Quality evaluation and optimization of global microwave land surface emissivity products
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摘要: 全球微波陆表发射率（MLSE）地图集是目前卫星资料同化观测算子中的重要初猜值或模拟值，但由于缺乏“真实”的观测MLSE，已有的基于多种微波传感器制作的全球MLSE产品质量可靠性未知，而且还缺少1套质量较好频率覆盖范围较宽的全球MLSE数据集。选择大气快速辐射传输模型（RTTOV）中使用的3套MLSE地图集（SSMI/S、AMSU-A/B、ATMS）、TELSEM2工具背景数据集、2套AMSR-E数据集（AMSR-E1和AMSR-E2）和1套FY-3D 数据集，基于统计分析技术开展7套MLSE产品的全球时、空一致性评估，并选择6种典型土地覆盖类型开展各产品MLSE随土地覆盖类型和频率变化一致性评估，并在MLSE产品优选的基础上对TELSEM2发射率产品进行优化，新建1套频率为6.9—150.0 GHz的全球MLSE数据集（CoTELSEM2）。研究结果显示：AMSR-E2几乎不可用，AMSR-E1、TELSEM2、SSMI/S、AMSU-A/B、ATMS、FY3D月MLSE之间具有较好的时、空一致性，平均空间相关系数为0.887—0.928，其中TELSEM2最高（0.928），FY-3D略低（0.914）；平均绝对偏差为0.031—0.041，其中TELSEM2最低（0.031），FY-3D最高（0.041）；传感器相同扫描方式较不同扫描方式的空间一致性更好，圆锥和跨轨扫描方式分别以TELSEM2和AMSU-A表现更好；ATMS的51.7 GHz MLSE存在系统性高估，AMSR-E1和FY-3D的23.8、89.0 GHz MLSE在高植被覆盖地区也存在系统性高估，而FY-3D MLSE则存在一些明显偏高或偏低问题。总体上，TELSEM2和AMSU-A/B的质量可靠性较高，FY-3D质量可靠性较差。新的CoTELSEM2发射率产品具有较好的时、空一致性和频率依赖一致性，且全球MLSE的季节变化不确定性存在明显的土地覆盖类型依赖特征。Abstract: The global microwave land surface emissivity (MLSE) atlas is currently an important initial guess or simulation value in satellite data assimilation observation operators. However, due to the lack of "real" observation of MLSE, the quality and reliability of existing global MLSE products based on multiple microwave sensors are unknown, and there is also a lack of global MLSE datasets with good quality and wide frequency coverage. This study selects three sets of MLSE atlases (SSMI/S, AMSU-A/B, ATMS) used in RTTOV (Radiative Transfer for TOVS) as well as the background dataset included in the TELSEM2 MLSE estimation tool, two sets of AMSR-E datasets (AMSR-E1 and AMSR-E2), and one set of FY-3D dataset. Based on statistical analysis techniques, global spatiotemporal consistency of the seven MLSE products is evaluated. Furthermore, six typical land cover types (Amazon tropical rainforest, Northern coniferous forest, North China plain farmland, Qingzang plateau grassland, Sahara desert, and Greenland ice cover) are selected to conduct a dependency assessment for each product based on land cover type and frequency. Based on the selection of emissivity products, the TELSEM2 emissivity products are optimized, and a new set of global MLSE data with a frequency coverage of 6.925—150.0 GHz is created (named CoTELSEM2). The results show that there is a significant inversion error in the AMSR-E2 product, making it almost unusable. The MLSEs on the monthly scale of AMSR-E1, TELSEM2, SSMI/S, AMSU-A/B, ATMS and FY-3D have a good spatiotemporal consistency with an average spatial correlation coefficient (R) of 0.887—0.928, with TELSEM2 having the highest value (0.928) and FY-3D having a slightly lower value (0.914). The mean absolute deviation (MAD) is 0.031—0.037, with TELSEM2 having the lowest (0.031) and FY-3D having the highest (0.041). The spatial consistency is better with the same scanning method than with different scanning methods. The cone and cross-track scanning methods perform better with TELSEM2 and AMSU-A/B products, respectively. ATMS 51.7 GHz channel MLSE has a systematic overestimation, and 23.8 GHz and 89.0 GHz channel MLSE for AMSR-E1 and FY-3D products also have a systematic overestimation in areas with high vegetation coverage, while FY-3D MLSE shows significant overestimation or underestimation at different times, regions, and frequencies. The seasonal variability uncertainty of MLSE increases with increasing frequency. Little uncertainty is found in the emissivity of the Sahara and Amazon tropical rainforests, while the uncertainty in the northern coniferous forest is the greatest. Overall, TELSEM2 and AMSU-A/B have higher quality reliability, followed by SMMI/S and ATMS, and AMSR-E1 and FY-3D have relatively poor quality reliability. The optimized new CoTELSEM2 emissivity product shows a good dependence on land cover type and frequency, while the uncertainty of global MLSE seasonal change has a strong dependence on land cover type, of which the uncertainty of Sahara Desert and Amazon tropical rainforest emissivity is very small, while the uncertainty of northern coniferous forest is the largest, and the high frequency is significantly greater than the low frequency.
