方红亮

中国科学院地理科学与资源研究所

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  • 方红亮
  • 研究员

个人简况

男,1971年生,浙江省淳安县人,博士。现任中国科学院地理科学与资源研究所研究员,博士生导师。国际对地观测委员会(CEOS)陆表关键参数验证工作组LAI专题组组长,IEEE Geoscience and Remote Sensing Letters编委,《地理学报》编委。

教育经历

1989年9月-1993年7月就读于华东师范大学地理系,获学士学位;

1993年9月-1996年7月就读于中国科学院地理科学与资源研究所,获硕士学位;

1996年9月-1998年12月就读于中国科学院地理科学与资源研究所,获博士学位;

1999年9月-2003年7月就读于美国马里兰大学地理系,获博士学位。

工作经历

2003年8月-2005年12月,美国马里兰大学地理系任博士后;

2006年1月-2007年5月,美国马里兰大学地理系任助理研究员;

2007年6月-2009年9月,美国宇航局全球变化数据中心任水文专家;

2009年9月-聘为中国科学院地理科学与资源研究所研究员;

2010年通过中国科学院择优选拔。

研究领域和研究方向

研究领域:

陆地生态系统关键参数的遥感反演、不确定性及其质量改进研究。

主要研究方向:

遥感辐射传输建模、关键植被参数反演与产品生产、遥感产品的不确定性分析与质量改进。

近期主要研究工作:

(1)基于复杂下垫面的辐射传输建模和参数反演。针对水体和积雪等复杂下垫面,研究新的有针对性的辐射传输模型和反演方法。

(2) 陆地生态系统关键参数的不确定分析与质量评价。针对影响全球变化关键数据集中的地面要素(如叶面积指数等)进行不确定性的分析与定量化表达方法研究,分别评价它们的可信度和适用范围,建立不确定性与质量评价理论方法体系,为全球变化关键数据集的正确使用提供科学依据。

(3)气候变化关键数据集质量改进方法研究。针对现有气候变化关键数据集存在的主要问题,研究适用于不同数据的均一化处理方法及相应的质量控制方案,发展多源数据的质量改进方法和多尺度时空数据融合与改进方法,构建具有针对性的数据订正方法体系,建立能够更准确反映地表动态变化的高时空分辨率数据产品。

主要科研成果

在植被参数的遥感反演、遥感产品的不确定性和质量改进以及植被辐射传输模型的构建与反演等方面,取得了系列研究成果。近年来在东北粮食主产区开展了长期的植被结构数据地面观测和遥感反演试验,对农作物叶面积指数、孔隙率和聚集指数连续观测对比研究。同时与国内外植被遥感专家合作开展关于全球LAI的交叉验证和不确定性研究,对全球主要的中尺度LAI产品进行了交叉验证,并对各产品的不确定性进行了分析,为LAI遥感信息产品在全球陆面、水文与气候模型的应用提供了科学依据。在此基础上,探索新型植被辐射传输建模理论和植被参数反演方法,从土壤背景反射率和直漫分离的反演方法两方面入手,提高冠层反射率建模水平和参数反演精度。共发表科技论文50余篇,其中SCI索引论文30余篇。

主要研究项目

1. 国家重点研发计划项目(2016YFA0600201)“基于多源卫星遥感的高分辨率全球碳同化系统研究”第一课题:生物圈碳循环关键参数遥感协同反演研究(07/2016-06/2021);644万

2. 国家自然科学基金面上项目(41471295):植被聚集度系数的时空变异特征、遥感反演与验证研究(01/2015-12/2018);90万

3. 国家自然科学基金面上项目(41171333):全球叶面积指数遥感产品在中国水稻区的不确定性评价与改进方法研究(01/2012-12/2015);65万

4. 中国科学院“百人计划”项目:遥感信息地学参数的获取及其与地表过程模型的同化(01/2011-12/2014);200万

5. 中国科学院地理科学与资源研究所“百人计划”启动项目,华北平原农作物关键生物物理参数的遥感获取(09/2009-09/2011);100万

代表性学术论文

In English

1.Fang, H., Baret, F., Plummer, S., and Schaepman-Strub, G. (2019). An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, https://doi.org/10.1029/2018RG000608 .

2.Fang, H., Zhang Y., Wei S., Li W., Ye Y., Sun T., and W. Liu, 2019. Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111377 .

