【个人信息】

房世波 ，中国气象局，研究员

中国科学院大学，新葡萄8883官网，中国气象科学研究院，博士生导师，硕士生导师

政治面貌：中国共产党党员

出生年月：1974年01月

电子邮件：sbfang0110@163.com

【个人简介】

姓  名：房世波，博士，研究员

领域：卫星气象遥感、生态与农业气象遥感

单位：中国气象科学研究院

出生年月：1974年1月，  性 别：男

个人主页：http://people.ucas.ac.cn/~0039464

主要从事卫星气象遥感、生态与农业气象遥感研究。主持在研国家重点研发项目1项，曾主持结题国家自然科学基金国际合作重点项目和国家国际科技合作专项等重点项目，累计主持国家级项8项，其中国家自然科学基金项目4项。发表第一或通讯作者论文80余篇，50余篇被SCI收录；主编著作3部，参编出版著作3部，参编译著 1部，排名第1获得省部级奖励3项。

在灾害遥感和粮食安全方面有多年积累,为中国科协“全球气象灾害与粮食安全团队”的团队首席，为辽宁生态与农业气象研究院 “生态与农业气象大模型”团队的团队首席，同时也是中国气象科学研究院与国家卫星气象中心联合团队 “农业气象遥感产品研发”的团队组长，现任世界气象组织（WMO）农业气象专家委员会委员和专家, 为江西省委、省政府特聘专家。主要社会兼职：现为中国遥感应用协会生态气象遥感专委会主任委员，国际数字地球学会中国国家委员会委员、国际数字地球学会中国国家委员会数字减灾专委会副主任委员等。

【研究方向】

1. 卫星气象学

2. 农业气象灾害卫星遥感

3. 生态与农业气象大模型

   【教育经历】

【工作经历】

【主持的在研项目】

1. 国家重点研发项目 (2023年度项目) “气候变化背景下中美高耗水作物面积调减”  （2023YFE0122200，2023.12-2026.11），150万元

2. 英国政府牛顿基金项目CSSP-China (气候科学支持服务伙伴关系中国项目) “VEgetation near Real time Detection And moNiToring for China (VERDANT) [CHN19/1 (Vegetation/crop monitoring) P107722]. （202004-202106）”  （中方主持），22万英镑（约合200万元）

【近5年主持完成的主要项目】

1.主持结题国家自然科学基金重点国际合作项目（牛顿基金）：中英国际合作项目“基于高分雷达遥感和快中子的水分传感技术，发展近实时的高时空分辨率的区域土壤湿度监测方法”(61661136005，2016-2019), 中方经费300万元, 英方经费100万英镑.

2.主持在研国家自然科学基金面上项目“华北平原冬小麦种植面积调减和节水增效研究”）(42075193，202101-202412：),  直接经费58万元

3.主持在研国家重点研发计划项目“全球气象卫星遥感动态监测、分析技术及定量应用方法及平台研究”（2018YFC1505600）专题 “多源卫星遥感全球干旱的定量监测关键技术研究”，50万

4.主持在研科技部公益性院所科研业务费重点项目“基于卫星遥感的农业干旱监测关键技术” (2019Z010，201901-202112：)，经费120万元

5.主持在研中国气象局风云卫星应用先行计划项目 FY-3E微光成像和GNOSⅡ反射信号监测火点、洪涝和干旱（FY-APP-2021.03010）, 60万元

【发表论文】

1. Y. Yu, S. Fang*, W. Zhuo and J. Han, "A Fast and Easy Way to Produce a 1-Km All-Weather Land Surface Temperature Dataset for China Utilizing More Ground-Based Data,  IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024, Art no. 5002016, doi: 10.1109/TGRS.2024.3368707.

2. Jiahao Han, Shibo Fang * , Qianchuan Mi, et al. A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations. Science of the Total Environment. 2024,169992,https://doi.org/10.1016/j.scitotenv.2024.169992.

