doi.org/10.1038/s41612-024-00578-5 (ESI 高被引、ESI热点论文).
  9. Xue R., W. Zuo, Z. Zheng, Q. Han, J. Shi, Y. Zhang, J. Qiu*, S. Wang, Y. Zhu, W. Cao, and X. Zhang*, Interpreting controls of stomatal conductance across different vegetation types via machine learning, Water, 2024, 16(16), 2251, https://doi.org/10.3390/w16162251.
  10. Qiu J.*, S. Feng, and W. Yuan, Upward-moving mountain treelines: An indicator of changing climate, Global Change Biology, 2023, 29(24), 6832–6833, https://doi.org/10.1111/gcb.16954.
  11. Feng S., J. Qiu*, W. T. Crow, X. Mo, S. Liu, S. Wang, L. Gao, X. Wang, and S. Chen, Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland, Journal of Hydrology, 2023, 617, 129015, https://doi.org/10.1016/j.jhydrol.2022.129015.
  12. Wang Z., Y. Zhang, G. Govers, G. Tang, T. Quine, J. Qiu, A. Navas, H. Fang, Q. Tan, and K. Van Oost, Temperature effect on erosion-induced disturbances to soil organic carbon cycling, Nature Climate Change, 2023, 13, 174–181, https://doi.org/10.1038/s41558-022-01562-8.
  13. Zhang Z., D. Wang, X. Wu, Y. Mei, J. Qiu, and J. Zhu, Unveiling flood-generating mechanisms using circular statistics-based machine learning approach without the need for discharge data during inference, Hydrology Research, 2023, 54(10), 1181–1195, https://doi.org/10.2166/nh.2023.058.
  14. Lin Y., D. Wang, Y. Meng, W. Sun, J. Qiu, W. Shangguan, J. Cai, Y. Kim, and Y. Dai, Bias learning improves data driven models for streamflow prediction, Journal of Hydrology: Regional Studies, 2023, 50, 101557, https://doi.org/10.1016/j.ejrh.2023.101557.
  15. Pan Y., R. Yang, J. Qiu, J. Wang, and J. Wu, Forty-year spatio-temporal dynamics of agricultural climate suitability in China reveal shifted major crop production areas, CATENA, 2023, 226, 107073, https://doi.org/10.1016/j.catena.2023.107073.
  16. Wan L., V. A. Bento, Y. Qu, J. Qiu, H. Song, R. Zhang, X. Wu, F. Xu, J. Lu, and Q. Wang, Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018, Science of The Total Environment, 2023, 901, 166362, https://doi.org/10.1016/j.scitotenv.2023.166362 (ESI 高被引论文).
  17. Zhang R., Bento V. A., J. Qi, F. Xu, J. Wu, J. Qiu, J. Li, W. Shui, and Q. Wang*, The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018, Big Earth Data, 2023, 7(3), 860–885, https://doi.org/10.1080/20964471.2022.2148331.
  18. Weng X., B. Zhang, J. Zhu, D. Wang, and J. Qiu, Assessing land use and climate change impacts on soil erosion caused by water in China, Sustainability, 2023, 15, 7865, https://doi.org/10.3390/su15107865.
  19. Li Q., G. Shi, W. Shangguan, J. Li, L. Li, F. Huang, Y. Zhang, C. Wang, D. Wang, J. Qiu, X. Lu, and Y. Dai, A 1-km daily soil moisture dataset of China based on in-situ measurement using machine learning, Earth System Science Data, 2022, 14(2), 5267–5286, https://doi.org/10.5194/essd-14-5267-2022 (ESI 高被引论文).
  20. Yang Y., S. Huang, J. Qiu*, C. Liu, and W. Jiang, A surface water mapping framework combining optical and radar remote sensing and its application in China, Geocarto International, 2022, 37(27), 17547–17564, https://doi.org/10.1080/10106049.2022.2129836.
