资源区划所-毛克彪导师介绍

更新于 2021-12-26 导师主页
毛克彪 研究员 硕,博士生导师
资源区划所
★农业遥感
农业定量遥感
maokebiao@126.com

硕士招生专业

1
★农业遥感
2022
1
学术型硕士
农业资源环境遥感
2
★农业遥感
2021
1
专业学位硕士
农业定量遥感

科研项目

研究成果

主要取得了如下创新:(1)在晴空条件下,通过利用近红外波段估算大气水汽含量,克服了以往算法需要从气象站点获得水汽的困难,提出了地表温度和发射率分步反演的新劈窗算法,简化了反演过程,提高了反演精度;针对多热红外波段数据,通过建立邻近波段发射率之间的关系,克服方程不足的困难,提出了同时反演地表温度和发射率的多波段反演算法,通过利用深度学习神经网络与辐射传输方程结合解决了地表温度和发射率反演及分离的难题,大大提高了反演精度和算法适用性;(2)首次提出利用先验知识和人工智能方法直接从遥感数据大面积估算近地表空气温度反演方法,提高了空气温度反演的精度和时效性;(3)通过利用同极化不同频率微波指数克服粗糙度的影响,建立了标准极化微波指数模型,提高了土壤水分反演精度;发明了一套利用 GPS 地面反射信号估算土壤水分的仪器和方法;提出利用卡曼滤波迭代优化方法估算窄波段、宽波段发射率及大气水汽含量,提高了反演精度;(4)提出了全天候的被动微波数据的地表温度反演方法,并利用深度学习进行优化计算,提高了有云情况下热红外无法准确反演地表温度的精度;(5)通过对全球数据分析发现近年来大气水汽呈减少趋势,发现全球北高纬植被和水汽同时增加,赤道地区植被和水汽同时减少,从而首次得出全球水汽分布对植被大时空分布方面是起主要决定性作用因素之一的结论;(6)通过对全球温度,二氧化碳,植被和水汽数据分析首次提出地球温度变化是由地球在太阳系中的轨道能级位置决定的理论;地球上生态系统(植被物种分布)大的时空分布受大气水汽时空分布影响很大,同时也是由天体运行轨道位置决定的结论;并在此基础上提出了建立以开普勒定律和万有引力定律以及广义相对论为基础的全球气候变化和生态系统理论思想;(7)为保护国家粮食安全,通过灾害时空变化分析和预测,提出了新时期的“藏粮于民与粮食节约行动”建议和以及推动“乡村振兴民间计划和三藏战略”得到社会各界人士认可。

 

发表专著和论文(*标记为通讯作者论文,#为并列第一作者):

1.         毛克彪,基于热红外和微波数据的地表温度和土壤水分反演算法研究,  中国农业科学技术出版社, 2007.12(专著). ISBN:9787802334687

2.         毛克彪,农业气象遥感关键参数反演算法及应用研究,中国农业科学技术出版社, 2017.10(专著). ISBN: 9787511632685

3.         Wang, H, Kebiao Mao#*, Yuan Zijin, Shi, J, Cao, M., Qin, Z., Duan, S., Tang, B., A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning, Remote Sensing of Environment, 2021, 265, 1-19.

4.         Meng, X., Kebiao Mao#*, Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., and Guo, Z.: A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 2021, 13, 3239–3261. https://doi.org/10.5194/essd-13-3239-2021.

5.         Yibo Yan, Kebiao Mao#*, Xinyi Shen, Mengmeng Cao, Tongren Xu, Zhonghua Guo, Qing Bao, Evaluation of the influence of ENSO on tropical vegetation in long time series using a new indicator, Ecological Indicators, 2021,129,1-22. https://doi.org/10.1016/j.ecolind.2021.107872.

6.         Cao, M., Kebiao Mao#*, Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan, Z., A new global gridded sea surface temperature data product based on multisource data, Earth Syst. Sci. Data, 13, 2111–2134, https://doi.org/10.5194/essd-13-2111-2021, 2021.

7.         Mengmeng Cao, Kebiao Mao#*, Xinyi Shen, Tongren Xu , Yibo Yan, Zijin Yuan, Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series, Remote Sensing, 2020, 12, 3622, 1-31, doi:10.3390/rs12213622.

8.         Zhao, B., Kebiao Mao#*, Y. Cai, J. Shi, Z. Li, Z. Qin, and X. Meng, A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 – 2017, Earth Syst. Sci. Data, 2020, 12, 2555–2577.

9.         Nusseiba Noureldeen, Kebiao Mao*, Alnail Mohmed, Zijin Yuan, Yanying Yang, Spatio-Temporal Drought Assessment over Sahelian Countries from 1985 to 2015, Journal of Meteorological Research, 2020, 34, 760-774.

10.     Yan, Y.B., Kebiao Mao#*, Shi, J., Piao, S.L., Shen, X.Y., Dozier, J., Liu, Y., Ren, H.L, Bao, Q., Driving forces of land surface temperature anomalous changes in North America in 2002–2018, Scientific Reports, 6931(10), 1-13, https://doi.org/10.1038/s41598-020-63701-5, 2020.