3. Wang, L.; Han, X.; Fang, S.; Xiao, F. Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China. Remote Sens. 2024, 16, 1363. https://doi.org/10.3390/rs16081363

4. Jilin Yang，,Jinwei Dong , Luo Liu, Miaomiao Zhao，… Shibo Fang , Yong Pang. A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2023,2020:610-636

5. Yanru Yu, Shibo Fang * and Wen Zhuo. Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector. Remote Sens. 2023, 15, 2814. https://doi.org/10.3390/rs15112814

6. W Zhuo, S Fang*, X Gao, L Wang, D Wu, S Fu, Q Wu, J Huang. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation 106 …, 2022.https://doi.org/10.1016/j.jag.2021.102668

7. L Wang, S Fang*, Z Pei, D Wu, Y Zhu, W Zhuo .Developing machine learning models with multisource inputs for improved land surface soil moisture in China. Computers and Electronics in Agriculture 192, 106623, 2022.https://doi.org/10.1016/j.compag.2021.106623

8. DN Khoi, PT Loi, NTT Trang, ND Vuong, S Fang, PTT Nhi, The effects of climate variability and land-use change on streamflow and nutrient loadings in the Sesan, Sekong, and Srepok (3S) River Basin of the Lower Mekong Basin. Environmental Science and Pollution Research 29 (5), 7117-7126, 2022 10.1007/s11356-021-16235-w

9. J Zhang, S Fang*, H Liu. Estimation of alpine grassland above-ground biomass and its response to climate on the Qinghai-Tibet Plateau during 2001 to 2019. Global Ecology and Conservation 35, e02065, 2022.https://doi.org/10.1016/j.gecco.2022.e02065

10. Y Wu, S Fang*, Y Xu, L Wang, X Li, Z Pei, D Wu. Analyzing the probability of acquiring cloud-free imagery in China with AVHRR cloud mask data. Atmosphere 12 (2), 214, 2021 10.3390/atmos12020214

11. Xuan Li, Shibo Fang*,et al,. 2021，Risk Analysis of Wheat Yield Losses at the County Level in Mainland.  Frontiers in Environmental Science  | (2021) 9| doi: 10.3389/fenvs.2021.642340

12. D Wu, S Fang*, X Tong, L Wang, W Zhuo, Z Pei, Y Wu, J Zhang, M Li, Analysis of variation in reference evapotranspiration and its driving factors in mainland China from 1960 to 2016. Environmental Research Letters  2021, 10.1088/1748-9326/abf687

13. D He, S Fang*, et al. Contrasting yield responses of winter and spring wheat to temperature rise in China , Environmental Research Letters 2020   15   12         124038   10.1088/1748-9326/abc71a

14. Wu, D et al, Fang, SB*. A new agricultural drought index for monitoring the water stress of winter wheat. Agricultural Water Management,  2021, 244 , DOI: 10.1016/j.agwat.2020.106599 ,

15. Dong Wu, Shibo Fang*, Xuan Li, et al. Spatial-temporal variation in irrigation water requirement for the winter wheat-summer maize rotation system since the 1980s on the North China Plain. Agricultural Water Management 214 (2019) 78–86

16. Wang Lei; Fang Shibo^*; Pei Zhifang; Zhu Yongchao; Khoi Dao Nguyen; Han Wei; Using FengYun-3C VSM data and multivariate models to estimate land surface soil moisture, Remote Sensing, 2020, 12: 1038.

17. Wang Lei; Wang Pengxin; Liang Shunlin; Zhu Yongchao; Khan Jahangir; Fang Shibo^*; Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model, Computers and Electronics in Agriculture, 2020, doi.org/10.1016/j.compag.2020.105238.

18. X Li, S Fang*, Y Zhu, D Wu, Risk analysis of wheat yield losses at county level in mainland ChinaX. Frontiers in Environmental Science 9, 141  2021

19. Wang, L; Zhuo, W ; Pei, ZF; Tong, XY; Han, W; Fang, SB*.Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss. REMOTE SENSING,2020,13 (4)  DOI: 10.3390/rs13040680  

20. Xuan Li, Shibo Fang*, Dong Wu, Yongchao Zhu & Yingjie Wu . Risk analysis of maize yield losses in mainland China at the county level. Scientific Reports ,2020,10, 10684

21. Zhifang Pei, Shibo Fang *,Lei Wang 1 and Wunian Yang. Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China. Water 2020, 12, 1925; doi:10.3390/w12071925

22. Zechao Bai  Shibo Fang*, Jian Gao, Yuan Zhang, Guowang Jin, Shuqing Wang, Yongchao Zhu & Jiaxin Xu.Could Vegetation Index be Derivefrom Synthetic Aperture Radar?  The Linear Relationship betweenI nterferometric Coherence and NDVI. Scientific Reports 2020，10:6749 | https://doi.org/10.1038/s41598-020-63560-0

