Discussions on key issues of remote sensing-based vegetation phenology monitoring
- Pages: 1-12(2023)
Published Online： 19 December 2023
DOI： 10.11834/jrs.20233088
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
植被物候是陆地生态系统响应全球气候变化最灵敏的生物学指示指标，也是影响陆地生态系统碳循环的重要因子。因此，植被物候的准确监测对模拟陆地生态系统碳循环以及理解陆地生态系统响应气候变化至关重要。物候学发展至今，已有包括人工观测、物候相机观测和通量监测在内的地面观测以及遥感监测等多种植被物候监测手段，并已形成卫星遥感监测为主要手段、地面观测为验证的区域和全球尺度植被物候监测体系。植被物候遥感监测通用技术流程中的每个处理环节都会造成物候监测的不确定性。本文重点梳理了遥感时序数据、物候指标提取和遥感物候真实性检验三个物候监测关键环节，深入探讨了地表背景对遥感指数的干扰、不同物候指标提取方法的差异以及物候验证中遥感物候指标与参考物候指标之间的匹配问题。最后，探讨了解决这些关键问题的两个重要发展方向：（1）发展抗地表背景干扰的植被物候遥感监测方法，（2）构建结合地面多传感器协同观测和“天-空-地”多尺度一体化观测的联合观测网络。
Abstract
Vegetation phenology is the most sensitive biological indicator of terrestrial ecosystem responses to global climate change and plays a crucial role in the terrestrial carbon cycle. Therefore
accurate monitoring of vegetation phenology is essential for simulating the terrestrial carbon cycle and understanding how terrestrial ecosystems respond to climate change. To date
various monitoring methods for vegetation phenology have been developed
including ground-based observations (such as manual observation
phenocam observation
and carbon flux based monitoring) and remote sensing monitoring. A regional- and global-scale vegetation phenology monitoring system has been established
with satellite remote sensing as the primary method and ground observations for validation. Each processing step in the general technical workflow of remote sensing-based vegetation phenology monitoring introduces uncertainty into the monitored phenometrics. This paper focuses on three key aspects of phenology monitoring: remote sensing time-series data
phenometrics extraction
and phenometrics validation. Meanwhile
we deeply discuss the impact of background on remote sensing time-series data
the differences between various phenometrics extraction methods
and the matching issue between remote sensing phenometrics and reference phenometrics during validation. Finally
two essential directions to address these key issues are proposed: (1) developing remote sensing monitoring method of vegetation phenology against background interference
and (2) establishing a comprehensive observation network that integrates ground-based multi-sensor coordinated observation and "space-air-ground" multi-scale integrated observation.
关键词
植被物候遥感时序数据植被指数验证尺度效应
Keywords
vegetation phenologyremote sensing time-series datavegetation indexvalidationscale effect
references
Atkinson P M, Jeganathan C, Dash J, et al. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology[J]. Remote Sensing of Environment, 123: 400-417.
Aubinet M, Vesala T, Papale D. 2012. Eddy covariance: a practical guide to measurement and data analysis[M]. Dordrecht: Springer 438.
Badgley G, Field Christopher B, Berry Joseph A. 2017. Canopy near-infrared reflectance and terrestrial photosynthesis[J]. Science Advances, 3(3): e1602244.
Baldocchi D D, Black T A, Curtis P S, et al. 2005. Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data[J]. International Journal of Biometeorology, 49(6): 377-387.
Berra E F, Gaulton R. 2021. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics[J]. Forest Ecology and Management, 480: 118663.
Browning D M, Karl J W, Morin D, et al. 2017. Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem[J]. Remote Sensing, 9(10).
Burke M W V, Rundquist B C. 2021. Scaling Phenocam GCC, NDVI, and EVI2 with Harmonized Landsat-Sentinel using Gaussian Processes[J]. Agricultural and Forest Meteorology, 300: 108316.
Cao R, Feng Y, Liu X, et al. 2020. Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes[J]. Remote Sensing, 12(1).
Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, et al. 2021. A unified vegetation index for quantifying the terrestrial biosphere [J]. Science Advances, 7(9): eabc7447.
Caparros-Santiago J A, Rodriguez-Galiano V, Dash J. 2021. Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 171: 330-347.
Chu H, Luo X, Ouyang Z, et al. 2021. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites[J]. Agricultural and Forest Meteorology, 301-302: 108350.
Cleland E E, Chuine I, Menzel A, et al. 2007. Shifting plant phenology in response to global change[J]. Trends in Ecology & Evolution, 22(7): 357-365.
De Beurs K M, Henebry G M. 2004. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan[J]. Remote Sensing of Environment, 89(4): 497-509.
De Beurs K M, Henebry G M. 2010. Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology, Hudson I L, Keatley M R,editor, Phenological Research: Methods for Environmental and Climate Change Analysis[M], Dordrecht: Springer Netherlands, 177-208.
Dechant B, Ryu Y, Badgley G, et al. 2022. NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales[J]. Remote Sensing of Environment, 268: 112763.
Fan D Q, Zhao X S, Zhu W Q, et al. 2016. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 35(3): 304-319
范德芹, 赵学胜, 朱文泉, 等. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(03): 304-319.
Fang H L, Che T, Jin R, et al. 2021. On the construction of China's fiducial reference measurement network for land surface remote sensing product validation [J]. Advances in Earth Science, 36 (12): 1215-1223
方红亮, 车涛, 晋锐, 等. 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[J]. 地球科学进展, 36(12): 1215-1223.
Filippa G, Cremonese E, Migliavacca M, et al. 2016. Phenopix: A R package for image-based vegetation phenology[J]. Agricultural and Forest Meteorology, 220: 141-150.
Forrest J, Miller-Rushing A J. 2010. Toward a synthetic understanding of the role of phenology in ecology and evolution[J]. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 365(1555): 3101-3112.
Fu Y S, Li X X, Zhou X C, et al. 2020. Progress in plant phenology modeling under global climate change. Science China Earth Sciences, 63: 1237–1247
付永硕, 李昕熹, 周轩成, 等. 2020. 全球变化背景下的植物物候模型研究进展与展望 [J]. 中国科学: 地球科学, 50(09): 1206-1218.
Gan L, Cao X, Chen X, et al. 2020. Comparison of MODIS-based vegetation indices and methods for winter wheat green-up date detection in Huanghuai region of China[J]. Agricultural and Forest Meteorology, 288-289: 108019.
Gonsamo A, Chen J M, D’odorico P. 2013. Deriving land surface phenology indicators from CO2 eddy covariance measurements[J]. Ecological Indicators, 29: 203-207.
Gonsamo A, Chen J M, Price D T, et al. 2012a. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique[J]. Journal of Geophysical Research: Biogeosciences, 117(G3): G03032.
Gonsamo A, Chen J M, Wu C, et al. 2012b. Predicting deciduous forest carbon uptake phenology by upscaling FLUXNET measurements using remote sensing data[J]. Agricultural and Forest Meteorology, 165: 127-135.
Gu L, Post W M, Baldocchi D D, et al. 2009. Characterizing the seasonal dynamics of plant community photosynthesis across a range of vegetation types, Noormets A,editor, Phenology of Ecosystem Processes: Applications in Global Change Research[M], New York: Springer: 35-58.
Gao H L, Gu X F, Zhou X, et al. 2021. Framework Design of Remote Sensing Common Product Validation System [J]. Radio Engineering, 51( 12): 1389-1396
高海亮, 顾行发, 周翔, 等. 2021. 遥感共性产品真实性检验系统框架设计[J]. 无线电工程, 51(12): 1389-1396.
Ge Q S, Dai J H, Zheng J Y. 2010. The progress of phenology studies and challenges to modern phenology research in China [J]. Bulletin of Chinese Academy of Sciences, 25(03): 310-316
葛全胜, 戴君虎, 郑景云. 2010. 物候学研究进展及中国现代物候学面临的挑战 [J]. 中国科学院院刊, 25(03): 310-316.