11.     Xinlei He, Tongren Xu, Youlong Xia, Sayed M. Bateni, Zhixia Guo, Shaomin Liu, Kebiao Mao, Yuan Zhang, Huaize Feng, Jingxue Zhao, A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation, Remote Sens. 2020, 12, 878; doi:10.3390/rs12050878.

12.     Feifan Ge, Tao Yan, Lu Zhou, Yuelin Jiang, Wei Li, Yufen Fan, Yishu Wang, Kebiao Mao, Wenge Wu, Impact of sea ice decline in the Arctic Ocean on the number of extreme low-temperature days over China, Int J Climatol. 2020, 40:1421–1434.

13.     NourEldeen, N., Kebiao Mao#*, Z. Yuan, X. Shen, T. Xu, and Z. Qin, Analysis of the spatiotemporal change in land surface temperature for a long-term sequence in Africa (2003–2017). Remote Sensing, 2020, 12, 488. doi: 10.3390/rs12030488.

14.     Jingxue Zhao, Tongren Xu*, Jingfeng Xiao, Shaomin Liu, Kebiao Mao, Lisheng Song, Yunjun Yao, Xinlei He, Huaize Feng,Responses of Water Use Efficiency to Drought in Southwest China,Remote Sens. 2020, 12(1), 199. https://doi.org/10.3390/ rs12010199.

15.     Hanwang, Kebiao Mao#*, Fengyun Mu, Jiancheng Shi, Jun Yang, Zhaoliang Li, Zhihao Qin, A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 data, Remote Sensing, 2019, 11, 20183, 1-25.

16.     Xiangjin Meng, Kebiao Mao#*, Fei Meng, Xinyi Shen, Tongren Xu, Mengmeng Cao, Long-term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products, Sensors, 2019, 19, 3527, 1-18.

17.     Jiancan Tan, Nusseiba NourEldeen, Kebiao Mao*, Jiancheng Shi, Zhaoliang Li, Tongren Xu,Zijin Yuan, Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China, Sensors, 2019, 19, 2987:1-20; doi:10.3390/s19132987. 

18.     Kebiao Mao*, Zijin Yuan, Zhiyuan Zuo, Tongren Xu, Xinyi Shen, Chunyu Gao, Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012, Chinese Geographical Science, 2019,29,2, 306–315.

19.     Jingpeng Guo, Kebiao Mao*, Yinghui Zhao, Zhong Lu, and Xiaoping Lu, Impact of Climate on Food Security in Mainland China: A New Perspective Based on Characteristics of Major Agricultural Natural Disasters and Grain Loss, Sustainability 2019, 11, 869, 1-25.

20.     Tongren Xu, Xinlei He, Saye M. Bateni, Thomas Aulignec, Shaomin Liu, Ziwei Xu, Ji Zhou, Kebiao Mao, Mapping regional turbulent heat fluxes variational assimilation of land surface temperature data from polar orbiting satellites, Remote Sensing of Environment,2019,221,444-461.

21.     Xinyi Shen, DachengWang, Kebiao Mao, Emmanouil Anagnostou, Yang Hong, Inundation Extent Mapping by Synthetic Aperture Radar: A Review, Remote sensing, 2019,11(879), 1-17.

22.     Kebiao Mao*, Zhiyuan Zuo, Xinyi Shen, Tongren Xu, Chunyu Gao, Guang Liu, Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network, Chinese Geographical Science. 2018, 28,1, 1–11.

23.     Jiaqi Han, Kebiao Mao*, Tongren Xu, Jingpeng Guo, Zhiyuan Zuo, Chunyu Gao, A soil moisture estimation framework based on the CART algorithm and its application in China, Journal of Hydrology, 2018, 561, 65-75.

24.     Feifan Ge, Kebiao Mao*, Yuelin Jiang, Lei Wang, Tongren Xu, Chunyu Gao, Zhiyuan Zuo, Regional climate change after the commissioning of the Three Gorges Dam: a case study for the middle reaches of the Yangtze River, Climate research, 2018, 75, 33-51.

25.     Lang Xia, Fen Zhao, Liping Chen, Ruirui Zhang, Kebiao Mao*,Arve Kylling, Ying Ma,Performance comparison of the MODIS and the VIIRS 1.38 μm cirrus cloud channels using libRadtran and CALIOP data,Remote Sensing of Environment,2018,206,363–374.

26.     Lang Xia, Fen Zhao, Kebiao Mao*, Zijin Yuan, Zhiyuan Zuo,Tongren Xu, SPI-Based Analyses of Drought Changes over the Past 60 Years in China’s Major Crop-Growing Areas, Remote Sens. 2018, 171(10),1-15.

27.     Kebiao Mao*, Ying Ma, Xuelan Tan, Xinyi Shen, Guang Liu, Zhaoliang Li, Jingming Chen, Lang Xia, Global surface temperature change analysis based on MODIS data in recent twelve years, Advance Space Research, 2017,59,503-512.

28.     Kebiao Mao*, Xinyi Shen, Zhiyuan Zuo, et al., An advanced radiative transfer and neural network scheme and evaluation for estimating water vapor content from MODIS data, Atmosphere, 2017, 139(8):1-11.

29.     Kebiao Mao*, Chen J M, Li Z L, Y. Ma, Y. Song, X. Tan, K. Yang, Global water vapor content decreases from 2003 to 2012: an analysis based on MODIS Data, Chinese Geographical Science, 2017, 27(1), 1-7.