  21. Wu Z., J. Qiu*, W. T. Crow, D. Wang, Z. Wang, and X. Zhang, Investigating the efficacy of the SMAP downscaled soil moisture product for drought monitoring based on information theory, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 1604–1616, https://doi.org/10.1109/JSTARS.2021.3136565.
  22. Qiu J.*, W. T. Crow, S. Wang, J. Dong, Y. Li, M. Garcia, and W. Shangguan, Microwave-based soil moisture improves estimates of vegetation response to drought in China, Science of The Total Environment, 2022, 849, 157535, https://doi.org/10.1016/j.scitotenv.2022.157535.
  23. Dong J., W. T. Crow, X. Chen, N. Tangdamrongsub, M. Gao, S. Sun, J. Qiu, L. Wei, H. Gao, and Z. Duan, Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation, Remote Sensing of Environment, 2022, 283, 113299, https://doi.org/10.1016/j.rse.2022.113299.
  24. Wang P., X. Tong, J. Qiu, Y. Chen, S. Wu, T. O. Chan, J. Zhu, Z. Liu, H. Zhang, and M. Luo, Amplification effect of urbanization on atmospheric aridity over China under past global warming. Earth's Future, 2022, 10(5), e2021EF002335, https://doi.org/10.1029/2021EF002335.
  25. Liang L., Y. Feng, J. Wu, X. He, S. Liang, X. Jiang, G. de Oliveira, J. Qiu, and Z. Zeng, Evaluation of ECOSTRESS evapotranspiration estimates over heterogeneous landscapes in the continental US, Journal of Hydrology, 2022, 613, 128470, https://doi.org/10.1016/j.jhydrol.2022.128470.
  26. Qiu J.*, J. Dong, W. T. Crow, X. Zhang, R. H. Reichle, and G. J. M. De Lannoy, The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis, Hydrology and Earth System Sciences, 2021, 25(3), 1569–1586, https://doi.org/10.5194/hess-25-1569-2021.
  27. Zhang Z.,  D. Wang, J. Qiu, J. Zhu, and T. Wang, Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOS-x, Journal of Hydrometeorology, 2021, 22(10), 2767–2781, https://doi.org/10.1175/JHM-D-21-0019.1.
  28. Wang Z., T. Zhao, J. Qiu, and X. Zhao, Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands, GIScience & Remote Sensing, 2021, 58(1), 48–67, https://doi.org/10.1080/15481603.2020.1857123.
  29. Lin Y., D. Wang, G. Wang, J. Qiu, K. Long, Y. Du, H. Xie, Z. Wei, W. Shangguan, and Y. Dai, A hybrid deep learning algorithm and its application to streamflow prediction, Journal of Hydrology, 2021, 601, 126636, https://doi.org/10.1016/j.jhydrol.2021.126636.
  30. Qiu J.*, W. T. Crow, J. Dong, and G. S. Nearing, Model representation of the coupling between evapotranspiration and soil water content at different depths, Hydrology and Earth System Sciences, 2020, 24(2), 581–594, https://doi.org/10.5194/hess-24-581-2020.
  31. Wang Z.*, J. Qiu*, and K. Van Oost, A multi-isotope model for simulating soil organic carbon cycling in eroding landscapes (WATEM_C v1.0). Geoscientific Model Development, 2020, 13(10), 4977–4992, https://doi.org/10.5194/gmd-13-4977-2020.
  32. Qiu J.*, W. T. Crow, W. Wagner, and T. Zhao, Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation, 2019, 80, 47–57, https://doi.org/10.1016/j.jag.2019.03.015.
  33. Zhang Z., D. Wang, G. Wang, J. Qiu, and W. Liao, Use of SMAP soil moisture and fitting methods in improving GPM estimation in near real time. Remote Sensing, 2019, 11(3), 368, https://doi.org/10.3390/rs11030368.