Helman D. 2018. Land surface phenology: What do we really ‘see’ from space?[J]. Science of The Total Environment, 618: 665-673.
Jin H, Eklundh L. 2014. A physically based vegetation index for improved monitoring of plant phenology[J]. Remote Sensing of Environment, 152: 512-525.
Karkauskaite P, Tagesson T, Fensholt R. 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone[J]. Remote Sensing, 9(5), 485.
Klosterman S T, Hufkens K, Gray J M, et al. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery[J]. Biogeosciences, 11(16): 4305-4320.
Kong J, Ryu Y, Liu J, et al. 2022. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates[J]. Agricultural and Forest Meteorology, 316: 108878.
Kowalski K, Senf C, Hostert P, et al. 2020. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series[J]. International Journal of Applied Earth Observation and Geoinformation, 92: 102172.
Kross A S E, Roulet N T, Moore T R, et al. 2015. Phenology and its role in carbon dioxide exchange processes in northern peatlands[J]. Journal of Geophysical Research: Biogeosciences, 119(7): 1370-1384.
Lara B, Gandini M. 2016. Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland[J]. International Journal of Remote Sensing, 37(8): 1801-1813.
Li N, Zhan P, Pan Y, et al. 2020. Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau[J]. Remote Sensing, 12(20), 3383.
Li R P, Zhou G S, Yan Q L. 2005. Study on Plant Phenological Model [J]. Chinese Journal of Agrometeorology, 26(04): 8-12
李荣平, 周广胜, 阎巧玲. 2005. 植物物候模型研究[J]. 中国农业气象, 26(04): 8-12.
Li X, Zhu W, Xie Z, et al. 2021. Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation [J]. Remote Sensing, 13(24), 5018.
Liang L, Schwartz M D, Fei S. 2011. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest[J]. Remote Sensing of Environment, 115(1): 143-157.
Liu X T, Zhou L, Shi H, et al. 2018. Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyll fluorescence and CO2 flux data [J]. Acta Ecologica Sinica, 38(10): 3482-3494
刘啸添, 周蕾, 石浩, 等. 2018. 基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析 [J]. 生态学报, 38(10): 3482-3494.
Lloyd D. 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery [J]. International Journal of Remote Sensing, 11(12): 2269-2279.
Migliavacca M, Galvagno M, Cremonese E, et al. 2011. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake [J]. Agricultural and Forest Meteorology, 151(10): 1325-1337.
Pastorello G, Trotta C, Canfora E, et al. 2020. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data [J]. Scientific Data, 7(1): 225.
Peng D, Zhang X, Zhang B, et al. 2017. Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 132: 185-198.
Piao S, Cui M, Chen A, et al. 2011. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau [J]. Agricultural and Forest Meteorology, 151(12): 1599-1608.
Piao S, Liu Q, Chen A, et al. 2019. Plant phenology and global climate change: Current progresses and challenges[J]. Global Change Biology, 25(6): 1922-1940.
Piao S L, Zhang X Z, Wang T, et al. 2019. Responses and feedback of the Tibetan Plateau’s alpine ecosystem to climate change (in Chinese) [J]. Chinese Science Bulletin, 64: 2842–2855
朴世龙, 张宪洲, 汪涛, 等. 2019. 青藏高原生态系统对气候变化的响应及其反馈[J]. 科学通报, 64(27): 2842-2855.
Reed B C, Schwartz M D, Xiao X. 2009. Remote Sensing Phenology: Status and the Way Forward, Noormets A,editor, Phenology of Ecosystem Processes: Applications in Global Change Research [M], New York, NY: Springer New York, 231-246.
Richardson A D, Anderson R S, Arain M A, et al. 2012. Terrestrial biosphere models need better representation of vegetation phenology:Results from the North American Carbon Program Site Synthesis [J]. Global Change Biology, 18(2): 566–584.
Richardson A D, Black T A, Ciais P, et al. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity [J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1555): 3227-3246.
Richardson A D, Hollinger D Y, Dail D B, et al. 2009. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests [J]. Tree Physiology, 29(3): 321-331.