30.     Kebiao Mao*, Zhaoliang Li, Jingming Chen, Ying Ma, Guang Liu, Xuelan Tan, Kaixin Yang, Global vegetation change analysis based on MODIS data in recent twelve years, High Technology Letters, 2016, 22(4), 343-349.

31.     Xinyi Shen, Humberto J.V., Efthymios I.N., Emmanouil N.A., Yang Hong, Zeong Hao, Ke Zhang, Kebiao Mao, GDBC: A tool for generating global-scale distributed basin morphometry, Environmental Modelling & Software, 2016, 83: 212-223.

32.     Kebaio Mao*, Y. Ma, T.R. Xu, Q. Liu, Han Jiaqi, L. Xia, X. Y. Shen, T. J. He. A New Perspective about Climate Change, Scientific Journal of Earth Science, 2015, 5(1): 12-17.

33.     Kebiao Mao*, Y. Ma, X. Y. Shen, L. Xia, Shiying Tian, Han Jiaqi, Qing Liu, A method for retrieving soil moisture from GNSS-R by using experiment data, High Technology letters, 2015,21(2):219-223.

34.     Lang Xia, F. Zhao, Y. Ma, Z. W. Sun, X. Y. Shen, Kebiao Mao*, An Improved Algorithm for the Detection of Cirrus Clouds in the Tibetan Plateau Using VIIRS and MODIS Data, Journal of Atmosphere and Oceanic Technology, 2015, 32, 2125-2129.

35.     Xinyi Shen*, Hong Yang, Qiming Qin, Basara Jeffrey, Kebiao Mao, A semi-physical microwave surface emission model for soil moisture retrieval, IEEE Transaction on Geoscience and Remote Sensing, 2015, 53 (7), 4079-4090.

36.     Lang Xia, Kebiao Mao*, Y. Ma, F. Zhao, L.P. Jiang, X.Y. Shen, Z. H. Qin, An algorithm for retrieving land surface temperature using VIIRS data in combination with multi-sensors, Sensors, 2014, 14, 21385-21408.

37.     Kebaio Mao*, Y. Ma, L. Xia, Wendy Y. Chen, X. Y. Shen, T. J. He, Global aerosol change in the last decade: An analysis based on MODIS data, Atmospheric Environment, 2014, 94, 680-686.

38.     Tongren Xu, S.M. Bateni, Shunlin Liang, Dara Entekhabi, Kebiao Mao, Estimation of Surface Turbulent Heat Fluxes via Variational Assimilation of Sequences of Land Surface Temperatures from Geostationary Operational Environmental Satellites, Journal of Geophysical Research-atmosphere, 2014, 119, 10780-10798.

39.      Guang Liu, Jinghui Fan, Feng Zhao, Kebiao Mao, Changyong Dou, Monitoring elevation change of glaciers on Geladandong Mountain using TanDEM-X SAR interferometry, Journal of Mountain Science, 2017, 14(5), 859-869.

40.     Kebiao Mao*, Y. Ma, X. Y. Shen, L. Xia, et al., The Study of Estimation Method of Broadband Emissivity from EOS/MODIS Data, High Technology letters, 2014, 21(1):88-91.

41.     Kebiao Mao*, Y. Ma, L, Xia, X. Shen, et al., A neural network method for monitoring snowstorm: A case study in southern China. Chinese Geographical Science, 2014, 24(5):599-606.

42.     Shen X., Kebiao Mao, Q. Qin, Y. Hong, Guifu Zhang, Bare surface soil moisture estimation using double-angle and dual-polarization L-band radar data, IEEE Transaction on Geoscience and Remote Sensing, 2013, 51(7):3931-3942.

43.     Kebiao Mao*, Y. Ma, X. Shen, et al., Estimation of Broadband Emissivity (8-12um) from ASTER Data by Using RM-NN, Optics Express, 2012, 20(18): 20096-20101.

44.     JianCheng Shi, Y. Du, J. Du, L. Jiang, L, Chai, Kebiao Mao, et al., Progresses on microwave remote sensing of land surface parameters, Science China Earth Science, 2012, 55 (7): 1052-1078.

45.     Kebiao Mao*, Y. Ma, L. Xia, et al., The Monitoring Analysis for the Drought in China by Using an Improved MPI Method, Journal of Integrative Agriculture, 2012, 11(6), 1048-1058.

46.     Kebiao Mao*, SanMei Li, DaoLong Wang, LiXin Zhang, Huajun Tang, Xiufeng Wang, Zhaoliang Li, Retrieval of Land Surface Temperature and Emissivity from ASTER1B data Using Dynamic Learning Neural Network, international journal of remote sensing, 2011, 32(19), 5413-5423.

47.     Kebiao Mao*, H. T. Li, D. Y. Hu, J. Wang, J. X. Huang, Z. L. Li, Q. B. Zhou, and H. J. Tang, Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN, Optics Express, 2010, 18(9), 9542–9554.

48.     Kebiao Mao*, Huajun Tang, Xiufeng Wang, Qingbo Zhou, Daolong Wang, Near-Surface Air Temperature Estimation From ASTER Data Using Neural Network, International Journal of Remote Sensing,2008, 29(20): 6021-6028.

