Journal of Remote Sensing期刊 青年编委

Water期刊 编委

Forests期刊 编委

中国定量遥感专家委员会 青年委员

数字山地委员会 委员

九三学社北京市委科技专委会 委员

1.植被辐射传输机理

2.全球植被参数产品

3.植被-气候响应关系

4.地表异常即时遥感

2007.9-2011.7 北京建筑大学 本科

2011.9-2018.1 北京师范大学 硕博连读

2014.9-2016.10 Boston University 联合培养

2018.7-2023.4 中国地质大学（北京）

2023.5-至今     北京师范大学

国家自然科学基金面上项目，基于异质性场景随机辐射传输建模的山地森林叶面积指数反演

国家自然科学基金青年项目，基于多源数据的山区公里级叶面积指数反演及验证

2024:

Yan, K.*, Wang, J., Peng, R., Yang, K.,Chen, X., Yin, G., Dong, J., Weiss, M., Pu, J., Myneni, R.B., 2024. HiQ-LAI: ahigh-quality reprocessed MODIS leaf area index dataset with betterspatiotemporal consistency from 2000 to 2022. Earth Syst. Sci. Data 16,1601–1622. https://doi.org/10.5194/essd-16-1601-2024

Pu, J., Yan, K.*, Roy, S., Zhu, Z., Rautiainen, M., Knyazikhin, Y., Myneni,R.B., 2024. Sensor-independent LAI/FPAR CDR: reconstructing a globalsensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to2022. Earth Syst. Sci. Data 16, 15–34. https://doi.org/10.5194/essd-16-15-2024

Yang, K., Yan, K.*, Zhang, X., Zhong, R., Chi, H., Liu, J., Ma, X., Wang, Y.,2024. Assessing FY-3D MERSI-II Observations for Vegetation Dynamics Monitoring:A Performance Test of Land Surface Reflectance. IEEE Trans. Geosci. RemoteSensing 62, 1–20. https://doi.org/10.1109/TGRS.2023.3348997

Yan, K., Zhang, X., Peng, R., Gao, S.,Liu, J., 2024. The Impact of Quality Control Methods on Vegetation MonitoringUsing MODIS FPAR Time Series. Forests 15, 553. https://doi.org/10.3390/f15030553

Zhong, R., Yan, K.*, Gao, S., Yang, K., Zhao, S., Ma, X., Zhu, P., Fan, L.,Yin, G., 2024. Response of grassland growing season length to extreme climaticevents on the Qinghai-Tibetan Plateau. Science of The Total Environment 909,168488. https://doi.org/10.1016/j.scitotenv.2023.168488

Zhu,X., Ma, X., Zhang, Z., Liu, Y., Luo, Y., Yan,K., Pei, T., Huete, A., 2024. Floating in the air:forecasting allergenic pollen concentration for managing urban public health.International Journal of Digital Earth 17, 2306894. https://doi.org/10.1080/17538947.2024.2306894

 

2023:

Gao, Si, Zhong, R., Yan, K.*, Ma, X., Chen, X., Pu, J.,Gao, Sicong, Qi, J., Yin, G., Myneni, R.B., 2023. Evaluating the saturationeffect of vegetation indices in forests using 3D radiative transfer simulationsand satellite observations. Remote Sensing of Environment 295, 113665. https://doi.org/10.1016/j.rse.2023.113665

Pu, J., Yan, K.*, Gao, S., Zhang, Y., Park, T., Sun, X., Weiss, M.,Knyazikhin, Y., Myneni, R.B., 2023. Improving the MODIS LAI compositing usingprior time-series information. Remote Sensing of Environment 287, 113493. https://doi.org/10.1016/j.rse.2023.113493

Wang, J., Yan, K.*, Gao, S., Pu, J., Liu, J., Park, T., Bi, J., Maeda, E.E.,Heiskanen, J., Knyazikhin, Y., Myneni, R.B., 2023. Improving the Quality ofMODIS LAI Products by Exploiting Spatiotemporal Correlation Information. IEEETrans. Geosci. Remote Sensing 61, 1–19. https://doi.org/10.1109/TGRS.2023.3264280

Sun,G., Pan, Z., Zhang, A., Jia, X., Ren, J., Fu, H., Yan, K., 2023. Large Kernel Spectral andSpatial Attention Networks for Hyperspectral Image Classification. IEEE Trans.Geosci. Remote Sensing 61, 1–15. https://doi.org/10.1109/TGRS.2023.3292065