  34. Yang Y., J. Qiu*, R. Zhang, S. Huang, S. Chen, H. Wang, J. Luo, and Y. Fan, Intercomparison of three two-source energy balance models for partitioning evaporation and transpiration in semiarid climates. Remote Sensing, 2018, 10(7), 1149, https://doi.org/10.3390/rs10071149.
  35. Yang Y., J. Qiu, H. Su, J. Tian, and R. Zhang, Estimation of surface soil moisture based on thermal remote sensing: Intercomparison of four methods. Journal of Infrared and Millimeter Waves, 2018, 37(4), 459–467, https://doi.org/10.11972/j.issn.1001-9014.2018.04.014.
  36. Zhang X., J. Qiu*, G. Leng, Y. Yang, Q. Gao, Y. Fan, and J. Luo, The potential utility of satellite soil moisture retrievals for detecting irrigation patterns in China. Water, 2018, 10(11), 1505, https://doi.org/10.3390/w10111505.
  37. Wang P., Y. Zhou, Z. Huo, L. Han, J. Qiu, Y. Tan, and D. Liu, Monitoring growth condition of spring maize in Northeast China using a process-based model. International Journal of Applied Earth Observation and Geoinformation, 2018, 66, 27–36, https://doi.org/10.1016/j.jag.2017.11.001.
  38. Yang Y., J. Qiu*, H. Su, Q. Bai, S. Liu, L. Li, Y. Yu, and Y. Huang, A one-source approach for estimating land surface heat fluxes using remotely sensed land surface temperature. Remote Sensing, 2017, 9(1), 43, https://doi.org/10.3390/rs9010043.
  39. Wang P., J. Qiu*, Z. Huo, M. Anderson, Y. Zhou, Y. Bai, T. Liu, S. Ren, R. Feng, and P. Chen, Temporal downscaling of crop coefficients for winter wheat in the North China Plain: A case study at the Gucheng agro-meteorological experimental station. Water, 2017, 9(3), 155, https://doi.org/10.3390/w9030155.
  40. Yan L., R. He, M. Kašanin-Grubin, G. Luo, H. Peng*, and J. Qiu*, The dynamic change of vegetation cover and associated driving forces in Nanxiong Basin, China. Sustainability, 2017, 9(3), 443, https://doi.org/10.3390/su9030443.
  41. Qiu J.*, W. T. Crow, and G. S. Nearing, The impact of vertical measurement depth on the information content of soil moisture for latent heat flux estimation. Journal of Hydrometeorology, 2016, 17(9), 2419–2430, https://doi.org/10.1175/JHM-D-16-0044.1.
  42. Qiu J.*, Q. Gao, S. Wang, and Z. Su, Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend. International Journal of Applied Earth Observation and Geoinformation, 2016, 48, 17–27, https://doi.org/10.1016/j.jag.2015.11.012.
  43. Zeng J., Z. Li, Q. Chen, H. Bi, J. Qiu, and P. Zou, Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sensing of Environment, 2015, 163, 91–110, https://doi.org/10.1016/j.rse.2015.03.008 (ESI 高被引论文).
  44. Wang S., S. Liu, X. Mo, B. Peng, J. Qiu, M. Li, C. Liu, Z. Wang, and P. Bauer-Gottwein, Evaluation of remotely sensed precipitation and its performance for streamflow simulations in basins of the southeast Tibetan Plateau. Journal of Hydrometeorology, 2015, 16, 2577–2594, https://doi.org/10.1175/JHM-D-14-0166.1.
  45. Zhang X., L. Jiang, X. Qiu, J. Qiu, J. Wang, and Y. Zhu, An improved method of delineating rectangular management zones using a semivariogram-based technique. Computers and Electronics in Agriculture, 2015, 121, 74–83, https://doi.org/10.1016/j.compag.2015.11.016.