3.Jiang, C., and H. Fang, 2019. GSV: a general model for hyperspectral soil reflectance simulation. International Journal of Applied Earth Observation and Geoinformation, 83, 101932, https://doi.org/10.1016/j.jag.2019.101932 .

4.Wei, S., Fang, H., Schaaf, C. B., He, L., and J. M. Chen, 2019. Global 500 m clumping index product derived from MODIS BRDF data (2001-2017). Remote Sensing of Environment. 232, 111296. https://doi.org/10.1016/j.rse.2019.111296 .

5.Fang, H., Liu, W., Li, W., and Wei, S., 2018. Estimation of the directional and whole apparent clumping index (ACI) from indirect optical measurements. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 1-13. doi:10.1016/j.isprsjprs.2018.06.022.

6.Fang, H., Ye Y., Liu, W., Wei, S., and Ma, L., 2018. Continuous estimation of canopy leaf area index (LAI) and clumping index over broadleaf crop fields: An investigation of the PASTIS-57 instrument and smartphone applications. Agricultural and Forest Meteorology, 253-254, 48-61. doi: 10.1016/j.agrformet.2018.02.003.

7.Sun, T., Fang, H., Liu, W., and Ye, Y., 2017. Impact of water background on canopy reflectance anisotropy of a paddy rice field from multi-angle measurements. Agricultural and Forest Meteorology, 233, 143-152. doi:10.1016/j.agrformet.2016.11.010.

8.Wei, S., and H. Fang, 2016. Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: The influence of BRDF models and solar zenith angle. Remote Sensing of Environment.187: 476-491. doi: 10.1016/j.rse.2016.10.039.

9.Li, W., and H. Fang, 2015. Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground-based estimates over six FLUXNET sites. Journal of Geophysical Research – Biogeosciences, 120: 96-112,doi:10.1002/2014JG002754.

10.Pisek, J., Govind, A., Arndt, S.K., Hocking, D., Wardlaw, T.J., Fang, H., Matteucci, G., & Longdoz, B., 2015. Intercomparison of clumping index estimates from POLDER, MODIS, and MISR satellite data over reference sites. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 47-56, doi: 10.1016/j.isprsjprs.2014.11.004.

11.Fang, H., Li, W., Wei, S., and C. Jiang, 2014. Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods. Agricultural and Forest Meteorology, 198-199(0): 126-141, doi: 10.1016/j.agrformet.2014.08.005.

12.Liu, Q., S. Liang, Z. Xiao, and H. Fang, 2014. Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sensing of Environment, 145: 25-37.

13.Fang, H., Jiang, C., Li, W., Wei, S., Baret, F., Chen, J.M., Garcia-Haro, J., Liang, S., Liu, R., Myneni, R.B., Pinty, B., Xiao, Z., & Zhu, Z., 2013. Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties. Journal of Geophysical Research – Biogeosciences, 118(2): 529-548, doi: 10.1002/jgrg.20051.

14.Fang, H., W. Li, and R.B. Myneni, 2013. The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective. Remote Sensing, 5(2):830-844.

15.Fang, H., S. Wei, C. Jiang, and K. Scipal, 2012.Theoretical uncertainty analysis of global MODIS, CYCLOPES and GLOBCARBON LAI products using a triple collocation method.Remote Sensing of Environment, 124, 610-621.

16.Fang, H., S. Wei, and S. Liang, 2012. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment,119, 43-54.

17.Peng D., B. Zhang , L. Liu , H. Fang , D. Chen , Y. Hu , and L. Liu, 2012. Characteristics and drivers of global NDVI-based FPAR from 1982 to 2006. Global Biogeochemical Cycles, 26, GB3015, doi:10.1029/2011GB004060.

18.Zhao T., D. G. Brown, H. Fang, D. M. Theobald, T. Liu, and T. Zhang, 2012. Vegetation productivity consequences of human settlement growth in the eastern United States. Landscape Ecology, 27(2): 1149-1165. doi:10.1007/s10980-012-9766-8.

19.Yang, F., J. Sun, H. Fang, Z. Yao, J. Zhang, Y. Zhua, K. Song, Z. Wang, M. Hua, 2012. Comparison of Different Methods for Corn LAI Estimation over Northeastern China. International Journal of Applied Earth Observation and Geoinformation.18, 462-471.