Xu, T., Yan, K.*, He, Y., Gao, S., Yang, K., Wang, J., Liu, J., Liu, Z.,2023. Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image ClassificationPerspective. Remote Sensing15, 2975. https://doi.org/10.3390/rs15122975

Sun,G., Li, Z., Zhang, A., Wang, X., Yan, K.,Jia, X., Liu, Q., Li, J., 2023. A 10-m resolutionimpervious surface area map for the greater Mekong subregion from remotesensing images. Sci Data 10, 607. https://doi.org/10.1038/s41597-023-02518-z

Li, H., Yan, K.*, Gao, S., Ma, X., Zeng, Y., Li, W., Yin, G., Mu, X., Yan,G., Myneni, R.B., 2023. A Novel Inversion Approach for the Kernel-Driven BRDFModel for Heterogeneous Pixels. J Remote Sens 3, 0038. https://doi.org/10.34133/remotesensing.0038

Chen, R., Yin, G., Zhao, W., Yan, K., Wu, S., Hao, D., Liu, G.,2023. Topographic Correction of Optical Remote Sensing Images in MountainousAreas: A systematic review. IEEE Geosci. Remote Sens. Mag. 2–22. https://doi.org/10.1109/MGRS.2023.3311100

Gao, Y., Yang,T., Ye, Z., Lin, J., Yan, K., Bi,J., 2023. Global vegetation greenness interannual variability and itsevolvement in recent decades. Environ. Res. Commun. 5, 051011. https://doi.org/10.1088/2515-7620/acd74d

Lin, Y., Liu,S., Yan, L., Yan, K., Zeng, Y.,Yang, B., 2023. Improving the estimation of canopy structure using spectralinvariants: Theoretical basis and validation. Remote Sensing of Environment284, 113368. https://doi.org/10.1016/j.rse.2022.113368

Liu, X., Chen,Y., Mu, X., Yan, G., Xie, D., Ma, X., Yan,K., Song, W., Liu, Z., 2023. Correction for the Sun-Angle Effect on theNDVI Based on Path Length. IEEE Trans. Geosci. Remote Sensing 61, 1–17. https://doi.org/10.1109/TGRS.2023.3322780

Pan, Y., Peng,D., Chen, J.M., Myneni, R.B., Zhang, X., Huete, A.R., Fu, Y.H., Zheng, S., Yan, K., Yu, L., Zhu, P., Shen, M., Ju,W., Zhu, W., Xie, Q., Huang, W., Chen, Z., Huang, J., Wu, C., 2023.Climate-driven land surface phenology advance is overestimated due to ignoringland cover changes. Environ. Res. Lett. 18, 044045. https://doi.org/10.1088/1748-9326/acca34

 

2022:

Yan, K.*, Gao, S., Chi, H., Qi, J.,Song, W., Tong, Y., Mu, X., Yan, G., 2022a. Evaluation of theVegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation CoverEstimation. IEEE Trans. Geosci. Remote Sensing 60, 1–14. https://doi.org/10.1109/TGRS.2020.3048493

Yan, K., Li, H., Song, W., Tong, Y., Hao, D.,Zeng, Y., Mu, X., Yan, G., Fang, Y., Myneni, R.B., Schaaf, C., 2022b. Extending a Linear Kernel-Driven BRDF Model to RealisticallySimulate Reflectance Anisotropy Over Rugged Terrain. IEEE Trans. Geosci. RemoteSensing 60, 1–16. https://doi.org/10.1109/TGRS.2021.3064018

Chi,H., Yan, K.*, Yang, K., Du, S., Li,H., Qi, J., Zhou, W., 2022. Evaluation of TopographicCorrection Models Based on 3-D Radiative Transfer Simulation. IEEE Geosci.Remote Sensing Lett. 19, 1–5. https://doi.org/10.1109/LGRS.2021.3110907

Li, H., Yan, K.*, Gao, S., Song, W., Mu, X.,2022. Revisiting the Performance of the Kernel-Driven BRDF Model Using FilteredHigh-Quality POLDER Observations. Forests 13, 435. https://doi.org/10.3390/f13030435

Zou, D., Yan, K.*, Pu, J., Gao, S., Li, W., Mu,X., Knyazikhin, Y., Myneni, R.B., 2022. Revisit the Performance of MODIS andVIIRS Leaf Area Index Products from the Perspective of Time-Series Stability.IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 15, 8958–8973. https://doi.org/10.1109/JSTARS.2022.3214224