49.     Kebiao Mao*, Jiancheng Shi, Huajun Tang, Zhao-Liang Li, Xiufeng Wang, Kunshan Chen, A Neural Network Technique for Separating Land Surface Emissivity and Temperature from ASTER Imagery, IEEE Trans. Geosci. Remote Sensing, 2008, 46(1), 200-208.

50.     Kebiao Mao*, J. Shi, Z. Li, and H. Tang, An RM-NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data, Journal of Geophysical Research-atmosphere, 2007, 112,D21102, 1-17.

51.     Kebiao Mao*, Jiancheng Shi, Zhaoliang Li, Zhihao Qin, Manchun Li, Bin Xu, A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data, Science in China (Series D),2007,7:1115-1120.

52.     Kebiao Mao*, Tang H. J., Zhang L. X., Li M. C., Guo Y., Zhao D. Z., A Method for Retrieving Soil  Moisture in Tibet Region By Utilizing Microwave Index from TRMM/TMI Data, International Journal of Remote Sensing, 2008,29(10):2905-2925.

53.     Kebiao Mao*, Qin Z., Shi J., Gong P., A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from MODIS Data, International Journal of Remote Sensing, 2005,26:3181-3204.

54.     Huang Jianxi, Zeng Yuan, Wu Wenbin, Mao Kebiao, Xu Jingyu, Su Wei,Estimation of Overstory and Understory Leaf Area Index by Combining Hyperion and Panchromatic QuickBird Data Using Neural Network Method,Sensor Letters, 2011, 9( 3), 964-973(10).

55.     Huang Jianxi, Zeng Yuan, Kuusk Andres, Wu Bingfang, Dong Lixin, Mao Kebiao, Chen Jinsong, Inverting a forest canopy reflectance model to retrieve the overstorey and understorey leaf area index for forest stands, International Journal of Remote Sensing, 2011, 32(22), 7591-7611.

56.     Changhong Su, Bojie Fu, Yongping Wei, Yihe Lü, Guohua Liu, Daolong Wang, Kebiao Mao, Xiaoming Feng,Ecosystem management based on ecosystem services and human activities: a case study in the Yanhe watershed,Sustainability Science. 2012, 7(1). 17-32. DOI:10.1007/s11625-011-0145-1.

57.      Kebiao Mao*, Y. Ma, Z.Y. Zuo, Y. Q. Jiao, F. Wang, Q. Liu, Z. W. Sun, Global water vapor content and vegetation change analysis based on remote sensing data, International Geoscience and Remote Sensing Symposium (IGARSS16), 2016, 17, 5205-5208.

58.     Jingpeng Guo, Huiqian Chen, Yinghui Zhao, Kebiao Mao*, Ning Li, Liang Zhu, A dataset of major agricultural disasters and disaster losses in China(1949-2015), China Scientific Data, 2018, 3(2), 1-7.

59.     Kebiao Mao*, Y. Ma, Z.Y. Zuo, F. Wang, Y. Q. Jiao, X.Y. Shen, Q. Liu, Which year is the hottest or coldest from 2001 to 2012 based on remote sensing data, International Geoscience and Remote Sensing Symposium (IGARSS16), 2016, 16, 5213-5216.

60.     kebiao Mao*, L.P. Jiang, Y. Z. Liu, D. L. Wang, H.J.Tang, Retrieval analysis of snow depth from AMSR-E data in complex weather conditions, IITA-GRS2010, V1, 177-180.

61.     kebiao Mao*, C.Y.Gao, L.J.Han, W. ZHANG, H.J. Tang, The drought monitoring in China by  using AMSR-E data, IITA-GRS2010, V1, 181-184.

62.     Kebiao Mao*, Zhang Mengyang, Wang Jianming,Tang Huajun,Zhou Qingbo, The Study of Soil Moisture Retrieval Algorithm from GNSS-R, IITA Conference on Geoscience and Remote Sensing (IITA-GRS 2008), Shanghai, 2008,12,1-5.

63.     Kebiao Mao*, Wang Jianming, Zhang Mengyang, Tang Huajun, Zhou Qingbo, An AMSR-E Monitoring of Snowstorm -Disaster in South-China in 2008 Year, IITA Conference on Geoscience and Remote Sensing (IITA-GRS 2008), Shanghai, 2008,12,1-5.

64.     Kebiao Mao*, Jiancheng Shi, Huajun Tang, Ying Guo, Yubao Qiu, Liying Li, A neural –network technique for retrieving land surface temperature from AMSR-E passive microwave data, International Geoscience and Remote Sensing Symposium (IGARSS07), 23-28 July 2007, 7: 4422-4425.

65.     Kebiao Mao*, Zhihao Qin, Manchun Li, Lixin Zhang, Bin Xu, Lingmei Jiang, An Algorithm for Surface Soil Moisture Retrieval Using the Microwave Polarization Difference Index, International Geoscience and Remote Sensing Symposium (IGARSS06).

66.     Kebiao Mao*,Jiangcheng Shi, Zhaoliang Li,  Zhihao Qin,  Xiufeng  Wang,  Lingmei  Jiang,  A Multiple-Band Algorithm for Separating Land Surface Emissivity and Temperature from ASTER Imagery, International Geoscience and Remote Sensing Symposium (IGARSS06).

