扫 描 看 全 文
Quote
杨磊库，胡秀清，王涵，何兴伟，刘培，徐娜，杨忠东，张鹏.2022.风云三号D星MERSI-II陆地气溶胶反演定量能力评估.遥感学报，26（5）： 923-940
Yang L K，Hu X Q，Wang H，He X W，Liu P，Xu N，Yang Z D and Zhang P. 2022. Preliminary test of quantitative capability in aerosol retrieval over land from MERSI-II onboard FY-3D. National Remote Sensing Bulletin， 26（5）：923-940
风云三号卫星搭载的中分辨率光谱成像仪MERSI（MEdium-Resolution Spectral Imager）与NASA的MODIS属于同类型传感器，大多数通道设计相似，具备气溶胶反演的能力，但目前还没有精度可靠、稳定且全球适用的业务化气溶胶产品可供使用。本文借鉴MODIS暗目标DT（Dark target）算法思想，针对新发射的风云三号D星搭载的升级版MERSI-II传感器，构建全球适用的陆地气溶胶反演算法。为测试MERSI-II传感器气溶胶反演的定量能力，并与MODIS气溶胶产品具有可对比性，本文算法设计尽可能与DT算法在各方面处理相一致，主要在两个方面进行了改进与优化：（1）考虑到传感器波段设置差异，给出了针对MERSI-II的地表反射率估计模型；（2）改进内陆水体判别方法，解决DT算法雾霾漏反演的缺陷。通过将单景反演结果与MODIS气溶胶产品对比，反演结果的空间分布和值域大小均具有较好的一致性，相关系数达到0.9以上；有效地实现雾霾高值区反演，而MODIS产品在该区域漏反演。通过对3个月份全球数据反演试验，利用AERONET地基站网观测数据对结果进行验证，落入期望误差EE=±（0.05+0.15τ）范围的点数达到65.14%，接近2/3的要求，相关系数达到0.866，整体具有较高的验证精度；MERSI幅宽大于MODIS，加之本文像元掩膜的改进，使得MERSI反演像元数增加更多，匹配的验证点比例较MODIS高出20%左右。进一步将月平均结果与MODIS进行对比，空间分布一致性较好，反演结果大小对比相关性较好，相关系数达到0.93左右。表明本文针对MERSI-II气溶胶反演算法结果已接近国际同类型产品的精度要求，将为全球长时间序列气溶胶观测有提供重要补充。从而印证了MERSI观测数据已具有较好的定量应用能力，传感器性能、定标等正在逐渐走向成熟。
The MEdium-Resolution Spectral Imager (MERSI) carried by the Chinese Fengyun-3 (FY-3) satellite belongs to the same type of sensor as MODIS of NASA. Most channels of MERSI are similar in design as MODIS and are capable for aerosol retrieval. However, no reliable, stable, and globally applicable operational products are available for MERSI.,On the basis of MODIS Dark Target (DT) algorithm, this paper constructs a globally applicable land aerosol retrieval algorithm for the new generation MERSI-II sensor onboard the newly launched FY-3D satellite. The aim of this paper is to test the quantitative capability of the sensor; hence, the algorithm design is consistent with DT algorithm as much as possible. The improvements of this algorithm from the MODIS DT algorithm are mainly in two aspects: surface estimation model and pixel screen. Considering the difference in channel settings between MERSI and MODIS, a surface reflectance estimation model is proposed for MERSI-II. Moreover, the method of inland water mask is improved to solve the defect of DT algorithm in haze leakage retrieval.,By comparing granule retrieval of MERSI-II with aerosol products of MODIS, the spatial distribution and magnitude of AOD value show good consistency with a correlation coefficient above 0.9. After improving the identification method of inland water mask, the high aerosol loading of haze region which was missing in MODIS aerosol product, has been successfully retrieved from MERSI-II in this paper. Finally, we conducted the retrieval test with three-month global observation of MERSI-II. A comparison of the retrieval results with ground-based observation of AERONET shows that the overall accuracy of validation is good, and the correlation coefficient of scatterplots reaches 0.866. Moreover, the number of collocated points falling into the expected error EE = ± (0.05 + 0.15 τ) reaches 65.14%, which is close to the requirement of 2/3. The larger width of MERSI, in addition to its improvement of pixel mask, increases the number of MERSI-retrieved pixels. The proportion of matched collocation points is approximately 20% more than that of MODIS. Furthermore, a comparison between the monthly average results of MERSI-II and MODIS shows that the consistency of their spatial distribution is good. The magnitude of AOD value has good correlation with a coefficient of approximately 0.93.,In conclusion, aerosol retrieval from MERSI-II using the proposed algorithm is close to the present similar products. Such performance is an important supplement to the global time series aerosol observation. Therefore, MERSI has good quantitative application ability, and the sensor performance and calibration are gradually becoming mature.
