研究員
方紅亮
文章來源： | 發(fā)布時間：2016-09-09 | 【打印】 【關(guān)閉】
（個人網(wǎng)頁）
????方紅亮，男，博士，中國科學(xué)院地理科學(xué)與資源研究所，地理信息科學(xué)與技術(shù)全國重點實驗室，研究員，中國科學(xué)院大學(xué)特聘崗位教授，博士生導(dǎo)師。主要從事冠層輻射傳輸建模、地表關(guān)鍵參數(shù)遙感反演以及驗證研究。2003年獲美國馬里蘭大學(xué)地理系博士學(xué)位，后在該大學(xué)地理系和美國宇航局等機構(gòu)工作，2009年由中科院人才計劃引進任現(xiàn)職至今。擔(dān)任國際對地觀測委員會（CEOS）陸表生物物理參數(shù)驗證專題組組長（2016?2022），Remote Sensing of Environment編委（2020?），IEEE Geoscience and Remote Sensing Letters副主編（2016?2024），《地理學(xué)報》編委等職。
研究領(lǐng)域與研究方向：
研究領(lǐng)域：陸地生態(tài)系統(tǒng)關(guān)鍵參數(shù)的遙感反演、不確定性及其質(zhì)量改進研究。?
主要研究方向：遙感輻射傳輸建模、關(guān)鍵植被參數(shù)反演與產(chǎn)品生產(chǎn)、遙感產(chǎn)品的不確定性分析與質(zhì)量改進。?
近期主要研究工作：（1）基于遙感大數(shù)據(jù)的輻射傳輸建模和參數(shù)反演。基于遙感大數(shù)據(jù)和光譜向量建模理論，針對陸表植被，研究新型高效輻射傳輸模型和參數(shù)反演方法。(2) 陸地生態(tài)系統(tǒng)關(guān)鍵參數(shù)的不確定分析與質(zhì)量評價。針對影響全球變化關(guān)鍵數(shù)據(jù)集中的地面要素（如葉面積指數(shù)等）進行不確定性的分析與定量化表達方法研究，分別評價它們的可信度和適用范圍，建立不確定性與質(zhì)量評價理論方法體系，為全球變化關(guān)鍵數(shù)據(jù)集的正確使用提供科學(xué)依據(jù)。（3）氣候變化關(guān)鍵數(shù)據(jù)集質(zhì)量改進方法研究。針對現(xiàn)有氣候變化關(guān)鍵數(shù)據(jù)集存在的主要問題，研究適用于不同數(shù)據(jù)的均一化處理方法及相應(yīng)的質(zhì)量控制方案，發(fā)展多源數(shù)據(jù)的質(zhì)量改進方法和多尺度時空數(shù)據(jù)融合與改進方法，構(gòu)建具有針對性的數(shù)據(jù)訂正方法體系，建立能夠更準確反映地表動態(tài)變化的高時空分辨率數(shù)據(jù)產(chǎn)品。?
教育背景：
1999年9月－2003年7月就讀于美國馬里蘭大學(xué)地理系，獲博士學(xué)位。
1996年9月－1998年12月就讀于中國科學(xué)院地理科學(xué)與資源研究所，獲博士學(xué)位；?
1993年9月－1996年7月就讀于中國科學(xué)院地理科學(xué)與資源研究所，獲碩士學(xué)位；?
1989年9月－1993年7月就讀于華東師范大學(xué)地理系，獲學(xué)士學(xué)位；?
工作經(jīng)歷：
?????2025.01~至今，中國科學(xué)院地理科學(xué)與資源研究所，地理信息科學(xué)與技術(shù)全國重點實驗室 研究員
2009.09~2025.01，中科院地理科學(xué)與資源研究所，資源與環(huán)境信息系統(tǒng)國家重點實驗室研究員
2007.06~2009.09 美國宇航局全球變化數(shù)據(jù)中心 水文專家?
2006.01~2007.05 美國馬里蘭大學(xué)地理系助理研究教授?
2003.08~2005.12 美國馬里蘭大學(xué)地理系 博士后?
