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. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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.

14. 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.

15. 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

16. 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

17. 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

18. 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  

19. 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 

20. 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

21. 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

22. 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

23. Pei Zhifang^#; Fang Shibo^#; Yang Wunian; Wang Lei;, Wu Mingyuan; Zhang Qifei; Han Wei; Khoi Dao Nguyen; The Relationship between NDVI and climate factors at dierent monthly time scales: A case study of grasslands in Inner Mongolia, China (1982–2015), Sustainability, 2019, 11: 7243

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. 陈燕丽;房世波*;莫建飞;刘志平, 基于地基可见光图像的喀斯特典型植被长势监测.遥感技术与应用, 2023,02,518-526

26. 彭慧文;赵俊芳;谢鸿飞;房世波, 作物模型应用与遥感信息集成技术研究进展.中国农业气象, 2022,08,644-656

27. 余卫国;房世波;齐月;陈金华, ASTER数据地表温度产品精度评价.干旱气象, 2019,06,987-992+1011

28. 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

29. 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, 2020,03,326-332

30. 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

31. 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

32. 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

33. 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

出版书籍

1.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

2. 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,

3. 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

4. 韩秀珍, 任素玲,徐榕焓,房世波,刘清华, 武胜利, 耿维成 等著. 多源卫星遥感全球主要气象灾害定量监测关键技术研究. 气象出版社, 2024-02

5. 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

6. 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

软著专利等知识产权

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）

其它情况🐪，国内外主要合作单位

(1)复大大学

(2)中国科学院大气物理所

(3)中国科学院大学

(4) 中国科学院植物研究所植被与环境变化国家重点实验室

(5) 南京信息工程大学气象灾害预报预警与评估协同创新中心

(6) 成都理工大学地学空间信息技术重点实验室

(7) 美国佛罗里达大学气候研究所 (http://www.floridaclimateinstitute-uf.org/)

(8) 英国林肯大学农业与食品技术研究所 (http://www.lincoln.ac.uk/home/researchatlincoln/researchshowcase/futureoffoodandfarming/)

(9)中佛罗里达大学飓风暴雨研究院(https://stormwater.ucf.edu/)

(10) 加拿大萨斯喀彻温大学地理系（http://artsandscience.usask.ca/geography/）

https://www.ceh.ac.uk/）

  #以上信息由本人提供😧，更新时间：2024/09/25