Richardson A D, Jenkins J P, Braswell B H, et al. 2007. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest [J]. Oecologia, 152(2): 323-334.
Richardson A D, Keenan T F, Migliavacca M, et al. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system [J]. Agricultural and Forest Meteorology, 169: 156-173.
Schwartz M D. 2013. Phenology: An Integrative Environmental Science [M]. Netherlands: Springer.
Shen M, Sun Z, Wang S, et al. 2013. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade [J]. Proceedings of the National Academy of Sciences, 110(26): E2329.
Sonnentag O, Hufkens K, Teshera-Sterne C, et al. 2012. Digital repeat photography for phenological research in forest ecosystems [J]. Agricultural and Forest Meteorology, 152: 159-177.
Sun L X, Zhu W Q, Xie Z Y, et al. 2022. Multi-dimension evaluation of remote sensing indices for land surface phenology monitoring [J]. National Remote Sensing Bulletin, 1-20
孙莉昕, 朱文泉, 谢志英, 等. 2022. 应用于陆表物候监测的遥感指数多维度评估[J]. 遥感学报, 1-20.
Studer S, Stöckli R, Appenzeller C, et al. 2007. A comparative study of satellite and ground-based phenology [J]. International Journal of Biometeorology, 51(5): 405-414.
Tan B, Morisette J, Wolfe R, et al. 2011. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4: 361-371.
Tian F, Cai Z, Jin H, et al. 2021. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe [J]. Remote Sensing of Environment, 260: 112456.
Walther G-R, Post E, Convey P, et al. 2002. Ecological responses to recent climate change [J]. Nature, 416(6879): 389-395.
Wang C, Chen J, Wu J, et al. 2017. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems [J]. Remote Sensing of Environment, 196: 1-12.
Wang C, Li J, Liu Q H, et al. 2017. Validation and analysis of remote sensing phenology products in the Heihe River Basin [J]. Journal of Remote Sensing, 21(3): 442–457
王聪, 李静, 柳钦火, 等. 2017. 黑河流域遥感物候产品验证与分析[J]. 遥感学报, 21(03): 442-457.
Wang M Y, Luo Y, Zhang Z Y, et al. 2022. Recent advances in remote sensing of vegetation phenology：Retrieval algorithm and validation strategy [J]. National Remote Sensing Bulletin，26(3)：431-455
王敏钰, 罗毅, 张正阳, 等. 2022. 植被物候参数遥感提取与验证方法研究进展[J]. 遥感学报, 26(03): 431-455.
White K, Pontius J, Schaberg P. 2014. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty [J]. Remote Sensing of Environment, 148: 97-107.
White M A, De Beurs K M, Didan K, et al. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 [J]. Global Change Biology, 15(8): 2335–2359.
White M A, Thornton P E, Running S W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability [J]. Global Biogeochem. Cycles, 11(2): 217-234.
Wu C, Chen J M. 2013. Deriving a new phenological indicator of interannual net carbon exchange in contrasting boreal deciduous and evergreen forests [J]. Ecological Indicators, 24: 113-119.
Wu C, Chen J M, Gonsamo A, et al. 2012. Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: Evidence from long records at two contrasting forest stands [J]. Agricultural and Forest Meteorology, 164: 29-38.
Wu C, Gough C M, Chen J M, et al. 2013. Evidence of autumn phenology control on annual net ecosystem productivity in two temperate deciduous forests [J]. Ecological Engineering, 60(11): 88-95.
Wu J, Albert L P, Lopes A P, et al. 2016. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests[J]. Science, 351(6276): 972-976.
Xia C F, Li J, Liu Q H. 2013. Review of advances in vegetation phenology monitoring by remote sensing [J]. Journal of Remote Sensing, 17(1): 1-16
夏传福, 李静, 柳钦火. 2013. 植被物候遥感监测研究进展[J]. 遥感学报, 17(1): 1-16.