  46. Qiu J., W. T. Crow, G. S. Nearing, X. Mo, and S. Liu, The impact of vertical measurement depth on the information content of soil moisture time series data. Geophysical Research Letters, 2014, 41(14), 4997–5004, https://doi.org/10.1002/2014GL060017. 
  47. Qiu J., W. T. Crow, X. Mo, and S. Liu, The impact of temporal auto-correlation mismatches on the assimilation of satellite-derived surface soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8), 3534–3542, https://doi.org/10.1109/JSTARS.2014.2349354.
  48. Qiu J., X. Mo, S. Liu, and Z. Lin, Exploring spatiotemporal patterns and physical controls of soil moisture at various spatial scales. Theoretical and Applied Climatology, 2014, 118, 159–171, https://doi.org/10.1007/s00704-013-1050-6.
  49. Qiu J., X. Mo, S. Liu, Z. Lin, L. Yang, X. Song, G. Zhang, V. Naeimi, and W. Wagner, Intercomparison of microwave remote sensing soil moisture data sets based on distributed eco-hydrological model simulation and in-situ measurements over the North China Plain. International Journal of Remote Sensing, 2013, 34(19), 6587–6610, https://doi.org/ 10.1080/01431161.2013.788799.
  50. Hu S., X. Mo, Z. Lin, and J. Qiu, Emergy assessment of a wheat-maize rotation system with different water assignments in the North China Plain. Environmental Management, 2010, 46(4), 643–657, https://doi.org/10.1007/s00267-010-9543-x.
  51. Qiu J., W. T. Crow, and W. Wagner, The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture. Abstract, The 5th Satellite Soil Moisture Validation and Application Workshop, October 2018 (Virginia, USA).
  52. Crow W. T., J. Qiu, and J. D. Bolten, Role of satellite-derived surface soil moisture retrievals for determining eco-hydrological feedback. Abstract H13L-01, AGU Fall Meeting, December 2013 (San Francisco, USA).
  53. Mo X., J. Qiu, S. Liu, and V. Naeimi, Estimating root-layer soil moisture for north China from multiple data sources, IAHS red book for Symposia JH01: GRACE, Remote Sensing and Ground-based Methods in Multi-Scale Hydrology, IAHS Publ. 2011, 343, 118–124, IUGG, July 2011 (Melbourne, Australia). 
  54. 王婵，蔡耀通，刘小平，江鑫，曾振中，邱建秀*，中国闭合林线的高程变化规律，遥感学报，2025.
  55. 石曼青，杨小玉，邱建秀*，罗明，王前锋，王大刚，中国植被光学厚度的时空变化规律及归因分析，地理学报，2025，80(5)，1212-1225.
  56. 杨天垚，邱建秀*，肖国安，综合植被光学特性和土壤湿度等信息的华北干旱监测与冬小麦估产研究，生态学报，2022，43(5)，1–12.
  57. 王宗侠，刘苏峡，邱建秀，莫兴国，中国1980—2019年1米土层贮水量的时空变化特征分析，地理研究，2022，41(11)，2979–2999.
  58. 杨永民，顾涛，吴迪，龙爱华，邱建秀，刘宏鑫，基于光学和雷达遥感信息的灌溉信号分析及灌溉面积提取方法研究—以华北平原灵寿县磁右灌区为例，水利学报，2022，53(9)，1039–1048.
  59. 蔡霁初，邱建秀*，王大刚，林凯荣，阳坤，曾庆峰，基于土壤水分和气象要素的林火预报研究，地理科学，2021，41(9)，1676–1686. 
  60. 吴泽棉，邱建秀*，刘苏峡，莫兴国，基于土壤水分的农业干旱监测研究进展，地理科学进展，2020，39(10)，1758–1769. 
  61. 罗家顺，邱建秀*，赵天杰，王大刚，基于Sentinel-1数据的黑河中游土壤水分反演，遥感技术与应用，2020，35(1)，23–32. 
  62. 范悦，邱建秀*，董建志，张小虎，王大刚，基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究，遥感技术与应用，2020，35(1)，85–96.
  63. 米蔚峰，邱建秀*，气象干旱指数在河北省的适用性分析，水文，2022，42(3)，53–60. 
  64. 王水寒，邱建秀*，王大刚，1960—2014 年广东省干旱时空演变特征，热带地理，2020，40(2)，357–366. 
  65. 刘苏峡，邱建秀，莫兴国，华北平原1951年至2006年风速变化特征分析，资源科学，2009，31(9)，1486–1492. 
  66. 莫兴国，刘苏峡，林忠辉，邱建秀，华北平原蒸散和GPP及其对气候波动的响应，地理学报，2011，66(5)，589–598. 
  67. 舒畅，刘苏峡，莫兴国，郑超磊，张守红，邱建秀，基于 RVA 法的河流生态流量谱系估算，生态环境学报，2010，19(5)，1151–1155. 
  68. 张守红，刘苏峡，舒畅，郑超磊，邱建秀，气候变化背景下黄土高原区植被侵蚀动力模型的思考，第七届中国水论坛论文集《水系统与水资源可持续管理》，中国北京，2009，66–70.