风云三号中分辨率光谱成像仪气溶胶光学厚度暗目标算法雾霾内陆水体掩膜地表估计地基验证
FY-3MERSIaerosol optical depthDark Target (DT)hazeinland water masksurface reflectance estimationground-based validation
Choi M, Kim J, Lee J, Kim M, Park Y J, Holben B, Eck T F, Li Z Q and Song C H. 2018. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. Atmospheric Measurement Techniques, 11(1): 385-408 [DOI: 10.5194/amt-11-385-2018http://dx.doi.org/10.5194/amt-11-385-2018]
Dong C H, Yang J, Lu N M, Yang Z D, Shi J M, Zhang P, Liu Y J and Cai B. 2010. Main characteristics and primary applications of polar-orbiting satellite FY-3A. Journal of Geo-Information Science, 12(4): 458-465
董超华, 杨军, 卢乃锰, 杨忠东, 施进明, 张鹏, 刘玉洁, 蔡斌. 2010. 风云三号A星(FY-3A)的主要性能与应用. 地球信息科学学报, 12(4): 458-465 [DOI: 10.3724/SP.J.1047.2010.00458http://dx.doi.org/10.3724/SP.J.1047.2010.00458]
Dubovik O, Herman M, Holdak A, Lapyonok T, Tanré D, Deuzé J L, Ducos F, Sinyuk A and Lopatin A. 2011. Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations. Atmospheric Measurement Techniques, 4(5): 975-1018 [DOI: 10.5194/amt-4-975-2011http://dx.doi.org/10.5194/amt-4-975-2011]
Dubovik O, Holben B N, Lapyonok T, Sinyuk A, Mishchenko M I, Yang P and Slutsker I. 2002. Non-spherical aerosol retrieval method employing light scattering by spheroids. Geophysical Research Letters, 29(10): 54-1-54-4 [DOI: 10.1029/2001gl014506http://dx.doi.org/10.1029/2001gl014506]
Dubovik O, Li Z Q, Mishchenko M I, Tanré D, Karol Y, Bojkov B, Cairns B, Diner D J, Espinosa W R, Goloub P, Gu X F, Hasekamp O, Hong J, Hou W Z, Knobelspiesse K D, Landgraf J, Li L, Litvinov P, Liu Y, Lopatin A, Marbach T, Maring H, Martins V, Meijer Y, Milinevsky G, Mukai S, Parol F, Qiao Y L, Remer L, Rietjens J, Sano I, Stammes P, Stamnes S, Sun X B, Tabary P, Travis L D, Waquet F, Xu F, Yan C X and Yin D K. 2019. Polarimetric remote sensing of atmospheric aerosols: instruments, methodologies, results, and perspectives. Journal of Quantitative Spectroscopy and Radiative Transfer, 224: 474-511 [DOI: 10.1016/j.jqsrt.2018.11.024http://dx.doi.org/10.1016/j.jqsrt.2018.11.024]
Eck T F, Holben B N, Reid J S, Dubovik O, Smirnov A, O'Neill N T, Slutsker I and Kinne S. 1999. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. Journal of Geophysical Research: Atmospheres, 104(D24): 31333-31349 [DOI: 10.1029/1999jd900923http://dx.doi.org/10.1029/1999jd900923]
Garay M J, Witek M L, Kahn R A, Seidel F C, Limbacher J A, Bull M A, Diner D J, Hansen E G, Kalashnikova O V, Lee H, Nastan A M and Yu Y. 2020. Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product. Atmospheric Measurement Techniques, 13(2): 593-628 [DOI: 10.5194/amt-13-593-2020http://dx.doi.org/10.5194/amt-13-593-2020]