Xiao X, Zhang J, Yan H, et al. 2009. Land surface phenology: Convergence of satellite and CO2 eddy flux observations, Noormets A,editor, Phenology of Ecosystem Processes: Applications in Global Change Research [M], New York, USA: Springer New York, 247-270.
Xie Z, Zhu W, He B, et al. 2022. A background-free phenology index for improved monitoring of vegetation phenology [J]. Agricultural and Forest Meteorology, 315: 108826.
Xie Z, Zhu W, Qiao K, et al. 2019. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands [J]. Ecophere, 10(5): e02733.
Yang L, Noormets A. 2021. Standardized flux seasonality metrics: a companion dataset for FLUXNET annual product [J]. Earth System Science Data, 13(4): 1461-1475.
Yang P, Van Der Tol C, Campbell P K E, et al. 2020. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence [J]. Remote Sensing of Environment, 240: 111676.
Yang W, Kobayashi H, Wang C, et al. 2019. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems [J]. Remote Sensing of Environment, 228: 31-44.
Yu H, Luedeling E, Xu J. 2010. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau [J]. Proceedings of the National Academy of Sciences, 107(51): 22151.
Yu G R, Zhang L M, Sun X M, et al. 2004. Advances in carbon flux observation and research in Asia [J]. Science in China Ser. D Earth Sciences, (S2): 15-29
于贵瑞, 张雷明, 孙晓敏, 等. 2004. 亚洲区域陆地生态系统碳通量观测研究进展[J]. 中国科学(D辑: 地球科学), (S2): 15-29.
Zeng L, Wardlow B D, Xiang D, et al. 2020. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data [J]. Remote Sensing of Environment, 237: 111511.
Zeng Y, Hao D, Huete A, et al. 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally [J]. Nature Reviews Earth & Environment, 3(7): 477-493.
Zhang G, Zhang Y, Dong J, et al. 2013. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 [J]. Proceedings of the National Academy of Sciences, 110(11): 4309.
Zhang H, Shen S H, Wen X F, et al. 2012. Flux footprint of carbon dioxide and vapor exchange over the terrestrial ecosystem: a review and perspective [J]. Acta Ecologica Sinica, 32(23): 7622-7633
张慧, 申双和, 温学发, 等. 2012. 陆地生态系统碳水通量贡献区评价综述[J]. 生态学报, 32(23): 7622-7633.
Zhang X. 2015. Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data [J]. Remote Sensing of Environment, 156: 457-472.
Zhang X, Friedl M A, Schaaf C B, et al. 2003. Monitoring vegetation phenology using MODIS [J]. Remote Sensing of Environment 84(3): 471-475.
Zhou G S, Song X Y, Zhou M Z, et al. 2023. Advances in influencing mechanism and model of total climatic production factors of plant phenology change (in Chinese) [J]. SCIENTIA SINICA Vitae, 53: 380–389
周广胜, 宋兴阳, 周梦子, 等. 2023. 植物物候变化的全气候生产要素影响机制与模型研究[J]. 中国科学:生命科学, 53(03): 380-389.
Zhu W, Chen G, Jiang N, et al. 2013. Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: A synthesis of ameriflux observations [J]. Plos One, 8(12): e84990.
Zhao J J, Liu L Y. 2013. Extraction of temperate vegetation phenology thresholds in North America based on flux tower observation data [J]. Chinese Journal of Applied Ecology, 24(2): 311-318
赵晶晶, 刘良云. 2013. 基于通量塔观测资料的北美温带植被物候阈值提取方法[J]. 应用生态学报, 24(2): 311-318.
Zuo L, Wang H J, Liu R G, et al. 2018. Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices [J]. Chinese Journal of Applied Ecology, 29(02): 599-606
左璐, 王焕炯, 刘荣高, 等. 2018. 基于不同光谱指数的植被物候期遥感监测差异[J]. 应用生态学报, 29(02): 599-606.
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
First Institute of Oceanography, Ministry of Natural Resources
College of Computer Science and Engineering, Shandong University of Science and Technology
School of Geosciences and Info-physics, Central South University
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP