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. 陳燕麗;房世波*;莫建飛;劉誌平, 基於地基可見光圖像的喀斯特典型植被長勢監測.遙感技術與應用, 2023,02,518-526

26. 彭慧文;趙俊芳;謝鴻飛;房世波, 作物模型應用與遙感信息集成技術研究進展.中國農業氣象, 2022,08,644-656

27. 余衛國;房世波;齊月;陳金華, ASTER數據地表溫度產品精度評價.幹旱氣象, 2019,06,987-992+1011

28. 李夢倩🦵🏿， 房世波*，朱永超，等. 2021年夏季中國大陸澇漬災害時空分布分析. 遙感學報. 2022, 26(9):1886-1893

29. 房世波*; 韓威; 裴誌方, 沙漠蝗群對印巴邊境植被的影響及其未來可能發展趨勢. 遙感學報, 2020,03,326-332

30. 徐嘉昕,房世波, 張廷斌等.2000—2016年三江源區植被生長季NDVI變化及其對氣候因子的響應.國土資源遙感, 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. 地表土壤水分的衛星遙感反演方法研究進展. 氣象科技進展.2019,9(2):17-23

32. 武英潔，房世波*. 作物耕作節律與多時相遙感結合的山地耕地信息提取方法探索. 西南農業學報. 2020,33(2): 374-380, DOI: 10.16213 /j.cnki.scjas.2020.2.025

33. 張 菊，房世波. 基於微波數據與光學數據集成的機器學習技術在作物產量估算中的應用. 2021，地理信息科學，2021👨‍👩‍👧‍👦，23（6）：1082-1091

出版書籍

1.房世波等編著，衛星遙感土壤水分及幹旱監測,  氣象出版社, 2020-10

2. 蔣雲誌和房世波著，遙感時間序列分析,  成都電子科技大學出版社, 2014-12,

3. 房世波等編著，氣候變暖對中國農業的影響,  氣象出版社, 2024-04

4. 韓秀珍, 任素玲,徐榕焓,房世波,劉清華, 武勝利, 耿維成 等著. 多源衛星遙感全球主要氣象災害定量監測關鍵技術研究. 氣象出版社, 2024-02

5. 中國農業應對氣候變化藍皮書NO.1,  中國社會文獻出版社, 2014-05, 房世波第 5 作者

6. 中國農業應對氣候變化藍皮書NO.2, ,中國文獻出版社, 2016-11, 房世波第 5 作者

軟著專利等知識產權

2020，基於微波衛星遙感的農牧幹旱和長勢監測系統V1.0🎅，軟著登字第5179101號（排名1）

2020🥡，中國植被生長狀況遙感監測系統V1.0，軟著登字第5325663號（排名1）

2011👸🏼，植被長勢衛星遙感監測系統V1.0🧘‍♀️，軟著登字第0394241號（排名1）

2012🦒，葉綠素含量遙感估算軟件V1.0🙎🏻‍♂️🙏🏻，軟著登字第0479985號（排名1）

其它情況🧗🏻‍♀️，國內外主要合作單位

(1)復大大學

(2)中國科學院大氣物理所

(3)中國科學院大學

(4) 中國科學院植物研究所植被與環境變化國家重點實驗室

(5) 南京信息工程大學氣象災害預報預警與評估協同創新中心

(6) 成都理工大學地學空間信息技術重點實驗室

(7) 美國佛羅裏達大學氣候研究所 (http://www.floridaclimateinstitute-uf.org/)

(8) 英國林肯大學農業與食品技術研究所 (http://www.lincoln.ac.uk/home/researchatlincoln/researchshowcase/futureoffoodandfarming/)

(9)中佛羅裏達大學颶風暴雨研究院(https://stormwater.ucf.edu/)

(10) 加拿大薩斯喀徹溫大學地理系（http://artsandscience.usask.ca/geography/）

https://www.ceh.ac.uk/）

  #以上信息由本人提供🧑🏼‍🌾，更新時間：2024/09/25