Ge B Y, Yang L K, Chen X F, Li Z Q, Mei X D and Liu L. 2018. Study on aerosol optical depth retrieval over land from Himawari-8 data based on dark target method. Journal of Remote Sensing, 22(1): 38-50
葛邦宇, 杨磊库, 陈兴峰, 李正强, 梅笑冬, 刘李. 2018. 暗目标法的Himawari-8静止卫星数据气溶胶反演. 遥感学报, 22(1): 38-50 [DOI: 10.11834/jrs.20187033http://dx.doi.org/10.11834/jrs.20187033]
Guo J P, Liu H, Wang F, Huang J F, Xia F, Lou M Y, Wu Y R, Jiang J H, Xie T, Zhaxi Y Z and Yung Y L. 2016. Three-dimensional structure of aerosol in China: a perspective from multi-satellite observations. Atmospheric Research, 178-179: 580-589 [DOI: 10.1016/j.atmosres.2016.05.010http://dx.doi.org/10.1016/j.atmosres.2016.05.010]
Gupta P, Levy R C, Mattoo S, Remer L A and Munchak L A. 2016. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm. Atmospheric Measurement Techniques, 9(7): 3293-3308 [DOI: 10.5194/amt-9-3293-2016http://dx.doi.org/10.5194/amt-9-3293-2016]
Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C. 2013. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296-9315 [DOI: 10.1002/jgrd.50712http://dx.doi.org/10.1002/jgrd.50712]
Hsu N C, Lee J, Sayer A M, Kim W, Bettenhausen C and Tsay S C. 2019. VIIRS Deep Blue aerosol products over land: extending the EOS long-term aerosol data records. Journal of Geophysical Research: Atmospheres, 124(7): 4026-4053 [DOI: 10.1029/2018jd029688http://dx.doi.org/10.1029/2018jd029688]
Hu X Q, Niu X H, Xu N, Wu R H, Ding L, Yang Z D and Zhang P. 2018. Improvement and application ability promotion of the new type MEdium Resolution Spectral Imager (MERSI-II) onboard FY-3d//The 35th Annual Meeting of China Meteorological Society. Hefei
胡秀清, 钮新华, 徐娜, 吴荣华, 丁雷, 杨忠东, 张鹏. 2018. FY-3D中分辨率光谱成像仪Ⅱ型仪器改进及应用能力提升//第35届中国气象学会年会. 合肥
IPCC. 2013. Climate change 2013: the physical science basis//Stocker T F, Qin D, Plattner G K, Tignor M, Allen S K, Boschung J, Nauels A, Xia Y, Bex V and Midgley P M, eds. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press
Jackson J M, Liu H Q, Laszlo I, Kondragunta S, Remer L A, Huang J F and Huang H C. 2013. Suomi-NPP VIIRS aerosol algorithms and data products. Journal of Geophysical Research: Atmospheres, 118(22): 12673-12689 [DOI: 10.1002/2013jd020449http://dx.doi.org/10.1002/2013jd020449]
Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067 [DOI: 10.1029/96JD03988http://dx.doi.org/10.1029/96JD03988]
Kokhanovsky A A. 2013. Remote sensing of atmospheric aerosol using spaceborne optical observations. Earth-Science Reviews, 116: 95-108 [DOI: 10.1016/j.earscirev.2012.10.008http://dx.doi.org/10.1016/j.earscirev.2012.10.008]
Kokhanovsky A A and De Leeuw G. 2009. Satellite Aerosol Remote Sensing Over Land. Berlin Heidelberg: Springer [DOI: 10.1007/978-3-540-69397-0http://dx.doi.org/10.1007/978-3-540-69397-0]
Lenoble J, Remer L and Tanré D. 2013. Aerosol Remote Sensing. Berlin Heidelberg: Springer [DOI: 10.1007/978-3-642-17725-5http://dx.doi.org/10.1007/978-3-642-17725-5]