23. Jiaxin Xu, Shibo Fang*, Xuan Li and Zichun Jiang Indication of the Two Linear Correlation Methods Between Vegetation Index and Climatic Factors:An Example in the Three River-Headwater Region of China During 2000–2016. Atmosphere 2020, 11, 606; doi:10.3390/atmos11060606

24. Yongchao Zhu, Xuan Li, Simon Pearson, …. Shibo Fang*. Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China. Water 2019, 11, 248; doi:10.3390/w11020248

25. Yongchao Zhu, Yongchun Zheng, Shibo Fang*, Yongliao Zou, Simon Pearson. Analysis of the brightness temperature features of the lunar surface using 37 GHz channel data from the Chang’E-2 microwave radiometer. Advances in Space Research, (2018) , https://doi.org/10.1016/j.asr.2018.10.014

26. Yang Song, Shibo Fang *，Zaiqiang Yang, Shuanghe Shen. Drought indices based on MODIS data, compared over a whole maize growth period in Songliao Plain, China. Journal of Applied remote sensing, 2018, 12(4),046003 (2018), doi: 10.1117/1.JRS.12.046003.

27. Yue Qi, Shibo Fang*, He Yin, Wenzuo Zhou, et al. Measuring the soil water retention capacity with an integrated vegetation and drought index in southwest China. Journal of Applied remote sensing, 2018, 12(4), 046001 (2018), doi: 10.1117/1.JRS.12.046001.

28. Fang Shibo, Cammarano Davide, Petropoulos George. Recent Advances in Earth Observation Technologies for Agrometeorology and Agroclimatology. Journal of Applied Remote Sensing, 2018,12(2), 022201 (2018), doi: 10.1117/1.JRS.12.022201.

29. Crowther, T.W., Rowe, C.W., Wieder, W.R., Carey, J.C., Machmuller, M.B., Todd-Brown, K.E.O., Snoek, L.B., Fang, S., Zhou, G., Allison, S.D., et al. Quantifying Global Soil C Losses in Response to Warming. Nature. 2016,540: 104-108 doi:10.1038/nature20150.

30. Fang Shibo, Yu Weiguo , Qi Yue.. Spectra and vegetation index variations in moss soil crust in different seasons, and in wet and dry conditions. International Journal of Applied Earth Observation and Geoinformation. 2015,38: 261-266  

31. FANG Shi-Bo, ZHANG Xin-Shi. Control of vegetation distribution: climate, geological substrate and geomorphic factors. A case study of grassland in Ordos, Inner Mongolia, China. Canadian Journal of Remote Sensing. 2013, 39:(2): 167-174

32. SHEN Bin, FANG Shi-Bo*. Vegetation Coverage Changes and Their Response to Meteorological Variables from Year 2000 to 2009 in Naqu, Tibet, China. Canadian Journal of Remote Sensing. 2014, 40:(1)：67-74

33. Shibo Fang, Yue Qi. Guojun Han, et al. Change in temperature extremes and its correlation with mean temperature in mainland China from 1960 to 2010. International Journal of Climatology. 2016, DOI: 10.1002/joc.4965.

34. Yu-heJi, Ke Guo, Shi-bo Fang*, et al. Long-term growth of temperate broadleaved forests no longer benefits soil C accumulation. Scientific Reports. 2017, doi:10.1038/srep42328(IF 5.228)

35. Fang Shibo, Cammarano Davide, Zhou Guangsheng. Effects of increased day and night temperature with supplemental infrared heating on winter wheat growth in North China.European Journal of Agronomy. 2015,64:67-77

36. FANG Shi-bo, ZHANG Xin-shi. Impact of Moss Soil Crust on vegetation Indexes Interpretation. Spectroscopy and Spectral Analysis.2011,31(3): 780-783

37. Tan KaiYan, Fang Shi-Bo,Zhou Guangsheng, et al. Responses of irrigated winter wheat yield in North China to increased temperature and elevated CO 2 concentration. Journal of Meteorological Research. 2015, 29, 691-702.