Liu, Y., Zhou, W., Gao, S., Ma,X., Yan, K., 2022a. PhenologicalResponses to Snow Seasonality in the Qilian Mountains Is a Function of BothElevation and Vegetation Types. Remote Sensing 14, 3629. https://doi.org/10.3390/rs14153629

Liu, Y., Zhou,W., Yan, K., Guan, Y., Wang, J.,2022b. Identification of the disturbed range of coal mining activities: A newland surface phenology perspective. Ecological Indicators 143, 109375. https://doi.org/10.1016/j.ecolind.2022.109375

Zhao, Y., Wang, M., Zhao, T.,Luo, Y., Li, Y., Yan, K., Lu, L.,Tran, N.N., Wu, X., Ma, X., 2022. Evaluating the potential of H8/AHIgeostationary observations for monitoring vegetation phenology over differentecosystem types in northern China. International Journal of Applied EarthObservation and Geoinformation 112, 102933. https://doi.org/10.1016/j.jag.2022.102933

 

2021:

Yan, K.*, Pu, J., Park, T., Xu, B.,Zeng, Y., Yan, G., Weiss, M., Knyazikhin, Y., Myneni, R.B., 2021a. Performancestability of the MODIS and VIIRS LAI algorithms inferred from analysis of longtime series of products. Remote Sensing of Environment 260, 112438. https://doi.org/10.1016/j.rse.2021.112438

Yan, K.*, Zhang, Y., Tong,Y., Zeng, Y., Pu, J., Gao, S., Li, L., Mu, X., Yan, G., Rautiainen, M.,Knyazikhin, Y., Myneni, R.B., 2021b. Modeling the radiation regime of adiscontinuous canopy based on the stochastic radiative transport theory:Modification, evaluation and validation. Remote Sensing of Environment 267,112728. https://doi.org/10.1016/j.rse.2021.112728

Wang, J., Wang, S., Zou, D.,Chen, H., Zhong, R., Li, H., Zhou, W., Yan,K., 2021. Social Network and Bibliometric Analysis of Unmanned AerialVehicle Remote Sensing Applications from 2010 to 2021. Remote Sensing 13, 2912. https://doi.org/10.3390/rs13152912

Yan, K.*, Zou, D., Yan, G., Fang, H., Weiss, M.,Rautiainen, M., Knyazikhin, Y., Myneni, R.B., 2021. ABibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to2020. J Remote Sens 2021, 7410921. https://doi.org/10.34133/2021/7410921

付东杰, 肖寒, 苏奋振, 周成虎, 董金玮, 曾也鲁, 闫凯, 李世卫, 吴进, 吴文周, 2021. 遥感云计算平台发展及地球科学应用. 遥感学报 25, 11.

刘钊, 闫凯, 王铸, 蔡闻佳, 史培军, 2021.1961-2020年中国31个城市热浪强度时空特征分析. 自然灾害学报 30, 9.

谢涓, 闫凯, 康志忠, 徐箫剑, 薛彬, 杨建峰, 陶金有, 2021. “祝融号”火星车多光谱相机岩矿类型识别的地面验证研究. 遥感学报 25, 15.

闫凯*, 陈慧敏, 付东杰, 曾也鲁, 董金玮, 李世卫, 吴秋生, 李翰良, 杜姝渊, 2022. 遥感云计算平台相关文献计量可视化分析. 遥感学报 26, 14.

阎广建, 姜海兰, 闫凯, 程诗宇, 宋婉娟, 童依依, 刘雅楠, 漆建波, 穆西晗, 张吴明, 2021. 多角度光学定量遥感. 遥感学报 25, 26.

 

2020:

Pu, J., Yan, K.*, Zhou, G., Lei, Y., Zhu, Y., Guo, D., Li, H., Xu, L.,Knyazikhin, Y., Myneni, R.B., 2020. Evaluation of the MODIS LAI/FPAR AlgorithmBased on 3D-RTM Simulations: A Case Study of Grassland. Remote Sensing 12,3391. https://doi.org/10.3390/rs12203391

Cao, Y., Wang, Y., Peng, J.,Zhang, L., Xu, L., Yan, K., Li, L.,2020. DML-GANR: Deep Metric Learning With Generative Adversarial NetworkRegularization for High Spatial Resolution Remote Sensing Image Retrieval. IEEETrans. Geosci. Remote Sensing 58, 8888–8904. https://doi.org/10.1109/TGRS.2020.2991545