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Key words:
- Microwave land surface emissivity /
- TELSEM2 /
- FY3D /
- Quality assessment /
- Data reconstruction
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图 1 全球土地覆盖类型典型样区选择示意
Figure 1. Schematic diagram of typical sample areas selection for global land cover types
图 2 不同产品不同频率1月 （a1—f1、a3—f3） 和7月 （a2—f2、a4—f4） 发射率空间分布 （a. AMSR-E1，b. AMSR-E2，c. TELSEM2，d. SSMI/S，e. FY-3D，f. AMSU-A （ATMS）；a1—a2、b1—b2、e1—e2. 18.7 GHz，c1—c2、d1—d2. 19.35 GHz，c3—c4. 85.0 GHz，a3—a4、b3—b4、e3—e4、f1—f4. 89.0 GHz，d3—d4. 91.65 GHz）
Figure 2. Spatial distributions of emissivity for different products in typical periods （a1—f1，a3—f3. January；a2—f2，a4—f4. July） （a. AMSR-E1，b. AMSR-E2，c. TELSEM2，d. SSMI/S，e. FY-3D，f. AMSU-A （ATMS）；a1—a2，b1—b2，e1—e2. 18.7 GHz，c1—c2，d1—d2. 19.35 GHz，c3—c4. 85.0 GHz，a3—a4，b3—b4，e3—e4，f1—f4. 89.0 GHz，d3—d4. 91.65 GHz）
图 3 国外产品不同月份不同频率的MLSE空间相关系数 （a、c、e、g） 和平均绝对偏差 （b、d、f、h） （a、b. 18.7或19.35 GHz，c、d. 23.8 GHz，e、f. 36.5或37.0 GHz，g、h. 85.0、89.0或91.65 GHz；n为评估样本数）
Figure 3. Comparison of R （a，c，e，g） and MAD （b，d，f，h） between foreign sensors for different months and frequencies（a，b. 18.7or 19.35 GHz，c，d. 23.8 GHz，e，f. 36.5 or 37.0 GHz，g，h. 85.0，89.0 or 91.65 GHz；n is the number of samples）
图 4 不同MLSE产品的平均空间相关系数 （a） 和平均绝对偏差 （b）
Figure 4. Comparison of average R （a） and MAD （b） between different MLSE products including all scanning methods，the same scanning method，and different scanning methods
图 5 三种发射率产品不同频率和不同月份的发射率空间相关系数 （a、c、e、g、i） 与平均绝对偏差 （b、d、f、h、j） （a、b. 10.65 GHz，c、d. 18.7、19.35 GHz，e、f. 23.8 GHz，g、h 36.5、37.0 GHz，i、j. 85.0、89.0 GHz；n为样本数）
Figure 5. Comparison of R （a，c，e，g，i） and MAD （b，d，f，g，j） between FY-3D and AMSR-E1 and Telsem2 at different frequencies for different months （a，f. 10.65 GHz，b，g. 18.7，19.35 GHz，c，h. 23.8 GHz，d，i. 36.5，37.0 GHz，e、j. 85.0，89.0 GHz；n is the number of samples）
图 6 基于TELSEM2产品的典型土地覆盖类型逐月19.35 GHz和85.0 GHz发射率变化 （a. 亚马逊热带雨林，b. 北方落叶针叶林，c. 华北平原农田，d. 青藏高原草地，e. 非洲撒哈拉沙漠，f.北极格林兰冰盖；V_pol表示垂直极化，H_pol表示水平极化）
Figure 6. Monthly changes in emissivity of typical land cover types based on TELSEM2 at 19.35 GHz and 85.0 GHz （a. Amazon rainforest，b. Boreal forest，c. North China farmland，d. Tibetan grassland，e. Sahara desert，f. Greenland ice；vertical polarization：V_pol，horizontal polarization：H_pol）
图 7 不同产品典型土地覆盖类型（a、b. 亚马逊热带雨林，c、d. 北方落叶针叶林，e、f. 华北平原农田，g、h. 青藏高原草地，i、j. 非洲撒哈拉沙漠，k、l. 北极格林兰冰盖）发射率在1月（a、c、e、g、i、k）和7月（b、d、f、h、j、l）随频率的变化（V代表垂直极化，H代表水平极化）
Figure 7. Variations of average emissivity of typical land cover types （a，b. Amazon rainforest，c，d. Boreal forest，e，f. North China farmland，g，h. Tibetan grassland，i，j. Sahara desert，k，l. Greenland ice） for different products with frequency in January （a，c，e，g，i，k） and July （b，d，f，h，j，l）（vertical polarization：V；horizontal polarization：H）
图 8 CoTELSEM2产品6.925 （a、d）、10.65 （b、e）和150.0 （c、f） GHz水平极化发射率1月 （a—c） 和7月 （d—f） 空间分布
Figure 8. Spatial distributions of horizontal polarization emissivity for CoTELSEM2 product at 6.925 （a，d）、10.65 （b，e） 和150.0 （c，f） GHz in January （a—c） and July （d—f）