科研業(yè)績：
????在植被參數(shù)的遙感反演、遙感產(chǎn)品的不確定性和質(zhì)量改進以及植被輻射傳輸模型的構(gòu)建與反演等方面，取得了系列研究成果。在東北糧食主產(chǎn)區(qū)開展了長期的植被結(jié)構(gòu)數(shù)據(jù)地面觀測和遙感反演試驗，對農(nóng)作物葉面積指數(shù)、孔隙率和聚集指數(shù)連續(xù)觀測對比研究。與國內(nèi)外植被遙感專家合作開展關(guān)于全球LAI的交叉驗證和不確定性研究，對全球主要的中尺度LAI產(chǎn)品進行了交叉驗證，并對各產(chǎn)品的不確定性進行了分析，為LAI遙感信息產(chǎn)品在全球陸面、水文與氣候模型的應(yīng)用提供了科學(xué)依據(jù)。在此基礎(chǔ)上，探索新型植被輻射傳輸建模理論和植被參數(shù)反演方法，從土壤背景反射率和直漫分離的反演方法兩方面入手，提高冠層反射率建模水平和參數(shù)反演精度。生產(chǎn)了國際上首套500米分辨率全球植被聚集指數(shù)和葉傾角產(chǎn)品，填補了相關(guān)產(chǎn)品的空白，為植被生態(tài)和陸面過程模型的應(yīng)用提供了數(shù)據(jù)基礎(chǔ)。在國內(nèi)外發(fā)表學(xué)術(shù)論文130余篇。
科研項目:
1．國家重點研發(fā)計劃項目 “脆弱生態(tài)系統(tǒng)修復(fù)成效與穩(wěn)定性監(jiān)測評估新技術(shù)研發(fā)與示范”第二課題：“恢復(fù)植被結(jié)構(gòu)和功能性狀與光譜信息定量耦合” (2024YFF1308102)下屬專題（12/2024-12/2027）；70萬?
2. 國家重點研發(fā)計劃項目 “多源遙感協(xié)同森林地上生物量估測技術(shù)”第三課題：“星載波形 LiDAR 和多角度光學(xué)協(xié)同森林 AGB 估測技術(shù)” (2023YFF1303903)下屬專題（12/2023-12/2027）；70萬?
3. 國家自然科學(xué)基金面上項目(42471398）：水稻冠層光子再碰撞概率測量、遙感反演與驗證研究（01/2025-12/2028）；45萬
4. 國家自然科學(xué)基金面上項目(42171358）：森林垂直分層LAI和CI時空變異特征、LiDAR遙感反演與驗證研究（01/2022-12/2025）；59萬
??5.?國家重點研發(fā)計劃項目(2016YFA0600201)“基于多源衛(wèi)星遙感的高分辨率全球碳同化系統(tǒng)研究”第一課題：生物圈碳循環(huán)關(guān)鍵參數(shù)遙感協(xié)同反演研究（07/2016-06/2021）；644萬?
6.?國家自然科學(xué)基金面上項目(41471295）：植被聚集度系數(shù)的時空變異特征、遙感反演與驗證研究（01/2015-12/2018）；90萬?
7.?國家自然科學(xué)基金面上項目(41171333）：全球葉面積指數(shù)遙感產(chǎn)品在中國水稻區(qū)的不確定性評價與改進方法研究（01/2012-12/2015）；65萬?
8.?中國科學(xué)院項目：遙感信息地學(xué)參數(shù)的獲取及其與地表過程模型的同化（01/2011-12/2014）；200萬?
9.?中國科學(xué)院地理科學(xué)與資源研究所啟動項目，華北平原農(nóng)作物關(guān)鍵生物物理參數(shù)的遙感獲取（09/2009-09/2011）；100萬
代表性論著：
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., M?ttus, 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 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.
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 (2001?2017). 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.
研究生招生與培養(yǎng):
招生專業(yè)：地圖學(xué)與地理信息系統(tǒng)
招生方向：植被定量遙感，遙感信息分析與應(yīng)用，遙感機理與方法
聯(lián)系方式：
通訊地址：北京市朝陽區(qū)大屯路甲11號 中國科學(xué)院地理科學(xué)與資源研究所
郵 ???編：100101
辦公電話：010-64888005
傳 ???真：010-64889630
E-mail地址:fanghl@lreis.ac.cn
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