参编专著

  • Qiu J., et al. "Remote sensing of soil moisture in agroecosystems" (Chapter). In: Satellites for Field Crops: Advancing Agriculture through Earth Observation (Invited Contribution). Elsevier, 2025.

主要科研项目

  1. 国家自然科学基金原创探索计划项目，面向端到端陆面数据同化的双层任务深度学习与优化方法，2025-2027
  2. 广东省科技计划项目，粤北岩溶区森林生态系统碳水耦合野外观测研究站，2024-2026，项目参与
  3. 技术服务，顾及水文过程的次表层土壤水分遥感反演，2022-2023，项目主持
  4. 技术服务，引黄灌区土壤盐碱化遥感监测，2024-2025，项目主持
  5. 国家自然科学基金面上项目，基于多源遥感数据融合的高分辨率土壤水分反演及其干旱监测应用，2020-2023，项目主持
  6. 国家自然科学基金青年项目，考虑模型背景场与多源观测值自相关结构差异的根层土壤水分同化研究，2016-2018，项目主持
  7. 广东省自然科学基金，基于遥感与GIS技术的华南湿润红层区土壤侵蚀研究，2016-2019，项目主持
  8. 中山大学青年教师培育项目，融合多源遥感数据的土壤水分预报研究及其在南方湿润区的运用，2016-2018，项目主持
  9. 中山大学青年教师起步资助计划，微波遥感数据在土壤水分估算中的应用，2015-2016，项目主持
  10. 农业部农业信息技术重点实验室开放课题，华北地区根层土壤水分估算方法研究，2011-2012，第二参与
  11. 国家自然科学基金面上项目，无资料地区土壤水分估算方法研究，2010-2013，主要参与
  12. 国家自然科学基金重点项目，水循环过程不同尺度观测与对比实验研究—以白洋淀流域为例，2009-2012，主要参与

获奖情况

  1. 国家水利部《成熟适用水利科技成果推广清单》（2021年）
  2. 广东省水利学会科学技术二等奖（2024年）
  3. 首届空天信息科技期刊高影响力论文（2024年；全国共14篇入选）
  4. 中国高影响力数据论文（2024年地球科学领域；国内期刊共5篇入选）
  5. 中山大学逸仙学者

发明专利

  1. 一种基于动态双通道模型的土壤水分反演方法及相关装置 
  2. 一种基于土壤水分的旱涝急转事件识别方法、装置及设备
  3. 一种基于土壤水分的森林火灾预测方法、装置及存储介质

软件著作

  1. 土壤水资源集中度评估系统V1.0
  2. 基于动态双通道算法的土壤水分监测系统V1.0
  3. 基于变化检测方法的高分辨率土壤水分在线监测平台V1.0
  4. 中高分辨率土壤干旱监测系统V1.0
  5. 多同位素土壤有机碳循环系统V1.0
  6. 农田水分空间数据管理系统V1.0