38. Fang Shibo, Hua Su, Wei Liu, et al.  Infrared Warming Reduced Winter Wheat Yields and Some Physiological Parameters, Which Were Mitigated by Irrigation and Worsened by Delayed Sowing. PLoS ONE, 2013, 8(7): e67518. doi:10.1371/journal.pone.00675167-74

39. Fang Shi-Bo, Hao Hu, Wan-Chun Sun, Jian-Jun Pan. Spatial Variations of Heavy Metals in the Soils of Vegetable-Growing Land along Urban-Rural Gradient of Nanjing, China. Int. J. Environ. Res. Public Health. 2011, 8, 1805-1816

40. Fang Shi-Bo, Tan KaiYan, Ren Sanxue, Zhang Xinshi. Fields Experiments in North China Show No Decrease in Winter Wheat Yields with Night Temperature Increased by 2.0-2.5℃.. SCIENCE CHINA Earth Science. 2012, 55: 1021-1027

41. Fang Shi-Bo, Wu-Nian Yang, Xin-Shi Zhang. Assessment of farmland afforestation in the upstream Yangtze River, China. Outlook on Agriculture. 2012,41(2):97-101

42. Fang Shibo , Sanxue Ren and Kaiyan Tan. Responses of winter wheat to higher night temperature in spring as compared within whole growth period by controlled experiments in North China. Journal of Food, Agriculture & Environment. 2013,11 (1): 777-781

43. Qi Yue, Fang Shibo*, Zhou Wenzuo. Correlative analysis between the changes of surface solar radiation and its relationship with air pollution, as well as meteorological factor in eastern and western China in recent 50 years. 2015, Acta Physica Sinica. 64(8) : http://dx.doi.org/10.7498/aps.64.089201

44. Hua-Jie Liu, Shi-Bo Fang*, Si-Wa Liu, Liang-Cheng Zhao, Xiu-ping Guo. Lichen elemental composition distinguishes anthropogenic emissions from dust storm input and differs among species: evidence from Xilinhot, Inner Mongolia, China. Scientific Reports. 2016, doi:10.1038/srep34694

45. Hua-Jie Liu, Liang-Cheng Zhao, Shi-Bo Fang*, Si-Wa Liu, Jian-Sen Hu, Lei Wang, Xiao-Di Liu, Qing-Feng Wu. Use of the lichen Xanthoria manchurica in monitoring atmospheric elemental deposition in the Taihang Mountains, Hebei, China. Scientific Reports. 2016, doi: 10.1038/srep23456.

46. Qi Yue, Fang Shibo*, Zhou Wenzuo, et al. Correlative Analysis of the Relationship between Changes in Surface Solar Radiation and Haze pollution (Atmospheric Turbidity Index) in Beijing from 1961 to 2011. Global NEST Journal. 2016 ,18(1): 180-184.

47. 房世波, 谭凯炎, 任三学. 气候变暖对冬小麦生长和产量影响的实验研究. 中国科学D辑 : 地球科学. 2012, 42 (7): 1069-1075沈斌, 房世波*, 余卫国. NDVI与气候因子关系在不同时间尺度上的结果差异. 遥感学报. 2016, 20(3): 481–490

48. 房世波，韩威，裴志方. 沙漠蝗群对印巴边境植被的影响及其未来可能发展趋势. 遥感学报. 2020, 24(3):326-332

49. 李梦倩， 房世波*，朱永超，等. 2021年夏季中国大陆涝渍灾害时空分布分析. 遥感学报. 2022, 26(9):1886-1893

50. 徐嘉昕,房世波*, 张廷斌等.2000—2016年三江源区植被生长季NDVI变化及其对气候因子的响应.国土资源遥感, 2020，32( 1) : 237 － 246, doi: 10.6046 /gtzyyg.2020.01.32

51. 宋扬，房世波*，柯丽娜. 基于MODIS数据的农业干旱遥感指数对比和应用. 国土资源遥感，2017，29( 2) : 215 -220.

52. 齐月;房世波*;周文佐. 近50年来中国东、西部地面太阳辐射变化及其与大气环境变化的关系.物理学报.2015, 64 (8):089201 http://dx.doi.org/10.7498/aps.64.089201.

