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，中国科学院大学特聘岗位教授，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生物物理专题组组长（2016-2022），Remote Sensing of Environment 编委，IEEE Geoscience and Remote Sensing Letters副主编，《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－聘为中科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.    国家自然科学基金面上项目(42171358）：森林垂直分层LAI和CI时空变异特征、LiDAR遥感反演与验证研究（01/2022-12/2025）；59万

　　2.  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 

　　3.  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 

　　4. 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 

　　5. 中国科学院项目：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 　　

　　6. 中国科学院地理科学与资源研究所启动项目，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  

　　2023

　　Zhu, K., Chen, J., Wang, S., Fang, H., Chen, B., Zhang, L., Li, Y., Zheng, C., & Amir, M. (2023). Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest. ISPRS Journal of Photogrammetry and Remote Sensing, 201, 78-91.  https://doi.org/10.1016/j.isprsjprs.2023.05.014

　　Wang, Y., Fang, H., Zhang, Y., Li, S., Pang, Y., Ma, T., and Li, Y., 2023. Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site. GIScience & Remote Sensing, doi: 10.1080/15481603.2023.2214987

　　Filella, I., Descals, A., Balzarolo, M., Yin, G., Verger, A., Fang, H., and Pe？uelas, J, 2023. Photosynthetically active radiation and foliage clumping improve satellite-based NIRv estimates of gross primary production. Remote Sensing, 15(8):2207. https://doi.org/10.3390/rs15082207

　　Wang, Z., Qu, Y., and Fang, H. 2023. Improving the performance of smartphone-derived crop leaf area index. National Remote Sensing Bulletin (in Chinese), 27(2): 441-455. https://doi.org/10.11834/jrs.20210439.

　　Li, S., and H.Fang, 2023. Determination of the leaf inclination angle (LIA) through field and remote sensing methods: Current status and future prospects. Remote Sensing, 15(4), 946. https://doi.org/10.3390/rs15040946

　　Ma, T., and H. Fang, 2023. GSV-L: A general spectral vector model for hyperspectral leaf spectra simulation. International Journal of Applied Earth Observation and Geoinformation, 117, 103216. https://doi.org/10.1016/j.jag.2023.103216

　　Zhang, Y., Wu, Z., Fang, H., Gao, X., Wang, J., and Wu, G., 2023. Estimation of daily FAPAR from MODIS instantaneous observations at forest sites. Agricultural and Forest Meteorology, 331, 109336. https://doi.org/10.1016/j.agrformet.2023.109336

　　2022

　　Liu, T., Jin, H., Li, A., Fang, H., Wei, D., Xie, X., & Nan, X. (2022). Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method. Remote Sensing, 14(19), 4733. https://doi.org/10.3390/rs14194733

　　Liu, T., Jin, H., Xie, X., Fang, H., Wei, D., and Li, A., 2022. Bi-LSTM model for time series leaf area index estimation using multiple satellite products, IEEE Geoscience and Remote Sensing Letters. 19, 2506805. https://doi.org/10.1109/LGRS.2022.3199765

　　Li, S., Fang, H., Zhang, Y., and Wang, Y., 2022. Comprehensive evaluation of global CI, FVC, and LAI products and their relationships using high-resolution reference data. Science of Remote Sensing, 5, 100066. https://doi.org/10.1016/j.srs.2022.100066

　　Li, Y. and Fang, H., 2022. Real-time software for the efficient generation of the clumping index and its application based on the Google Earth Engine. Remote Sensing, 14(15), 3837. https://doi.org/10.3390/rs14153837

　　Geng, X., Wang, X., Fang, H., Ye, J., Han, L., Gong, Y., & Cai, D. (2022). Vegetation coverage of desert ecosystems in the Qinghai-Tibet Plateau is underestimated. Ecological Indicators, 137, 108780. https://doi.org/10.1016/j.ecolind.2022.108780

　　Sun, T., Fang, H., Chen, L., and Sun, R., 2022. A method to estimate clear-sky albedo of paddy rice fields. Remote Sensing, 14(20), 5185. https://doi.org/10.3390/rs14205185

　　2021

　　Fang, H., Che, T., Jin, R., Li, A., Li, X., Li, Z., Liu, S., Ma, M., Xiao, Q., and Zhang Y., 2021. On the construction of China's fiducial reference measurement (FRM) network for land surface remote sensing product validation (In Chinese), Advances in Earth Science, 36(12): 1215-1223. https://doi.org/10.11867/j.issn.1001-8166.2022.003

　　Chen, B., Lu, X., Wang, S., Chen, J.M., Liu, Y., Fang, H., Liu, Z., Jiang, F., Arain, M.A., Chen, J., & Wang, X. (2021). Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration. Remote Sensing, 13, 4075. https://doi.org/10.3390/rs13204075

　　Yan, K., Zou, D., Yan, G., Fang, H., Weiss, M., Rautiainen, M., Knyazikhin, Y., & Myneni, R.B. (2021). A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020. Journal of Remote Sensing, 2021, 7410921. https://doi.org/10.34133/2021/7410921