Levy R C, Mattoo S, Munchak L A, Remer L A, Sayer A M, Patadia F and Hsu N C. 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11): 2989-3034 [DOI: 10.5194/amt-6-2989-2013http://dx.doi.org/10.5194/amt-6-2989-2013]
Levy R C, Munchak L A, Mattoo S, Patadia F, Remer L A and Holz R E. 2015. Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance. Atmospheric Measurement Techniques, 8(10): 4083-4110 [DOI: 10.5194/amt-8-4083-2015http://dx.doi.org/10.5194/amt-8-4083-2015]
Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420 [DOI: DOI 10.5194/acp-10-10399-2010http://dx.doi.org/DOI10.5194/acp-10-10399-2010]
Levy R C, Remer L A, Mattoo S, Vermote E F and Kaufman Y J. 2007. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112(D13): D13211 [DOI: 10.1029/2006jd007811http://dx.doi.org/10.1029/2006jd007811]
Li Z Q, Guo J P, Ding A J, Liao H, Liu J J, Sun Y L, Wang T J, Xue H W, Zhang H S and Zhu B. 2017. Aerosol and boundary-layer interactions and impact on air quality. National Science Review, 4(6): 810-833 [DOI: 10.1093/nsr/nwx117http://dx.doi.org/10.1093/nsr/nwx117]
Li Z Q, Hou W Z, Hong J, Zheng F X, Luo D G, Wang J, Gu X F and Qiao Y L. 2018. Directional Polarimetric Camera (DPC): monitoring aerosol spectral optical properties over land from satellite observation. Journal of Quantitative Spectroscopy and Radiative Transfer, 218: 21-37 [DOI: 10.1016/j.jqsrt.2018.07.003http://dx.doi.org/10.1016/j.jqsrt.2018.07.003]
Lu N M and Gu S Y. 2016. Review and prospect on the development of meteorological satellites. Journal of Remote Sensing, 20(5): 832-841
卢乃锰, 谷松岩. 2016. 气象卫星发展回顾与展望. 遥感学报, 20(5): 832-841 [DOI: 10.11834/jrs.20166194http://dx.doi.org/10.11834/jrs.20166194]
Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765 [DOI: 10.5194/amt-11-5741-2018http://dx.doi.org/10.5194/amt-11-5741-2018]
Lyapustin A, Wang Y, Laszlo I, Kahn R, Korkin S, Remer L, Levy R and Reid J S. 2011. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. Journal of Geophysical Research: Atmospheres, 116(D3): D03211 [DOI: 10.1029/2010jd014986http://dx.doi.org/10.1029/2010jd014986]
Mei L L, Rozanov V, Vountas M, Burrows J P and Richter A. 2018. XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation. Atmospheric Measurement Techniques, 18(4): 2511-2523 [DOI: 10.5194/acp-18-2511-2018http://dx.doi.org/10.5194/acp-18-2511-2018]
Munchak L A, Levy R C and Mattoo S. 2014. Global and regional validation of the Collection 6 MODIS dark target aerosol products, and comparison to Collection 5//AGU Fall Meeting. San Francisco, CA, United States: American Geophysical Union
Patadia F, Levy R C and Mattoo S. 2018. Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation. Atmospheric Measurement Techniques, 11(6): 3205-3219 [DOI: 10.5194/amt-11-3205-2018http://dx.doi.org/10.5194/amt-11-3205-2018]