图 9 基于CoTELSEM2产品典型土地覆盖类型发射率在1 （a） 和7 （b） 月随频率的变化 （V-pol代表垂直极化，H-pol代表水平极化）
Figure 9. Variations of average emissivity of typical land cover types for CoTELSEM2 product with frequency in January （a） and July （b） （vertical polarization：V-pol，horizontal polarization：H-pol）
图 10 基于CoTELSEM2产品全球陆表不同频率 （a. 10.65 GHz，b. 19.35 GHz，c. 85.0 GHz，d. 150.0 GHz，） 水平极化发射率不确定性 （年内标准差） 空间分布
Figure 10. Spatial distributions of uncertainty （annual standard deviation） of global land surface horizontal polarization emissivity for different frequencies （a. 10.65 GHz，b. 19.35 GHz，c. 85.0 GHz，d. 150.0 GHz，） based on CoTELSEM2 product
表 1 7套全球0.25°空间分辨率数据集评估频率与观测时间
Table 1. Evaluation frequency and observation times of 7 sets of global 0.25° spatial resolution datasets
序号 数据集名称
（简称）评估频率（GHz） 观测时间 1 SSMI/S地图集（SSMI/S） 19.35V、19.35H、22.235V、37.0V、37.0H、91.656V、91.65H 2014年1月—2015年12月 2 AMSU-A/B地图集（AMSU-A/B） AMSU-A：23.8HA、31.4HA、50.3HA、89.0HA；
AMSU-B：150 HA2014年1月—2015年12月 3 ATMS地图集（ATMS） 23.8HA、31.4HA、50.3HA、51.7HA、89.5HA 2014年1月—2015年12月 4 TELSEM2工具背景数据集（TELSEM2） 19.35V、19.35H、37.0V、37.0H、85.0V、85.0H 1993年1月—2003年12月 5 CREST AMSR-E数据集（AMSR-E1） 6.925V、6.925H、10.65V、10.65H、18.7V、18.7H、23.8V、23.8H、36.5V、36.5H、89.0V、89.0H 2002年7月—2008年6月 6 AIRCAS AMSR-E数据集（AMSR-E2） 6.925V、6.925H、10.65V、10.65H、18.7V、18.7H、23.8V、23.8H、36.5V、36.5H、89.0V、89.0H 2002年6月—2011年10月 7 FY-3D MWRI数据集（FY3D） 10.65V、10.65H、18.7V、18.7H、23.8V、23.8H、36.5V、36.5H、89.0V、89.0H 2022年1月—2022年12月 注： V：垂直极化；H：水平极化；HA：大天顶角（≥40°）。 
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[1] 潘火平. 2013. 基于亮温观测数据一维变分同化的微波发射率估算研究[D]. 北京:中国科学院大学. Pan H P. 2013. Microwave emissmity retrieval from brightness temperature through one-dimensional variational assimilation[D]. Beijing:University of Chinese Academy of Sciences (in Chinese). [2] 彭丹青,李京,赵天杰等. 2009. 基于被动微波的寒旱区地表温度反演. 冰川冻土,31(2):233-238. Peng D Q,Li J,Zhao T J,et al. 2009. Land surface temperature retrieved from passive microwave data over cold and arid regions. J Glaciol Geocryol,31(2):233-238 (in Chinese Peng D Q, Li J, Zhao T J, et al .2009 . Land surface temperature retrieved from passive microwave data over cold and arid regions. J Glaciol Geocryol,31 （2 ）:233 -238 （in Chinese）[3] 邱玉宝,郭华东,石利娟等. 2016. 基于AMSR-E的全球陆表被动微波发射率数据集. 遥感技术与应用,31(4):809-819. Qiu Y B,Guo H D,Shi L J,et al. 2016. Global land surface emissivity dataset based on AMSR-E observations. Remote Sens Technol Appl,31(4):809-819 (in Chinese Qiu Y B, Guo H D, Shi L J, et al .2016 . Global land surface emissivity dataset based on AMSR-E observations. Remote Sens Technol Appl,31 （4 ）:809 -819 （in Chinese）[4] 施建成,蒋玲梅,张立新. 2006. 多频率多极化地表辐射参数化模型. 遥感学报,10(4):502-514. Shi J C,Jiang L M,Zhang L X. 2006. A parameterized multi-frequency-polarization surface emission model. J Remote Sens,10(4):502-514 (in Chinese Shi J C, Jiang L M, Zhang L X .2006 . A parameterized multi-frequency-polarization surface emission model. J Remote Sens,10 （4 ）:502 -514 （in Chinese）[5] 王博. 2016. 微波地表发射率的多模型集成模拟研究[D]. 