67.     Kebiao Mao*, Jiancheng Shi, Zhihao Qin, Peng Gong, Wei Liu, Lina Xu, A Multiple-band Algorithm for Retrieving Land-Surface Temperature and Emissivity from MODIS Data, International Geoscience and Remote Sensing Symposium (IGARSS05), 25-29 July 2005, 5: 3269 - 3272.

68.     Kebiao Mao*,Jiancheng Shi ,Zhaoliang Li,Zhihao Qin, Yuanyuan Jia, Land Surface Temperature and Emissivity Retrieved from the AMSR Passive Microwave Data, International Geoscience and Remote Sensing Symposium (IGARSS05), 25-29 July 2005, 4: 2247 – 2249.

69.     Kebiao Mao*, Zhihao Qin, Bin Xu, Manchun Li, Jianming Wang Shengli Wu, The Influence Analysis of Water Content for the Accuracy of Practical Split-window Algorithm, International Geoscience and Remote Sensing Symposium (IGARSS05), 25-29 July 2005, 5: 3266 – 3268.

70.     Kebiao Mao*, Jiancheng Shi, Zhihao Qin, Peng Gong, An Advanced and Optimized Split-Window Algorithm for Retrieving Land-Surface Temperature from ASTER Data, International Symposium on Physical Measurements and Signatures in Remote Sensing (ispmsrs),2005.10.

71.     Kebiao Mao*, Jiancheng Shi, Zhaoliang Li, Zhihao Qin, Peng Gong, A Physics Based on Statistics Algorithm for Retrieving Land Surface Temperature and Soil Moisture From AMSR-E Passive Microwave Data, International Symposium on Physical Measurements and Signatures in Remote Sensing (ispmsrs), 2005.10.

72.     Shengli Wu, Kebiao Mao, Jiangyang Du, Lina Xu, Jiangming Wang, The Potential of TRMM/PR Data to Monitor Snow in Tibetan Plateau, International Geoscience and Remote Sensing Symposium (IGARSS05), 25-29 July 2005, 5: 4431 -4434.

73.     Xu Lina, Jiancheng Shi, Kebiao Mao, Estimating snow albedo in Tibetan plateau using MODIS, Proceedings of SPIE - The International Society for Optical Engineering, 2005.

74.     Ying Guo, Jiancheng Shi, Kebiao Mao, Surface temperature effect on soil moisture retrieval from AMSR-E data, International Geoscience and Remote Sensing Symposium (IGARSS07), 23-28 July 2007, 7:1192 –1195.

75.  毛克彪*,田世英,袁紫晋,王涵,谭雪兰,乡村振兴战略视域下极端气候灾害与藏粮于民计划分析和展望,农业展望,2019,8,47-51.

76.  曹萌萌,毛克彪*,严毅博,崔京路,袁紫晋,Nusseiba,基于MODIS 数据的洞庭湖水体和水华时空变化研究,中国环境科学,2019,39(6),2523-2531.

77.  孟祥金,毛克彪*,孟飞,师春香,赵冰,袁紫晋,基于空间权重分解的降尺度土壤水分产品的中国土壤水分时空格局研究,高技术通讯,2019,29(4),402-412.

78.  毛克彪*,杨军,韩秀珍,唐世浩,袁紫晋,高春雨,基于深度动态学习神经网络和辐射传输模型地表温度反演算法研究,中国农业信息,2018,30(5),47-57.

79.  严毅博,毛克彪*,许世卫,田世英,曹萌萌,袁紫晋,基于国际贸易与自然灾害背景下的中国农产品供需平衡展望,农业展望,2019,6,76-82.

80.  杨艳颖,毛克彪*,韩秀珍,杨军,郭晶鹏,1949-2016年中国旱灾规律及其对粮食产量的影响,2018,30(5),76-90.

81.  赵冰,毛克彪*,蔡玉林,王涵,孟祥金,袁紫晋,农业大数据关键技术及应用进展,中国农业信息,2018,30(6),25-34.

82.  崔京路,毛克彪*,陈日清,曹萌萌,袁紫晋,唐世浩,基于高分辨率遥感影像的农作物灾损评估研究,2018,30(6),63-70.

83.  安悦,周国华,贺艳华,毛克彪,谭雪兰,基于“三生”视角的乡村功能分区及调控-以长株潭地球为例,地理研究,2018,37(4),695-703.

84.  葛非凡,毛克彪*,蒋跃林,姜立鹏,范玉芬,王一舒,谭雪兰,李建军,三峡大坝运行后长江中下游流域气温与植被变化特征及原因分析,气候变化研究进展, 2017,13 (6): 578-588.

85.  韩家琪,毛克彪*,葛非凡,郭晶鹏,黎玲萍,分类回归树算法在土壤水分估算中的应用,遥感信息,2018,33(3),46-53.

86.  毛克彪*,左志远,朱高峰,唐华俊,赵映慧,马莹,全球气候和生态系统变化与星体轨道位置变化关系研究,高技术通讯,2016, 26(11), 890 - 899.

87.  赵映慧,郭晶鹏,毛克彪*,项亚楠,李怡函,韩家琪,吴馁,1949-2015年中国典型自然灾害及粮食灾损特征, 地理学报, 2017,72(7), 1261-1276.