Li, X., Huang,H., Shabanov, N.V., Chen, L., Yan, K.,Shi, J., 2020. Extending the stochastic radiative transfer theory to simulateBRF over forests with heterogeneous distribution of damaged foliage inside oftree crowns. Remote Sensing of Environment 250, 112040. https://doi.org/10.1016/j.rse.2020.112040

Pu, J., Yan, K.*, Zhang, Y., Xu,L., 2020a. Quality Analysis of the VIIRS LAI/FPAR Time-Series,in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote SensingSymposium. Presented at the IGARSS 2020 - 2020 IEEE International Geoscienceand Remote Sensing Symposium, IEEE, Waikoloa, HI, USA, pp. 3176–3179. https://doi.org/10.1109/IGARSS39084.2020.9323339

Xu, B., Li, J.,Park, T., Liu, Q., Zeng, Y., Yin, G., Yan,K., Chen, C., Zhao, J., Fan, W., Knyazikhin, Y., Myneni, R.B., 2020.Improving leaf area index retrieval over heterogeneous surface mixed withwater. Remote Sensing of Environment 240, 111700. https://doi.org/10.1016/j.rse.2020.111700

Yan, G., Chu,Q., Tong, Y., Mu, X., Qi, J., Zhou, Y., Liu, Y., Wang, T., Xie, D., Zhang, W., Yan, K., Chen, S., Zhou, H., 2020. AnOperational Method for Validating the Downward Shortwave Radiation Over RuggedTerrains. IEEE Trans. Geosci. Remote Sensing 1–18. https://doi.org/10.1109/TGRS.2020.2994384

Yin, G., Li, J., Xu, B., Zeng, Y., Wu, S., Yan, K., Verger, A., Liu, G., 2021. PLC-C:An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEEGeosci. Remote Sensing Lett. 18, 1446–1450. https://doi.org/10.1109/LGRS.2020.3001905

Zeng, Y.,Badgley, G., Chen, M., Li, J., Anderegg, L.D.L., Kornfeld, A., Liu, Q., Xu, B.,Yang, B., Yan, K., Berry, J.A.,2020a. A radiative transfer model for solar induced fluorescence using spectralinvariants theory. Remote Sensing of Environment 240, 111678. https://doi.org/10.1016/j.rse.2020.111678

Zeng, Y., Li,J., Liu, Q., Huete, A.R., Xu, B., Yin, G., Fan, W., Ouyang, Y., Yan, K., Hao, D., Chen, M., 2020b. ARadiative Transfer Model for Patchy Landscapes Based on Stochastic RadiativeTransfer Theory. IEEE Trans. Geosci. Remote Sensing 58, 2571–2589. https://doi.org/10.1109/TGRS.2019.2952377

张寅、闫凯*、刘钊、濮嘉彬、张一满、曾也鲁, 2020. 基于CRU数据的1901—2018年全球陆表气温时空变化特征分析. 首都师范大学学报：自然科学版 41, 8.

 

2019:

Chu, Q., Yan, G., Wild, M., Zhou,Y., Yan, K., Li, L., Liu, Y., Tong,Y., Mu, X., 2019. Ground-Based Radiation Observational Method in MountainousAreas, in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote SensingSymposium. Presented at the IGARSS 2019 - 2019 IEEE International Geoscienceand Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 8566–8569. https://doi.org/10.1109/IGARSS.2019.8900174

Yan, K.*, Tong, Y., Song, W., Zeng, Y., Liu, Z.,Mu, X., Yan, G., 2019. Analysis of the Kernel-DrivenBRDF Model Over Rugged Terrains, in: IGARSS 2019 - 2019 IEEE InternationalGeoscience and Remote Sensing Symposium. Presented at the IGARSS 2019 - 2019IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama,Japan, pp. 6807–6810. https://doi.org/10.1109/IGARSS.2019.8898377

 

2018:

Yan, G., Tong, Y., Yan, K.*, Mu, X., Chu, Q., Zhou, Y.,Liu, Y., Qi, J., Li, L., Zeng, Y., Zhou, H., Xie, D., Zhang, W., 2018. TemporalExtrapolation of Daily Downward Shortwave Radiation Over Cloud-Free RuggedTerrains. Part 1: Analysis of Topographic Effects. IEEE Trans. Geosci. RemoteSensing 56, 6375–6394. https://doi.org/10.1109/TGRS.2018.2838143