　　Fang, H., Wang Y., Zhang, Y., and Li S., 2021. Long-term variation of global GEOV2 and MODIS leaf area index (LAI) and their uncertainties: An insight into the product stabilities. Journal of Remote Sensing, 2021, 9842830. https://doi.org/10.34133/2021/9842830

　　Fang, H., Li, S., Zhang, Y., Wei, S., and Wang Y., 2021. New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index. Science of Remote Sensing, 4, 100027. https://doi.org/10.1016/j.srs.2021.100027

　　Hu, K., Zhang, Z., Fang, H., Lu, Y., Gu, Z., and Gao M., 2021. Spatial-temporal characteristics and driving factors of the foliage clumping index in the Sanjiang Plain from 2001 to 2015, Remote Sensing, 13(14), 2797. https://doi.org/10.3390/rs13142797

　　Zhang Y., Fang, H., Wang, Y., and Li S., 2021. Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data. Agricultural and Forest Meteorology, 307, 108535. https://doi.org/10.1016/j.agrformet.2021.108535

　　Fang H., 2021. Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques: A review (in Chinese).  Chinese Science Bulliten, 66(24), 3141-3153. https://doi.org/10.1360/TB-2020-1057.        

　　Fang, H., 2021. Scaling effects of the true and effective leaf area index (LAI and LAIe) and clumping Index (CI) (in Chinese). Journal of Geo-information Science, 23(7): 1155-1168. https://doi.org/0.12082/dqxxkx.2021.200609.

　　Fang, H., 2021. Canopy clumping index (CI): A review of methods, characteristics, and applications. Agricultural and Forest Meteorology, 303, 108374.  https://doi.org/10.1016/j.agrformet.2021.108374

　　Fang, H., 2021. Retrieval of land surface parameters from geostationary satellite data： An overview of recent developments (in Chinese). National Remote Sensing Bulletin, 25(1): 109-125. https://doi.org/10.11834/jrs.20210194    

　　Li, W., Fang, H., Wei, S., Weiss, M., and Baret F., 2021. Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops. Agricultural and Forest Meteorology, 297, 108273. https://doi.org/10.1016/j.agrformet.2020.108273

　　2020

　　Chen, B., Arain, M. A., Chen, J. M., Wang, S., Fang, H., Liu, Z., Mo, G., and Liu, J., 2020.  Importance of shaded leaf contribution to the total GPP of Canadian terrestrial ecosystems: evaluation of MODIS GPP. Journal of Geophysical Research: Biogeosciences, 125(10), https://doi.org/10.1029/2020JG005917.

　　Wang, Y., and H. Fang, 2020. Estimation of LAI with the LiDAR Technology: A Review. Remote Sensing, 12(20), 3457. https://doi.org/10.3390/rs12203457

　　Fang, H., 2020. Development and validation of satellite leaf area index (LAI) products in China (in Chinese). Remote Sensing Technology and Application, 35(5), 990-1003.

　　Wang Y., Fang H., Zhang Y., and Li S., 2020. Retrieval of Forest LAI Using Airborne LVIS and Spaceborne GLAS Waveform LiDAR Data (in Chinese). Remote Sensing Technology and Application, 35(5), 1004-1014.

　　Zhang Y., Fang, H., Ma, L., Ye, Y., and Wang Y., 2020. Estimation of forest leaf area index and clumping index from the Global Positioning System (GPS) satellite carrier-to-noise-density ratio (C/N₀). Remote Sensing Letters, 11(2): 146-155.  https://doi.org/10.1080/2150704X.2019.1692386.

　　2019

　　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, 57(3): 739-799. https://doi.org/10.1029/2018RG000608.

　　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, 233, 111377, https://doi.org/10.？1016/？j.？rse.？2019.？111377.

　　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.

　　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.

　　2018

　　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.

　　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.

　　2017

　　Jiang, C., Ryu, Y., Fang, H., Myneni, R., Claverie, M. and Zhu, Z., 2017. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Global Change Biology, 23(10): 4133-4146. doi: 10.1111/gcb.13787.

　　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.

　　2016

　　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.

　　2015

　　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.

　　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.

　　2014

　　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.

　　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.

　　2013

　　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.

　　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.

　　2012

　　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.

　　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. 

　　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.

　　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.

　　Jiang, C., H. Fang, and S. Wei, 2012. Review of land surface roughness parameterization study (in Chinese). Advances in Earth Science, 27(3): 292-303.

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

　　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.

　　2011

　　Fang, H., S. Liang, G. Hoogenboom, 2011. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 32(4): 1039-1065.

　　2008

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

　　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 Sensing of Environment, 112(1): 75-93.

　　2007

　　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.

　　Fang, H., S. Liang, H.-Y. Kim, J. R. Townshend, C. L. Schaaf, A. H. Strahler, and R. E. Dickinson, 2007. Developing a spatially continuous1  kmsurface albedo data set over North America from Terra MODIS products. Journal of Geophysical Research – Atmospheres, 112, D20206, doi: 10.1029/2006JD008377.