88.  付秀丽,黎玲萍,毛克彪*,谭雪兰,李建军,孙旭,左志远,基于卷积神经网络模型的遥感图像分析,高技术通讯,2017,27(3),203-212.

89.  谭雪兰,于思远,欧阳巧玲,毛克彪,贺艳华,周国华,快速城市化区域农村空心化测度与影响因素研究—以长株潭地区为例,地理研究,2017,36(4),684-694.

90.  毛克彪*,把脉极端气候-保护粮食安全,科学大观园,2016,8, 24-26.

91.  黎玲萍,毛克彪*,付秀丽,马莹,王芳,刘勍,国内外农业大数据应用研究分析,高技术通讯,2016,(4):414-422

92.  刘勍,毛克彪*,马莹,韩家琪,夏浪,农业大数据浅析及与Web GIS结合应用,遥感信息,2016,31(1):124-128.

93.  刘勍,毛克彪*,马莹,谭雪兰,韩家琪,黎玲萍,夏浪,基于农业大数据可视化方法的中国生猪空间流通模式,地理科学,2017, 37(1):118-124.

94.  葛非凡,毛克彪*,蒋跃林,谭雪兰,赵映慧,夏浪,华东地区夏季极端高温特征及其对植被的影响,中国农业气象, 2017, 38(01): 42-51.

95.  郭晶鹏,毛克彪*,赵映慧,左志远,陈冬冬. 我国蔬菜价格研究进展[J]. 北方园艺,2016,(23):180-186.

96.  夏浪,毛克彪*,孙知文,马莹,赵芬,基于DNB验证的VIIRS夜间云检测方法,国土资源遥感,2014,26(3):74-79.

97.  夏浪,毛克彪*,马莹,孙知文,赵芬,基于可见光红外成像辐射仪数据的地表温度反演,农业工程学报,2014, 8(4):109-116.

98.  夏浪,毛克彪*,孙知文,马莹,针对NPP VIIRS数据的云检测方法研究,中国环境科学, 2014, 34(3):574-580.

99.  夏浪, 毛克彪*,孙知文,马莹,Suomi Npp VIIRS数据介绍及其在云检测上的应用分析. 地球科学前沿,2013, 3:1-6.

100.    毛克彪*, 施建成, 李召良, 覃志豪, 李满春, 徐斌, 一个针对被动微波数据AMSRE数据反演地表温度的物理统计算法, 中国科学D辑,2006, 36(12):1170-1176. 

101.    施建成,杜阳,杜今阳,蒋玲梅,柴琳娜,毛克彪等,微波遥感地表参数反演进展,中国科学D辑,2012, 42(6):814-842.

102.    毛克彪*,马莹,夏浪,沈心一,用MODIS数据反演近地表空气温度的RM-NN算法,高技术通讯,2013,23(5):462-466.

103.    毛克彪*, 胡德勇,黄健熙,张武,张立新,邹金秋,唐华俊, 针对被动微波数据AMSR-E数据的土壤水分反演算法, 高技术通讯, 2010,20(6), 651-659.

104.    毛克彪*,王道龙,李滋睿,张立新,周清波,唐华俊,李丹丹,利用AMSR-E被动微波数据反演地表温度的神经网络算法, 高技术通讯,2009,19(11):1195-1200.

105.    毛克彪*,王建明,张孟阳,唐华俊,周清波,基于AIEM和实地观测数据对GNSS-R反演土壤水分的研究,高技术通讯,2009,3(19):295-301.

106.    毛克彪*,覃志豪, 施建成, 宫鹏, 针对MODIS数据的劈窗算法研究,武汉大学学报(信息科学版)2005(8):703-708.

107.    毛克彪*,覃志豪,宫鹏, 余琴, 劈窗算法精度评价及参数敏感性分析,中国矿业大学学报,2005(3):318-322.

108.    毛克彪*, 覃志豪, 施建成, 用MODIS影像和劈窗算法反演山东半岛的地表温度,中国矿业大学学报(自然科学版),2005(1):46-50.

109.    毛克彪*, 唐华俊, 周清波, 马柱国, 实用劈窗算法的改进及大气水汽含量对精度影响评价,武汉大学学报(信息科学版),2008,33(2):116-119.

110.    毛克彪*,王建明,张孟阳,周清波,马柱国,GNSS-R信号反演土壤水分研究分析,遥感信息,2009,3:92-97.

111.    毛克彪*, 马莹, 正视极端气候与粮食安全, 财经月刊, 2011, 407: 100-102.

112.    马莹,毛克彪,全球天灾回顾与前瞻,财经月刊,2012,419:81-83.

113.    毛克彪*, 唐华俊, 周清波,王建明, 马柱国, 利用被动微波数据AMSR-E对2008年中国南方雪灾监测分析,中国农业资源与区划, 2009, 30(1):46-50.

114.    毛克彪*, 唐华俊, 陈仲新, 王永前 , 一个用神经网络优化的针对ASTER数据反演地表温度和发射率的多波段算法, 国土资源遥感,2007, 73 (3): 18-22.

