人才
方红亮
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方红亮，男，博士，中国科学院地理科学与资源研究所，地理信息科学与技术全国重点实验室，研究员，中国科学院大学特聘岗位教授，博士生导师。主要从事冠层辐射传输建模、地表关键参数遥感反演以及验证研究。2003年获美国马里兰大学地理系博士学位，后在该大学地理系和美国宇航局等机构工作，2009年由中科院人才计划引进任现职至今。担任国际对地观测委员会（CEOS）陆表生物物理参数验证专题组组长（20162022），Remote Sensing of Environment编委（2020），IEEE Geoscience and Remote Sensing Letters副主编（20162024），《地理学报》编委等职。
研究领域与研究方向：
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）基于遥感大数据的辐射传输建模和参数反演。基于遥感大数据和光谱向量建模理论，针对陆表植被，研究新型高效辐射传输模型和参数反演方法。(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。
教育背景：
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。
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；
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；
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；
工作经历：
2025.01~至今，中国科学院地理科学与资源研究所，地理信息科学与技术全国重点实验室 研究员
2009.09~2025.01，中科院地理科学与资源研究所，资源与环境信息系统国家重点实验室研究员
2007.06~2009.09 美国宇航局全球变化数据中心 水文专家
2006.01~2007.05 美国马里兰大学地理系助理研究教授
2003.08~2005.12 美国马里兰大学地理系 博士后
科研业绩：
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。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。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。生产了国际上首套500米分辨率全球植被聚集指数和叶倾角产品，填补了相关产品的空白，为植被生态和陆面过程模型的应用提供了数据基础。在国内外发表学术论文130余篇。
科研项目:
1．国家重点研发计划项目 “脆弱生态系统修复成效与稳定性监测评估新技术研发与示范”第二课题：“恢复植被结构和功能性状与光谱信息定量耦合” (2024YFF1308102)下属专题（12/2024-12/2027）；70万
2. 国家重点研发计划项目 “多源遥感协同森林地上生物量估测技术”第三课题：“星载波形 LiDAR 和多角度光学协同森林 AGB 估测技术” (2023YFF1303903)下属专题（12/2023-12/2027）；70万
3. 国家自然科学基金面上项目(42471398）：水稻冠层光子再碰撞概率测量、遥感反演与验证研究（01/2025-12/2028）；45万
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
代表性论著：
2025
Li, S., and Fang, H., 2025. Mapping global leaf inclination angle (LIA) based on field measurement data. Earth System Science Data, 17(4): 1347-1366. https://doi.org/10.5194/essd-17-1347-2025
Gu, C., Li, J., Liu, Q., Zhang, H., Huete, A., Fang, H., Liu, L., Mumtaz, F., Lin, S., Wang, X., Dong, Y., Zhao, J., Bai, J., Yu, W., Liu, C., & Guan, L. (2025). Deriving leaf-scale chlorophyll index (CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory. Remote Sensing of Environment, 322, 114692. https://doi.org/10.1016/j.rse.2025.114692
Yang, P., Verhoef, W., Fang, H., Fan, W., & van der Tol, C., 2025. Linking Kubelka-Munk and recollision probability theories for radiative transfer simulations in turbid canopy. Remote Sensing of Environment, 321, 114680.
https://doi.org/10.1016/j.rse.2025.114680
Fang, H., Wu, Y., Zhang, Y., Wang, Y., Li, S., Ma, T., Li, Y., and Guo, K., 2025. Canopy vertical structural profiles measured at two temperate forest sites in northern China: Intercomparison of tower, mast, crane, and UAV measurements. Trees,39(1),9. https://doi.org/10.1007/s00468-024-02589-4
Fang, H., 2024. A synthesis on the vegetation spectral invariant theory. Remote Sensing Technology and Application (in Chinese).
Fan, Y., Li, Y., Li, S., and Fang, H., 2024. Continuous automatic observation of forest canopy structure parameters based on digital hemispherical photography.
Remote Sensing Technology and Application (in Chinese).
Jin, H., Qiao, Y., Liu, T., Xie, X., Fang, H., Guo, Q., and Zhao, W., 2024. A hierarchical downscaling scheme for generating fine-resolution leaf area index with multisource and multiscale observations via deep learning. International Journal of Applied Earth Observation and Geoinformation. 133, 104152. https://doi.org/10.1016/j.jag.2024.104152
Li, Y., Fang, H., Wang, Y., Li, S., Ma, T., Wu, Y., and Tang, H., 2024. Validation of the vertical canopy cover profile products derived from the GEDI over selected forest sites. Science of Remote Sensing, 10, 100158. https://doi.org/10.1016/j.srs.2024.100158
Liu, W., Mõttus, M., Gastellu-Etchegorry, J. P., Fang, H., and Atherton, J., 2024. Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field. Agricultural and Forest Meteorology, 355, 110132. https://doi.org/10.1016/j.agrformet.2024.110132
Mao, H., Felker-Quinn, E., Sive, B., Zhang, L., Ye, Z., & Fang, H., 2024. Examining indicators and methods for quantifying ozone exposure to vegetation. Atmospheric Environment, 316, 120195. https://doi.org/10.1016/j.atmosenv.2023.120195
Wang, Y., and Fang, H., 2024. Derivation and evaluation of LAI from the ICESat-2 data over the NEON sites: The impact of segment size and beam type. Remote Sensing, 16(16), 3078. https://doi.org/10.3390/rs16163078
Fang, H., 2023. Photon recollision probability and the spectral invariant theory: Principles, methods, and applications. Remote Sensing of Environment,
299, 113859. https://doi.org/10.1016/j.rse.2023.113859
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, 60(1), 2209968, https://doi.org/10.1080/15481603.2023.2214987
Filella, I., Descals, A., Balzarolo, M., Yin, G., Verger, A., Fang, H., and Peñuelas, 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.
Wen, J. G., Liu, Q. H., Li, Z. Y., Li, X., Liu, S. M., Xiao, Q., Gao, Z. H., Ma, M. G., Che, T., Liu, L. Y., Fang, H. L., Yan, G. J., Ge, Y., Chen, E. X., Zhang, Y., Ma, L. L., Wu, X. D., and Chen, X., 2023. A review of the development of remote sensing field experiments and product validation in China. National Remote Sensing Bulletin (in Chinese), 27(3): 573-583.
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
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
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
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.
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 (20012017). Remote Sensing of Environment. 232, 111296. https://doi.org/10.1016/j.rse.2019.111296.
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.
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.
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.
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.
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.
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.
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, 2012. Comparison of Different Methods for Corn LAI Estimation over Northeastern China
International Journal of Applied Earth Observation and Geoinformation.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.
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.
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 Sensingof Environment, 112(1): 75-93.
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 continuous 1 km surface albedo data set over North America from Terra MODIS products. Journal of Geophysical Research – Atmospheres, 112, D20206, doi: 10.1029/2006JD008377.
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.
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.
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.
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.
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.
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.
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.
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
联系方式：
通讯地址：北京市朝阳区大屯路甲11号 中国科学院地理科学与资源研究所
邮 编：100101
办公电话：010-64888005
传 真：010-64889630
E-mail地址:fanghl@lreis.ac.cn
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