　　2006

　　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.

　　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.

　　2005

　　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.

　　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.

　　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.

　　2004

　　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.

　　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.

　　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.

　　2003

　　Fang, H. and S. Liang, 2003. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Transactions on Geosciences and Remote Sensing, 41(9): 2052-2062.

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

　　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.

　　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.

　　2002

　　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.

　　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.

　　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.

　　2001

　　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.

　　Before 2000

　　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.

　　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.

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

　　Xu J., H. Fang, S. Fu, X. Huang, 1999. Estimating Suspended Sediment Concentrations from SPOT Image: A Case Study in Danshuihe, Taiwan (in Chinese). Remote Sensing Technology and Application, 14(4): 17-22.

　　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.

　　Zhang J., D. Guo, H. Fang, 1998. Geospatial Data Ming and Knowledge Discovery using Decision Tree Algorithm-A Case Study of Soil Data Set of Yellow River Delta (YRD) (in Chinese). Geographical Research, 17, Supplement, 43-49.

　　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.

　　Fang H., J. Li, F. Huang, 1998. Integrated Database Development in Large Scale Remote Sensing Application Project (in Chinese). Remote Sensing Information. 1998-4, pp.10-13.

　　Liu W., J. Gong and H. Fang, 1998. Knowledge Extraction from GIS Database and its Application in Vegetation Classification (in Chinese). The Journal of Remote Sensing, 2(3):1-7.

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

　　Fang H., 1998. An Discussion On Two Strategies Applied to Estimate Rice planting Area of an Administrative Division Using Remote Sensing Technique (in Chinese). ACTA Geographical Sinica. 63(1):58-65.

　　Fang H., X. Yang, and Y. Du, 1998. Research on Integrating ADEOS-AVNIR XS and PAN DataUsing Primary Component Transformation – Antitransformation (in Chinese). Remote Sensing Technology and Application, 13(3): 48-53.

　　Fang H., and Q. Tian, 1998. A Review of Hyperspectral Remote Sensing in Vegetation Monitoring (in Chinese). Remote Sensing Technology and Application, 13(1): 62-69.

　　Fang H., and X. Huang, 1997. Remote Sensing Technique Applied in Geoscience-A Review Of Its Present Development (in Chinese). Geographical Research, 16(2): 96-103.

　　Fang H., H. Liu, J. Huang, K. Liu, 1996. An Integrated System For Rice Production Estimation (in Chinese). Remote Sensing Technology and Application, 11(2): 45-53. 

　　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

　　汪梓鑫，屈永华，方红亮，2023.智能手机农作物叶面积指数测量算法改进.遥感学报，27(2): 441-455.  https://doi.org/10.11834/jrs.20210439.　

　　方红亮，车涛，晋锐，李爱农，李新，李增元，刘绍民，马明国，肖青，张永光, 2021. 建设中国陆表遥感产品真实性检验基准台站网络的思考, 地球科学进展, 36(12): 1215-1223. https://doi.org/10.11867/j.issn.1001-8166.2022.003.

　　方红亮，2021. 森林垂直结构参数实测与遥感研究进展: 以叶面积指数和聚集指数为例, 科学通报, , 66(24), 3141-3153. https://doi.org/10.1360/TB-2020-1057.

　　方红亮，2021. 真实和有效叶面积指数及聚集指数的尺度效应, 地球信息科学学报, 23(7): 1155-1168. https://doi.org/10.12082/dqxxkx.2021.200609.　　

　　方红亮, 2021. 基于地球静止气象卫星的地表参数遥感研究进展, 遥感学报, 25(1): 109-125

　　方红亮，2020. 我国叶面积指数卫星遥感产品生产及验证. 遥感技术与应用, 35(5): 990-1003. 

　　汪垚 ，方红亮，张英慧，李思佳, 2020. 基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI. 遥感技术与应用, 35(5): 1004-1014. 

　　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.

　　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. 

　　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. 

　　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. 

　　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.  

　　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. 

　　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. 

　　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. 

　　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. 

　　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. 

　　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. 

　　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. 

　　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. 　

研究生招生与培养 

　　招生专业：地图学与地理信息系统。 

　　招生方向: 植被定量遥感，遥感信息分析与应用，遥感机理与方法 

　　研究生培养：以形成探索的科学精神，塑造严谨的思维方式，锻炼扎实的科研素养为培养目标，以地表参数遥感定量反演方向的科学研究为培养内容，为国家输送具有扎实的理论基础、过硬的科研素养和优秀的创新能力的遥感学科人才。 

　　欢迎有地学农学或植物学基础、对遥感科学感兴趣的学生报考。 

联系方式 

　　通信地址：北京市安定门外大屯路甲11号 

　　中国科学院地理科学与资源研究所 

　　邮    编： 100101 

　　办公电话: +86-10-64888005 

　　传    真：+86-10-64889630 

　　E–mail: fanghl@lreis.ac.cn 

更新日期：2023年6月6日