Popp T, de Leeuw G, Bingen C, Brühl C, Capelle V, Chedin A, Clarisse L, Dubovik O, Grainger R, Griesfeller J, Heckel A, Kinne S, Klüser L, Kosmale M, Kolmonen P, Lelli L, Litvinov P, Mei L L, North P, Pinnock S, Povey A, Robert C, Schulz M, Sogacheva L, Stebel K, Zweers D S, Thomas G, Tilstra L G, Vandenbussche S, Veefkind P, Vountas M and Xue Y. 2016. Development, production and evaluation of aerosol climate data records from European satellite observations (Aerosol_cci). Remote Sensing, 8(5): 421 [DOI: 10.3390/rs8050421http://dx.doi.org/10.3390/rs8050421]
Sawyer V, Levy R C, Mattoo S, Cureton G, Shi Y X and Remer L A. 2020. Continuing the MODIS Dark target aerosol time series with VIIRS. Remote Sensing, 12(2): 308 [DOI: 10.3390/rs12020308http://dx.doi.org/10.3390/rs12020308]
Sayer A M, Hsu N C, Bettenhausen C and Jeong M J. 2013. Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research: Atmospheres, 118(14): 7864-7872 [DOI: 10.1002/jgrd.50600http://dx.doi.org/10.1002/jgrd.50600]
Sayer A M, Hsu N C, Lee J, Kim W V and Dutcher S T. 2019. Validation, stability, and consistency of MODIS Collection 6.1 and VIIRS version 1 deep blue aerosol data over land. Journal of Geophysical Research: Atmospheres, 124(8): 4658-4688 [DOI: 10.1029/2018jd029598http://dx.doi.org/10.1029/2018jd029598]
Sinyuk A, Holben B N, Eck T F, Giles D M, Slutsker I, Korkin S, Schafer J S, Smirnov A, Sorokin M and Lyapustin A. 2020. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2. Atmospheric Measurement Techniques, 13(6): 3375-3411 [DOI: 10.5194/amt-13-3375-2020http://dx.doi.org/10.5194/amt-13-3375-2020]
Su C L, Su L, Chen L F, Jia S L, Liu C and Yu C. 2015. Retrieval of aerosol optical depth using NPP VIIRS data. Journal of Remote Sensing, 19(6): 977-982
苏城林, 苏林, 陈良富, 贾松林, 刘聪, 余超. 2015. NPP VIIRS数据反演气溶胶光学厚度. 遥感学报, 19(6): 977-982 [DOI: 10.11834/jrs.20155051http://dx.doi.org/10.11834/jrs.20155051]
Tan Y H, Li E G, Zhang Z Y, Lin X W, Chi Y G, Zhou L, Wu C F and Wang Q. 2019. Validation of POLDER-3/GRASP aerosol products using AERONET measurements over China. Atmospheric Environment, 215: 116893 [DOI: 10.1016/j.atmosenv.2019.116893http://dx.doi.org/10.1016/j.atmosenv.2019.116893]
Tang S H, Qiu H and Ma G. 2016. Review on progress of the Fengyun meteorological satellite. Journal of Remote Sensing, 20(5): 842-849
唐世浩, 邱红, 马刚. 2016. 风云气象卫星主要技术进展. 遥感学报, 20(5): 842-849 [DOI: 10.11834/jrs.20166232http://dx.doi.org/10.11834/jrs.20166232]
Tao M H, Chen L F, Wang Z F, Wang J, Che H Z, Xu X G, Wang W C, Tao J H, Zhu H and Hou C. 2017. Evaluation of MODIS deep blue aerosol algorithm in desert region of East Asia: ground validation and intercomparison. Journal of Geophysical Research: Atmospheres, 122(19): 10357-10368 [DOI: 10.1002/2017jd026976http://dx.doi.org/10.1002/2017jd026976]