Yan, K., Park, T., Chen,C., Xu, B., Song, W., Yang, B., Zeng, Y., Liu, Z., Yan, G., Knyazikhin, Y.,Myneni, R.B., 2018. Generating Global Products of LAI and FPAR From SNPP-VIIRSData: Theoretical Background and Implementation. IEEE Trans. Geosci. RemoteSensing 56, 2119–2137. https://doi.org/10.1109/TGRS.2017.2775247

Chen,L., Mei, G., Yan, K., Hao, W., Yu,X., 2018. Species Discrimination of Plantations inSubtropical China Using 4-Band VHR Imagery and an Operational Image AnalysisFramework. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 11,2800–2813. https://doi.org/10.1109/JSTARS.2018.2837884

Li, L., Mu, X., Macfarlane, C., Song, W., Chen, J., Yan, K., Yan, G., 2018. A half-Gaussianfitting method for estimating fractional vegetation cover of corn crops usingunmanned aerial vehicle images. Agricultural and Forest Meteorology 262,379–390. https://doi.org/10.1016/j.agrformet.2018.07.028

Song, W.,Knyazikhin, Y., Wen, G., Marshak, A., Mõttus, M., Yan, K., Yang, B., Xu, B., Park, T., Chen, C., Zeng, Y., Yan, G.,Mu, X., Myneni, R.B., 2018. Implications of Whole-Disc DSCOVR EPIC SpectralObservations for Estimating Earth’s Spectral Reflectivity Based onLow-Earth-Orbiting and Geostationary Observations. Remote Sensing 10, 1594. https://doi.org/10.3390/rs10101594

Yin, G., Li, A., Wu, S., Fan, W., Zeng, Y., Yan, K., Xu, B., Li, J., Liu, Q., 2018. PLC:A simple and semi-physical topographic correction method for vegetationcanopies based on path length correction. Remote Sensing of Environment 215,184–198. https://doi.org/10.1016/j.rse.2018.06.009

Zeng, Y., Xu, B., Yin, G., Wu, S., Hu, G., Yan, K., Yang, B., Song, W., Li, J., 2018. SpectralInvariant Provides a Practical Modeling Approach for Future BiophysicalVariable Estimations. Remote Sensing 10, 1508. https://doi.org/10.3390/rs10101508

Zhou, Y., Yan,G., Zhao, J., Chu, Q., Liu, Y., Yan, K.,Tong, Y., Mu, X., Xie, D., Zhang, W., 2018. Estimation of Daily AverageDownward Shortwave Radiation over Antarctica. Remote Sensing 10, 422. https://doi.org/10.3390/rs10030422

 

2017:

Chen, C., Knyazikhin, Y., Park,T., Yan, K., Lyapustin, A., Wang,Y., Yang, B., Myneni, R., 2017. Prototyping of LAI and FPAR Retrievals from MODISMulti-Angle Implementation of Atmospheric Correction (MAIAC) Data. RemoteSensing 9, 370. https://doi.org/10.3390/rs9040370

Li, L., Yan, G.,Mu, X., Suhong, Liu, Chen, Y., Yan, K.,Luo, J., Song, W., 2017. Estimation of fractional vegetation cover usingmean-based spectral unmixing method, in: 2017 IEEE International Geoscience andRemote Sensing Symposium (IGARSS). Presented at the 2017 IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS), IEEE, Fort Worth, TX, pp.3178–3180. https://doi.org/10.1109/IGARSS.2017.8127672

Yang, B.,Knyazikhin, Y., Mõttus, M., Rautiainen, M., Stenberg, P., Yan, L., Chen, C., Yan, K., Choi, S., Park, T., Myneni,R.B., 2017. Estimation of leaf area index and its sunlit portion from DSCOVREPIC data: Theoretical basis. Remote Sensing of Environment 198, 69–84. https://doi.org/10.1016/j.rse.2017.05.033

 

2016:

Yan, K., Park, T., Yan, G., Chen, C.,Yang, B., Liu, Z., Nemani, R., Knyazikhin, Y., Myneni, R., 2016a. Evaluation ofMODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements.Remote Sensing 8, 359. https://doi.org/10.3390/rs8050359

Yan, K., Park, T., Yan,G., Liu, Z., Yang, B., Chen, C., Nemani, R., Knyazikhin, Y., Myneni, R., 2016b.Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation andIntercomparison. Remote Sensing 8, 460. https://doi.org/10.3390/rs8060460

Bi, J., Myneni, R., Lyapustin,A., Wang, Y., Park, T., Chi, C., Yan, K.,Knyazikhin, Y., 2016. Amazon Forests’ Response to Droughts: A Perspective fromthe MAIAC Product. Remote Sensing 8, 356. https://doi.org/10.3390/rs8040356

Yang, B.,Knyazikhin, Y., Lin, Y., Yan, K.,Chen, C., Park, T., Choi, S., Mõttus, M., Rautiainen, M., Myneni, R., Yan, L.,2016. Analyses of Impact of Needle Surface Properties on Estimation of NeedleAbsorption Spectrum: Case Study with Coniferous Needle and Shoot Samples.Remote Sensing 8, 563. https://doi.org/10.3390/rs8070563

Zeng, Y., Li,J., Liu, Q., Huete, A.R., Xu, B., Yin, G., Zhao, J., Yang, L., Fan, W., Wu, S.,Yan, K., 2016a. An Iterative BRDF/NDVIInversion Algorithm Based on A Posteriori Variance Estimation of ObservationErrors. IEEE Trans. Geosci. Remote Sensing 54, 6481–6496. https://doi.org/10.1109/TGRS.2016.2585301

Zeng, Y., Li, J., Liu, Q., Huete, A.R., Yin, G., Xu, B., Fan, W., Zhao, J.,Yan, K., Mu, X., 2016b. A Radiative Transfer Model for Heterogeneous Agro-ForestryScenarios. IEEE Trans. Geosci. Remote Sensing 54, 4613–4628. https://doi.org/10.1109/TGRS.2016.2547326

 

Before2015:

Chen, Y., Zhang, W., Yan, K., Li, X., Zhou, G., 2012.Extracting corn geometric structural parameters using Kinect, in: 2012 IEEEInternational Geoscience and Remote Sensing Symposium. Presented at the IGARSS2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium, IEEE,Munich, Germany, pp. 6673–6676. https://doi.org/10.1109/IGARSS.2012.6352068

Wang, H., Zhang,W., Chen, Y., Chen, M., Yan, K.,2015. Semantic Decomposition and Reconstruction of Compound Buildings withSymmetric Roofs from LiDAR Data and Aerial Imagery. Remote Sensing 7,13945–13974. https://doi.org/10.3390/rs71013945

Yan, G., Ren,H., Hu, R., Yan, K., Zhang, W.,2012. A portable Multi-Angle Observation System, in: 2012 IEEE InternationalGeoscience and Remote Sensing Symposium. Presented at the IGARSS 2012 - 2012IEEE International Geoscience and Remote Sensing Symposium, IEEE, Munich,Germany, pp. 6916–6919. https://doi.org/10.1109/IGARSS.2012.6352572

Yan, K.*, Ren, H., Hu, R., Mu, X., Liu, Z., Yan,G., 2013. Error analysis for emissivity measurementusing FTIR spectrometer, in: 2013 IEEE International Geoscience and RemoteSensing Symposium - IGARSS. Presented at the IGARSS 2013 - 2013 IEEEInternational Geoscience and Remote Sensing Symposium, IEEE, Melbourne,Australia, pp. 3080–3083. https://doi.org/10.1109/IGARSS.2013.6723477

Zhang, W., Wang,H., Chen, Y., Yan, K., Chen, M.,2014. 3D Building Roof Modeling by Optimizing Primitive’s Parameters UsingConstraints from LiDAR Data and Aerial Imagery. Remote Sensing 6, 8107–8133. https://doi.org/10.3390/rs6098107

植被是陆地碳储量的核心，气候变化驱动的植被动态具有渐变性和微弱性，对植被参数产品提出了长时序和高精度的要求，构建满足要求的数据产品是当前遥感科学领域的前沿和难点，是我国实现“双碳”目标自然解决方案的重要保证。在植被产品“拓时序-提精度-评性能”的总体框架下，开展了如下工作：

1、 创建了基于光谱不变理论的植被参数跨卫星反演理论与方法，实现了NASA MODIS、NOAA VIIRS、国产风云产品的有效链接，为碳循环研究提供了国产可控的长时序关键数据源。

2、研发了耦合时空关联信息的遥感再分析算法，实现了现有植被产品的云端实时优化与更新，为发展新一代高精度、时空无缝遥感产品提供了通用性方案。

3、 研发了卫星产品多维度评价技术，实现了植被参数“从模型到产品”的性能综合量化，为卫星产品国际评价标准的优化升级提供了科学依据。

第十四届九三学社北京市代表