53. 谭凯炎, 房世波*,  任三学. 增温对华北冬小麦生产影响的试验研究. 气象学报. 2012,70(4): 902-908

54. 房世波, 齐月, 韩国军, 周广胜. 1960—2010年中国主要麦区冬春气象干旱趋势及其可能影响. 中国农业科学.2014, 47(8):1754-1763

55. 齐月,房世波*,周文佐. 近50 年来中国地面太阳辐射变化及其空间分布研究. 生态学报 2014,34(24): 7444-7453

56. 房世波, 阳晶晶, 周广胜.30年来我国农业气象灾害的变化趋势和分布特征. 自然灾害学报. 2011,20(5):69-73

57. 房世波. 分离趋势产量和气候产量的方法探讨. 自然灾害学报. 2011,20(6):13-18

58. 沈斌, 房世波*, 高西宁. 基于MODIS的雪情监测及其对农业的影响评估. 中国农业气象. 2011,32(1):129-133

59. 房世波, 沈斌, 谭凯炎. 大气[CO2]和温度升高对农作物的影响.中国生态农业学报,2010,18(5):1116-1124

60. 房世波,韩国军,张新时,周广胜. 气候变化对农业生产的影响及其适应. 气象科技进展.2011,1(2):15-18

61. 谭凯炎, 房世波, 任三学. 灌溉农田土壤湿度的时空变化特征.中国农业气象,2010,03:423-426

62. 房世波, 谭凯炎, 任三学.夜间增温对冬小麦生长的影响. 中国农业科学.2010, 43(15):3251-3258

63. 宋扬, 房世波, 卫亚星. 农业干旱遥感监测指数及其适用性研究进展. 科技导报2016，34（5）: 45-52; doi: 10.3981/j.issn.1000-7857.2016.05.004

64. 赵俊芳,房世波*, 郭建平. 受蚜虫危害与干旱胁迫的冬小麦高光谱判别. 国土资源遥感. 2013,25(3):153-158

65. 徐嘉昕, 李璇, 朱永超, 房世波*，等. 地表土壤水分的卫星遥感反演方法研究进展. 气象科技进展.2019,9(2):17-23

66. 韩语轩，房世波*，梁瀚月，周莉，周广胜．基于减产概率的辽宁水稻灾害风险区划．生态学报，2017，37(23)：8077．8088．

67. 房世波. 气候变化背景下,高寒草地牧草长势监测和牧草产量预报系统的研制.中国科技成果.2012, 10:18-20

68. 武英洁，房世波*. 作物耕作节律与多时相遥感结合的山地耕地信息提取方法探索. 西南农业学报. 2020,33(2): 374-380, DOI: 10.16213 /j.cnki.scjas.2020.2.025

69. 张 菊，房世波.基于微波数据与光学数据集成的机器学习技术在作物产量估算中的应用. 2021，地理信息科学，2021，23（6）：1082-1091

【授权国家发明专利】

2020，基于微波卫星遥感的农牧干旱和长势监测系统V1.0，软著登字第5179101号（排名1）

2020，中国植被生长状况遥感监测系统V1.0，软著登字第5325663号（排名1）

2011，植被长势卫星遥感监测系统V1.0，软著登字第0394241号（排名1）

2012，叶绿素含量遥感估算软件V1.0，软著登字第0479985号（排名1）

【出版专著】

（1）房世波等编著，气候变暖对中国农业的影响, , 气象出版社, 2024-04

（2）房世波等编著，卫星遥感土壤水分及干旱监测, , 气象出版社, 2020-10

（3）蒋云志和房世波著，遥感时间序列分析, , 成都电子科技大学出版社, 2014-12,

（4） 中国农业应对气候变化蓝皮书NO.1,  中国社会文献出版社, 2014-05, 房世波第 5 作者

（5） 中国农业应对气候变化蓝皮书NO.2, , 中国文献出版社, 2016-11, 房世波第 5 作者

【获得的省部级以上奖励】

2022，中国气象局“十三五”以来的“优秀”科技成果, 一等奖, （排名1）

2020，中国测绘学会，中国测绘科学技术奖，二等奖（排名1）

2021，获得：中国产学研促进会，中国产学研合作创新成果奖，一等奖（排名1）

2020，通讯作者论文获“青年科学家杰出论文奖”，该奖项与“赵九章奖”同属于国际科学理事会国际空间研究委员会的八个奖项之一。论文发表于Advances in Space Research, 2019, 63: 750-765