20.Peng, D., B. Zhang , L. Liu , D. Chen , H. Fang , and Y. Hu, 2012.Seasonal dynamic pattern analysis on global FPAR derived from AVHRR GIMMS NDVI. International Journal of Digital Earth, 5(5): 439-455. doi:10.1080/17538947.2011.596579.

21.Fang, H., S. Liang, G. Hoogenboom, 2011. Integration of MODIS products and a crop simulation model for crop yield estimation. International Journal of Remote Sensing, 32(4): 1039-1065.

22.Fang, H., S. Liang, G. Hoogenboom, J. Teasdale, and M. Cavigelli, 2008. Crop yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing, 29(10): 3011-3032.

23.Fang, H., S. Liang, J. R. Townshend, and R. E. Dickinson, 2008. Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America. Remote Sensingof Environment, 112(1): 75-93.

24.Sun, W., S. Liang, G. Xu, H. Fang, and R. Dickinson, (2007), Mapping Plant Functional Types from MODIS Data Using Multisource Evidential Reasoning, Remote Sensing of Environment, 112(3): 1010-1024.

25.Fang, H., S. Liang, H.-Y. Kim, J. R. Townshend, C. L. Schaaf, A. H. Strahler, and R. E. Dickinson, 2007. Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products. Journal of Geophysical Research – Atmosphere, 112, D20206, doi: 10.1029/2006JD008377.

26.Liang, S., B. Zhong and H. Fang, 2006. Improved estimation of aerosol optical depth from MODIS imagery over land surfaces. Remote Sensing of Environment,104(4): 409-415.

27.Liang S., T. Zheng, R. Liu, H. Fang, S.C. Tsay, and S. Running, 2006. Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data. Journal of Geophysical Research - Atmosphere, 111, D15208, doi:10.1029/2005JD006730.

28.Fang, H., S. Liang, M. P. McClaran, W. van Leeuwen, S. Drake, S. E. Marsh, A. Thomson, R. C. Izaurralde, and N. J. Rosenberg, 2005. Biophysical Characteristics and management effects on semiarid rangeland observed from Landsat ETM+ data. IEEE Transactions on Geosciences and Remote Sensing, 43(1): 125-134.

29.Fang, H. and S. Liang, 2005. A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment, 94(3): 405-424.

30.Fang, H., G. Liu, and M. Kearney, 2005. Geo-relational analysis of soil type, soil salt content, landform, and land use in the Yellow River Delta, China. Environmental Management , 35(1): 1-13.

31.Walthall, C. L., W. P.Dulaney, M. C. Anderson, J. M. Norman, H. Fang and S. Liang, 2004. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sensing of Environment, 92(4): 465-474.

32.Fang, H., S. Liang, M. Chen, C. Walthall, and C. Daughtry, 2004. Statistical comparison of MISR, ETM+ and MODIS land surface reflectance and albedo products of the BARC Land Validation Core Site, USA. International Journal of Remote Sensing, 25(2): 409-422.

33.Liang, S., H. Fang, 2004. An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery. IEEE Geosciences and Remote Sensing Letters, 1(2): 112-117.

34.Fang, H. and S. Liang, 2003. Retrieve LAI from Landsat 7 ETM+ data with a neural network method: simulation and validation study. IEEE Transactions on Geosciences and Remote Sensing, 41(9): 2052-2062.

35.Fang, H., S. Liang and A. Kuusk, 2003. Retrieving LAI using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment,85(3): 257-270.

36.Liang, S. , H. Fang, L. Thorp, M. Kaul, T.G. Van Niel, T. R. McVicar, J. Pearlman, C. Walthall, C. Daughtry, F. Huemmrich, and D. L. B. Jupp, 2003. Estimation and validation of land surface broadband albedos and leaf area index from EO-1 ALI data. IEEE Transactions on Geosciences and Remote Sensing,41(6): 1260-1267.

37.Van Niel, T. G., T. R. McVicar, H. Fang, and S. Liang, 2003. Environmental moisture mapping for per-field discrimination of rice. International Journal of Remote Sensing,24(4): 885-890.

38.Liang, S., H. Fang, M. Chen, C. Walthall, C. Daughtry, J. Morisette, C. Schaff, and A. Strahler, 2002. Validating MODIS land surface reflectance and albedo products: Methods and preliminary results. Remote Sensing of Environment, 83(1-2): 149-162.

39.Liang, S., C. Shuey, A. Russ, H. Fang, M. Chen, C. Walthall, and C. Daughtry, 2002. Narrowband to Broadband Conversions of Land Surface Albedo: II. Validation. Remote Sensing of Environment,84(1): 25-41.

