女，山东枣庄人，理学博士。现任中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室副研究员。
教育经历：
2016.09-2019.06 中国科学院大学，获地图学与地理信息系统专业理学博士学位
2013.09-2016.06 山东科技大学，获地图学与地理信息系统专业理学硕士学位
2009.09-2013.06 山东师范大学，获资地理科学专业理学学士学位
工作经历：
2019.07-2021.08 广东省科学院广州地理研究所，博士后
研究方向：
水循环遥感；土壤水分；机器学习；夜间灯光
主持项目：
1. 全国博士后创新人才支持计划：FY-3C 土壤水分时空序列多尺度融合方法研究(BX20200100)
2. 国家自然科学基金青年项目：多云雨地区微波遥感土壤水分高时空分辨率重构方法研究（42101475）
3. 国家对地观测科学数据中心开放基金：多尺度水文与地表辐射数据汇聚(NODAOP2020002)
4. 广东省科学院建设国内一流研究机构行动专项资金项目：基于SMAP的混合气候带高分辨率土壤水分数据融合方法研究(2020GDASYL-20200103006)
5. 广州市基础与应用基础项目：基于深度学习的FY-3C土壤水分数据降尺度融合算法研究(202102020676)
代表性论文：
[1]Liu, Y.; Zhou, Y.; Lv, N.; Tang, R.; Jing, W.; Zhou, C. Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe[J]. Journal of Hydrology. 2021, 1694, 125949. (SCI)
[2]Liu, Y.; Yao, L.; Jing, W.; Di, L.; Yang, J.; Li, Y. Comparison of two satellite-based soil moisture reconstruction algorithms: a case study in the state of Oklahoma, USA[J]. Journal of Hydrology. 2020, 1016, 125406. (SCI)
[3]Liu, Y.; Jing, W., Wang, Q., Xia, X. Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms[J]. Advances in Water Resources. 2020, 141, 103601. (SCI)
[4]Liu, Y.; Jing, W., Sun, S.; Wang, Q. Multi-Scale and Multi-Depth Validation of Soil Moisture From the China Land Data Assimilation System[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14,9913-9930. (SCI)
[5]Liu, Y.; Yang Y., Jing, W. Potential Applicability of SMAP in ECV Soil Moisture Merging: A Case Study in Europe[J]. IEEE Access. 2020, 8, 133114-133127. (SCI)
[6]Liu, Y.; Yang, Y.; Jing, W.; Yao, L.; Yue, X.; Zhao, X. A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL[J]. Remote Sens. 2017, 9, 777. (SCI)
[7]Liu, Y.; Yang, Y.; Jing, W.; Yue, X. Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China[J]. Remote Sens. 2018, 10, 31. (SCI)
[8]Liu, Y.; Yang, Y.; Yue, X. Evaluation of Satellite-Based Soil Moisture Products over Four Different Continental In-Situ Measurements[J]. Remote Sens. 2018, 10, 1161. (SCI)
[9]Liu, Y.; Xia, X.; Yao, L.; Jing, W.; Zhou, C.; Huang, W.; Li, Y.; Yang, J. Downscaling Satellite Retrieved Soil Moisture Using Regression Tree-based Machine Learning Algorithms Over Southwest France[J]. Earth and Space Science, 2020, 7, 001267. (SCI)
[10]Ji, T.; Li, G.; Liu, Y.; Liu, R.; Zhu, Y. Spatio‐temporal features of storm surge activity and its response to climate change in the southeastern coastal area of China in the past 60 years[J]. Journal of Geophysical Research: Atmospheres, e2020JD033234. (SCI)
[11]Xia, X.; Liu, Y.; Jing, W.; Yao, L. Assessment of Four Satellite-based Precipitation Products over the Pearl River Basin, China[J]. IEEE Access, doi: 10.1109/ACCESS.2021.3095239. (SCI)