115.    毛克彪*, 唐华俊, 周清波,陈仲新, 陈佑启,覃志豪, 用辐射传输方程从MODIS数据中反演地表温度的方法,兰州大学学报(自然科学版),2007,43(4):12-17.(EI)

116.    毛克彪*, 唐华俊, 李丽英, 许丽娜, 一个从MODIS数据同时反演地表温度和发射率的神经网络算法, 遥感信息, 2007,92(4):9-15.

117.    毛克彪*, 唐华俊, 周清波,陈佑启,被动微波遥感土壤水分反演研究综述,遥感技术与应用,2007,22(3):466-470.

118.    毛克彪*, 唐华俊, 周清波,陈仲新,陈佑启,赵登忠, AMSR-E微波极化指数与MODIS植被指数关系研究, 国土资源遥感, 2007, 1: 27-31.

119.    毛克彪*, 唐华俊, 陈仲新, 邱玉宝, 覃志豪, 李满春, 一个针对ASTER数据的劈窗算法, 遥感信息, 2006, 5:7-11.

120.    毛克彪*,施建成, 覃志豪, 宫鹏, 徐斌, 蒋玲梅, 一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法,遥感学报,2006, 4: 593-599.

121.    毛克彪*,覃志豪, 徐斌, 被动微波土壤水分反演模型研究,测绘与空间地理信息, 2005(5):12-15

122.    毛克彪*, 施建成, 李召良, 覃志豪, 贾媛媛, 用被动微波AMSR数据反演地表温度及发射率方法研究,国土资源遥感,2005(3):14-18

123.    毛克彪*, 覃志豪, 李满春, 徐斌, AMSR被动微波数据介绍及主要应用研究领域分析,遥感信息.2005,3:63-66.

124.    毛克彪*,施建成, 覃志豪, 宫鹏, 徐斌,从MODIS数据中同时反演地表温度和比辐射率的多波段算法研究,兰州大学学报(自然科学版)(专辑)2005(6):49-55.

125.    毛克彪*,覃志豪, 秦晓敏, 高懋芳, 中国中部地带乡镇企业发展战略研究,经济地理2004(增刊):286-290.

126.    毛克彪*,覃志豪等,针对ASTER的单窗算法研究,测绘学院学报,2005(1):40-43。

127.    毛克彪*,覃志豪,王建明,武胜利,针对MODIS数据的大气水汽含量及31和32波段透过率计算,国土资源遥感,2005.1:26-30.

128.    毛克彪*,覃志豪,用MODIS影像反演环渤海地区的大气水汽含量,遥感信息,2004(4):47-49.

129.    毛克彪*,覃志豪, 刘伟, 用MODIS影像和单窗算法反演环渤海地区的地表温度,空间与测绘, 2004(6):23-25

130.    毛克彪*, 覃志豪,大气辐射传输模型及MODTRAN中大气透过率计算,空间与测绘, 2004, 27(2):1-3.

131.    毛克彪*, 覃志豪,张万昌,针对ETM基于BP网络模型的像元分解研究,遥感信息, 2004, 74(2):27-30.

132.    毛克彪*, 覃志豪,李昕,李海涛, 空间数据挖掘与GIS集成及应用研究,测绘与空间地理信息, 2004, 27(1):14-18.

133.    毛克彪*, 覃志豪,张万昌,一个基于SOFM网络模型的遥感图象分类方法,遥感技术与应用,2003, 6:399-402.

134.    毛克彪*, 覃志豪,陈晓燕,李昕,基于WEBGIS的电子商务数据挖掘研究,测绘学院学报, 2003, 3:180-182

135.    毛克彪*, 覃志豪,李海涛,周若鸿,基于空间数据仓库的空间数据挖掘研究,遥感信息, 2002,68(4):19-26.

136.    毛克彪*,田庆久,空间数据挖掘技术及应用研究,遥感技术与应用,2002, (4):198-206.

137.    邱玉宝,施建成,蒋玲梅,毛克彪,郭英,AMSR-E被动微波土壤水分与降雨率时空相关性分析研究,北京师范大学学报(自然科学版),2007,43(3):350-356.

138.    丁莉东,覃志豪,毛克彪,基于MODIS影像数据的劈窗算法研究及其参数确定,遥感技术与应用,2005,20(2):284-289.

139.    秦晓敏,覃志豪,毛克彪,基于MODIS数据的陕西省地表温度的空间分布研究,干旱区地理, 2005,28(4):548-553.

140.    汤海鹏,毛克彪,覃志豪,吴毅, 空间数据挖掘工具浅谈, 空间与测绘.2005(3):4-6.

141.    汤海鹏,毛克彪,覃志豪,吴毅, 遥感分析中小型地物波谱数据库系统的设计与实现,空间与测绘,2004(6):32-35.

会议论文:

142.    毛克彪*, 唐华俊, 周清波,王建明, 马柱国, 利用被动微波数据AMSR-E对2008年中国南方雪灾监测分析,中国农业资源与区划学会年会(2008.11).(获论文一等奖)

143.    毛克彪*,施建成,唐华俊,Xiufeng Wang, 应用人工神经网络法从ASTER1B数据中反演陆表温度、发射率和水汽含量, 海峡两岸会议,桂林, 2008.9.

144.    毛克彪*,唐华俊,周清波,马柱国,针对被动微波AMSR-E数据的土壤水分反演算法研究, 全国节水农作制度理论与技术研讨会,北京,2008.5.