40.Liang, S., H. Fang, J. Morisette, M. Chen, C. Walthall, C. Daughtry, and C. Shuey, 2002. Atmospheric Correction of Landsat ETM+ Land Surface Imagery: II. Validation and Applications. IEEE Transactions on Geosciences and Remote Sensing, 40(12): 2736-2746.

41.Liang, S., H. Fang, M. Chen, 2001. Atmospheric Correction of Landsat ETM+ Land Surface Imagery: I. Methods. IEEE Transactions on Geosciences and Remote Sensing,39(11): 2490-2498.

42.Fang H. and J. Xu, 2000. Land Cover and Vegetation Change in the Yellow River Delta Nature Reserve Analyzed with Landsat Thematic Mapper Data. Geocarto International,15(4): 41-47.

43.Fang H., 1999. The Distribution of Physicians and Hospital Beds in Kansas. Papers and Proceedings of the Applied Geography Conferences. F. Schoolmaster (ed.). pp. 360-365. Charlotte, North Carolina. October 13-16, 1999.

44.Xu J., H. Fang, S. Fu, X. Huang, 1999. SPOT Image used in River Water Suspended Sediment and Its Environmental Background Analysis. The Journal of Chinese Geography, 9(4): 402-409.

45.Fang H., 1998. Rice Crop Area Estimation of an Administrative Division in China Using Remote Sensing. International Journal of Remote Sensing. 19(17): 3411-3419.

46.Fang H., B. Wu, H. Liu and X. Huang, 1998. Using NOAA AVHRR and Landsat TM Data to Estimate Rice Planting Area Year-by-Year. International Journal of Remote Sensing.19(3):521-525.

47.Fang H., and G. Liu, 1998. YRDGIS and the Yellow River Delta. GIS Asia/Pacific, April/May, 26-30.

48.Liu H., B. Wu, H. Fang, J. Huang, 1996. A Practical Method for Rice Acreage Estimation with Remote Sensing. The Journal of Chinese Geography, 6(4): 61-65.

In Chinese

49.居为民,方红亮, 田向军, 江飞等, 2016. 基于多源卫星遥感的高分辨率全球碳同化系统研究. 地球科学进展,31(11): 1105-1110.

50.江冲亚,方红亮,魏珊珊,2012. 地表粗糙度参数化研究综述. 地球科学进展, 27(3): 292-303.

51.许珺, 方红亮, 傅肃性, 黄绚, 1999. 运用SPOT数据进行河流水体悬浮固体浓度的研究——以台湾淡水河为例. 遥感技术与应用, 14(4):17-22.

52.张健挺, 郭殿声, 方红亮, 1998. 基于决策支持树算法的地理空间数据挖掘和知识获取-以黄河三角洲土壤数据库为例. 地理研究, 17:43-49.

53.刘高焕, 方红亮, 陈晓莉, 1998. 黄河三角洲可持续发展信息系统. 地球信息, 3: 46-51.

54.方红亮, 李军, 黄方红, 1998. 大型遥感图像处理应用项目综合数据库开发. 遥感信息, 4:10-13.

55.刘卫国, 龚建华, 方红亮, 1998. 地理信息系统支持下的知识获取及其在遥感影像植被分类中的应用研究. 遥感学报, 2(3):1-7.

56.方红亮, 1998. 两种水稻种植面积遥感提取方案的分析. 地理学报, 63(1):58-65.

57.方红亮, 杨晓梅, 杜云艳, 1998. 运用主成分变换-逆变换对ADEOS AVNIRXS和PAN进行复合的研究. 遥感技术与应用, 13(3):48-53.

58.方红亮, 田庆久, 1998. 高光谱遥感在植被监测中的研究综述. 遥感技术与应用, 13(1):62-69.

59.方红亮, 吴炳方, 刘海燕, 黄绚, 1997. 运用NOAAAVHRR和LandsatTM数据估算多年水稻种植面积. 遥感技术与应用, 12(3):23-26.

60.方红亮, 黄绚, 1997. 地学应用中的遥感图像处理若干问题的分析. 地理研究, 16(2): 96-103.

61.方红亮, 刘海燕, 黄进良, 刘可群, 1996. 江汉平原水稻遥感估产集成系统. 遥感技术与应用, 11(2): 45-53.