Wei Y Y, Li Z Q, Zhang Y, Chen C, Dubovik O, Zhang Y, Xu H, Li K T, Chen J, Wang H F, Ge B Y and Fan C. 2020. Validation of POLDER GRASP aerosol optical retrieval over China using SONET observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 246: 106931 [DOI: 10.1016/j.jqsrt.2020.106931http://dx.doi.org/10.1016/j.jqsrt.2020.106931]
Xu N. 2019. FY-3 (batch 02) meteorological satellite ground application system project: User guide for MERSI. National Satellite Meteorological Center
徐娜. 2019. 风云三号(02 批)气象卫星地面应用系统工程D星中分辨率光谱成像仪数据使用指南. 国家卫星气象中心
Xu N, Niu X H, Hu X Q, Wang X H, Wu R H, Chen S S, Chen L, Sun L, Ding L, Yang Z D and Zhang P. 2018. Prelaunch calibration and radiometric performance of the advanced MERSI II on FengYun-3D. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4866-4875 [DOI: 10.1109/tgrs.2018.2841827http://dx.doi.org/10.1109/tgrs.2018.2841827]
Yang Z D, Lu N M, Shi J M, Zhang P, Dong C H and Yang J. 2013. Overview of FY-3 payload and ground application system. Advances in Meteorological Science and Technology, 3(4): 6-12
杨忠东, 卢乃锰, 施进明, 张鹏, 董超华, 杨军. 2013. 风云三号卫星有效载荷与地面应用系统概述. 气象科技进展, 3(4): 6-12 [DOI: 10.3969/j.issn.2095-1973.2013.04.001http://dx.doi.org/10.3969/j.issn.2095-1973.2013.04.001]
Yang Z D, Zhang P, Gu S Y, Zhu A J, Hu X Q and Yang J. 2017. Application and development of FY-3 meteorological satellite. Aerospace Shanghai, 34(4): 1-7
杨忠东, 张鹏, 谷松岩, 朱爱军, 胡秀清, 杨军. 2017. FY-3卫星应用和发展. 上海航天, 34(4): 1-7 [DOI: 10.19328/j.cnki.1006-1630.2017.04.001http://dx.doi.org/10.19328/j.cnki.1006-1630.2017.04.001]
Yang Z D, Zhang P, Gu S Y, Hu X Q, Tang S H, Yang L K, Xu N, Zhen Z J, Wang L, Wu Q, Dou F L, Liu R X, Wu X, Zhu L, Zhang L Y, Wang S J, Sun Y Q and Bai W H. 2019. Capability of Fengyun-3D satellite in earth system observation. Journal of Meteorological Research, 33(6): 1113-1130 [DOI: 10.1007/s13351-019-9063-4http://dx.doi.org/10.1007/s13351-019-9063-4]
Zhang P, Lu Q F, Hu X Q, Gu S Y, Yang L, Min M, Chen L, Xu N, Sun L, Bai W G, Ma G and Xian D. 2019. Latest progress of the Chinese meteorological satellite program and core data processing technologies. Advances in Atmospheric Sciences, 36(9): 1027-1045 [DOI: 10.1007/s00376-019-8215-xhttp://dx.doi.org/10.1007/s00376-019-8215-x]
Zhang P, Yang H, Qiu H, Ma G, Yang Z D, Lu N M and Yang J. 2012. Quantitative remote sensing from the current Fengyun 3 satellites. Advances in Meteorological Science and Technology, 2(4): 6-11
张鹏, 杨虎, 邱红, 马刚, 杨忠东, 卢乃锰, 杨军. 2012. 风云三号卫星的定量遥感应用能力. 气象科技进展, 2(4): 6-11 [DOI: 10.3969/j.issn.2095-1973.2012.04.001http://dx.doi.org/10.3969/j.issn.2095-1973.2012.04.001]
相关文章
相关作者
相关机构
京公网安备11010802024621