145.    毛克彪*, 唐华俊, 覃志豪等, 昭通发展生态观光农业产业的思路和措施,中国农业资源与区划学会年会(2007).(获论文优秀奖)

146.    毛克彪*, 唐华俊, 陈仲新, 周清波, 覃志豪, 赵登忠, 大气水汽含量参数对实用劈窗算法LST的影响及精度评价, 全国博士论坛-林业及生态相关学科,北京林业大学,2006.10. 论文集: 890-895.(获二等奖)

147.    毛克彪*,施建成, 覃志豪, 宫鹏,徐斌,张钟军, 蒋玲梅, 一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法,海峡两岸会议,2005.8.

148.    毛克彪*,李丹丹,高懋芳,周清波,王道龙,唐华俊,陈仲新,一个针对被动微波AMSR-E数据的土壤水分反演算法,全国农业遥感技术研讨会,2009.8,106-116.

149.    毛克彪*,马莹,夏浪,徐同仁,沈心一,韩家琪,刘勍,徐彬, 地球气候变化与星球引力和磁场变化关系研究新观点,第七届海峡两岸遥感/遥测研讨会,2014, 1-8.

 


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学校介绍

中国农业科学院研究生院成立于1979年,1981年经国务院批准开始实施硕士、博士学历学位教育,是我国国家级科研机构举办研究生教育的先行院所之一。作为支撑我院研究生教育的中国农业科学院成立于1957年,是农业农村部直属的综合性国家农业科研机构,是全国综合性农业科学研究的最高学术机构,是农业及农业科学技术战略咨询机构,是三农领域国家战略科技力量,担负着全国农业重大基础与应用基础研究、应用研究和高新技术技术研究的任务,致力于解决我国农业及农村经济发展中公益性、基础性、全局性、战略性、前瞻性的重大科学与技术问题。在推动农业科技创新、服务经济社会发展、培养高层次人才、促进国际交流与合作等方面发挥着重要作用。“十三五”期间,共获得国家科学技术奖36项,占全国农业领域获奖总数的26%。其中科技进步一等奖1项,自然科学二等奖2项,技术发明二等奖6项,科技进步二等奖27项;获得省部级奖励229项;发表论文近30000篇,其中SCI论文近15000篇、《NATURE》《SCIENCE》《CELL》等国际顶级学术期刊论文29篇;出版专著近1500部,通过国审品种等近1200个,获得植物新品种权397项,新兽药证书55个等。科研成果与科研实力处于行业领先地位。

中国农科院研究生教育依托中国农业科学院的国家级科研平台基地、先进科研设施设备、重大科研攻关项目、稳定的科研经费保障、前沿交叉学科集群、一流的导师队伍、广泛的国际合作机制、丰富的图书文献等各种重要资源,形成了38个研究所共同参与、“院所结合、两段式培养”这一特色鲜明的科研机构举办研究生教育的创新模式,将中国农业科学院的科研资源优势转化为学科建设、人才培养、特色办学优势,为研究生完成课程教学、开展学术研究、参与课题实践、培养创新能力提供了农业科研国家队特有的广阔舞台。

中国农业科学院深圳农业基因组研究所(以下简称“基因组所”),创建于2014年,是农业农村部,中国农科院和深圳市在科技体制改革的背景下,整合农业基因组学研究力量在深圳成立的新型研究所。

  成立以来,基因组所深入贯彻落实习近平总书记“四个面向、两个一流”指示精神,开展科研自主权改革试点工作,被列为中国农科院现代院所改革的“试验田”,建设了由中国农科院与深圳市主管领导任共同理事长的理事会;组建了近800人的研究队伍;形成了以组学技术为核心、辐射农业、食品和生态方向的学科体系,获批“岭南现代农业科学与技术广东省实验室深圳分中心”“农业农村部农业基因数据分析重点实验室”等创新载体;在包括 Science、Nature、Cell 等顶级期刊在内的杂志上发表SCI论文400多篇,以基因组设计育种育成国审、省审新品种30余个,农业基因组学等研究领域占据世界前沿。 2019年、2020年连续两年自然指数排名全国农业类科研院所第一名,多项成果入选“‘十三五’农业科技十大标志性成果”“中国生命科学十大进展”“中国农业科学十大进展”。先后获得“何梁何利基金”奖、“周光召基础科学奖”“深圳经济特区建立40周年创新创业和先进模范人物”“深圳市市长奖”等奖励。基因组所联合深圳市相关部门提出了“深圳国际食品谷”,规划已得到市政府印发,将构建农业食品产学研协作生态,做出科技推动农业食品产业转型升级的先行示范。

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按照国家相关规定,我院全日制与非全日制硕士研究生学费标准均为:8000元/人/年。凡我院各研究所录取的推免生均免收第一年学费,优秀推免生免收基本学制内全部学费。

  我院全日制非定向硕士研究生奖助学金体系由奖学金和助学金两部分组成。奖学金包括国家奖学金、学业奖学金(100%覆盖)、研究生院单项奖学金和企业奖学金。助学金包括国家助学金、研究生院助学金、导师助研津贴、“三助”津贴和特困生补助,具体奖助学金政策可登陆中国农业科学院研究生院网站查询。


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