[12]Xia, X.; Yao, L.; Lu, J.; Liu, Y.; Jing, W.; Li, Y. A Comparison Analysis of Causative Impact of PM2.5 on Acute Exacerbation of Chronic Obstructive Pulmonary Disease (COPD) in Two Typical Cities in China[J]. Atmosphere, 2021, 12(8): 970. (SCI)
[13]Jing, W.; Yao, L.; Zhao, X.; Zhang, P.; Liu, Y.; Xia, X.et al. Understanding terrestrial water storage declining trends in the Yellow River Basin[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(23): 12963-12984. (SCI)
[14]Jing, W., Di, L., Zhao, X., Yao, L., Xia, X., Liu, Y., Yang, J., Li, Y., Zhou, C. A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations[J]. Advances in Water Resources. 2020, 143, 103683. (SCI)
[15]Ding, X.; Li, Y.; Yang, J.; Li, H.; Liu, L.; Liu, Y.; Zhang, C. An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification. Remote Sens. 2021, 13,2445. (SCI)
[16]Sun, M., Deng, Y., Li, M., Jiang, H., Huang, H., Liao, W., Liu, Y., Yang, Ji., & Li, Y. Extraction and analysis of blue steel roof information based on CNN using Gaofen-2 imageries[J]. Sensor. 2020, 11(8), 1211. (SCI)
[17]Yao, L.; Lu, J.; Xia, X.; Jing, W.; Liu, Y. Evaluation of the ERA5 Sea Surface Temperature Around the Pacific and the Atlantic[J]. IEEE Access. 2021, 9: 12067-12073. (SCI)
[18]Wang, C.; Wang, L.; Wang, D.; Li, D.; Zhou, C.; Jiang, H.; Zheng, Q.; Chen, S.; Jia, K.; Liu, Y.; Yang, J.; Zhou, X.; Li, Y. Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries[J]. Geoscientific Model Development. 2021,14, 11, 6833-6846. (SCI)
[19]Xin, Y.; Lu, N.; Jiang, H.; Liu, Y.; Yao, L. Performance of ERA5 Reanalysis Precipitation Products in the Guangdong-Hong Kong-Macao Greater Bay Area, China[J]. Journal of Hydrology. 2021, 126791. (SCI)
[20]王卓颖,刘杨晓月.全球土壤水分产品融合数据集(2015–2019)[J].全球变化数据学报(中英文),2020,4(04):315-324+315-324.
[21]王卓颖,刘杨晓月,杨骥,刘昭华.国产土壤水分产品多尺度精度验证与评价——以青藏高原那曲地区为例[J].中国农业气象, 2021, 21(09):1-15.
[22]刘杨晓月,季民. 海洋溢油应急响应决策支持系统设计与实现[J].地理信息世界, 2015, 22(03):68-72.
[23]刘杨晓月,季民. 基于NetCDF数据的流线生成算法简析[J].北京测绘, 2015(03):38-42.
[24]刘杨晓月,季民,邱兆勇. 矿山沉陷监测克里金插值分析研究[J].山东煤炭科技, 2015(04) :154-156+159.
[25]刘杨晓月,季民,马俊瑞. 基于PW图的溢油鉴别分析方法研究[J].海岸工程, 2015, 34(01):69-76.
[26]王重洋,周成虎,陈水森,谢一春,李丹,杨骥,周霞,李勇,王丹妮,刘杨晓月. 河口最大浑浊带研究的回顾与展望[J].科学通报,2020, 38(09): 1-34.
[27]王树祥,韩留生,杨骥,李勇,赵倩,刘杨晓月,吴昊. 一种改进的融合多指标荒漠化等级分类方法[J].测绘通报,2021, 04:8-12.
授权发明专利：
[1]一种基于随机森林算法的陆地水储量预测方法及设备（ZL201910904058.1）
[2]基于极端梯度提升的岛礁浅海水深预测方法（ZL201910945145.1）
[3]一种土壤水分数据获取方法、系统、存储介质及设备（ZL201910905363.2）
[4]基于极端梯度提升算法的植被指数预测方法、系统及设备（ZL201910905212.7）
[5]基于随机森林的岛礁浅海水深预测方法（ZL201910943217.9）
[6]一种基于极端梯度算法的陆地水储量预测方法及设备（ZL201910904059.6）
[7]一种河口浑浊带识别方法和识别系统（ZL20201 466246.X）
[8]基于神经网络算法的植被指数预测方法、系统及设备（201910905197.6）
[9]一种基于神经网络算法的陆地水储量预测方法及设备（201910904368.3）
联系方式：
通信地址：北京市朝阳区大屯路甲11号中国科学院地理科学与资源研究所
邮 编：100101
E–mail: lyxy@lreis.ac.cn