北京:北京化工大学. Wang B. 2016. Multi model integrated simulation of microwave surface emissivity[D]. Beijing:Beijing University of Chemical Technology (in Chinese). [6] 吴莹. 2012. 微波地表发射率的卫星遥感反演和模式模拟研究[D]. 南京:南京信息工程大学. Wu Y. 2012. Satellite remote sensing and modeling of microwave land surface emissivity[D]. Nanjing:Nanjing University of Information Science & Technology (in Chinese). [7] 吴莹,何灵莉,钱博,等. 2019. 基于FY-3B/MWRI数据的青藏高原地区地表发射率反演. 地球物理学进展,34(1):0012-0018. Wu Y,He L L,Qian B,et al. 2019. Retrieval of land surface emissivity from FY-3B/MWI data over the Qinghai-Tibetan Plateau. Progress in Geophysics,34(1):0012-0018 (in Chinese Wu Y, He L L, Qian B, et al .2019 . Retrieval of land surface emissivity from FY-3B/MWI data over the Qinghai-Tibetan Plateau. Progress in Geophysics,34 （1 ）:0012 -0018 （in Chinese）[8] Aires F,Prigent C,Bernardo F,et al. 2011. A tool to estimate land-surface emissivities at microwave frequencies (TELSEM) for use in numerical weather prediction. Qurat J Roy Meteor Soc,137(656):690-699 doi: 10.1002/qj.803 [9] Boukabara S A,Garrett K,Chen W C,et al. 2011. MiRS:An all-weather 1DVAR satellite data assimilation and retrieval system. IEEE Trans Geosci Remote Sens,49(9):3249-3272 doi: 10.1109/TGRS.2011.2158438 [10] Boukabara S A,Garrett K,Grassotti C. 2018. Dynamic inversion of global surface microwave emissivity using a 1DVAR approach. Remote Sens,10(5):679 doi: 10.3390/rs10050679 [11] Chen M,Weng F Z. 2016. Modeling land surface roughness effect on soil microwave emission in community surface emissivity model. IEEE Trans Geosci Remote Sens,54(3):1716-1726 doi: 10.1109/TGRS.2015.2487885 [12] Ferraro R R,Peters-Lidard C D,Hernandez C,et al. 2013. An evaluation of microwave land surface emissivities over the continental united states to benefit GPM-Era precipitation algorithms. IEEE Trans Geosci Remote Sens,51(1):378-398 doi: 10.1109/TGRS.2012.2199121 [13] Friedl M A,Mciver D K,Hodges J C F,et al. 2002. Global land cover mapping from MODIS:Algorithms and early results. Remote Sens Environ,83(1-2):287-302 doi: 10.1016/S0034-4257(02)00078-0 [14] Grody N C,Weng F Z. 2008. Microwave emission and scattering from deserts:Theory compared with satellite measurements. IEEE Trans Geosci Remote Sens,46(2):361-375 doi: 10.1109/TGRS.2007.909920 [15] Hu J H,Fu Y Y,Zhang P,et al. 2021. Satellite retrieval of microwave land surface emissivity under clear and cloudy skies in China using observations from AMSR-E and MODIS. Remote Sens,13(19):3980 doi: 10.3390/rs13193980 [16] Jones A S,Vonder Haar T H. 1997. Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements. J Geophys Res,102(D12):13609-13626 doi: 10.1029/97JD00797 [17] Karbou F,Prigent C,Eymard L,et al. 2005. Microwave land emissivity calculations using AMSU measurements. IEEE Trans Geosci Remote Sens,43(5):948-959 doi: 10.1109/TGRS.2004.837503 [18] Karbou F,Gerard E,Rabier F. 2006. Microwave land emissivity and skin temperature for AMSU-A and -B assimilation over land. Quart J Roy Meteor Soc,132(620):2333-2355 doi: 10.1256/qj.05.216 [19] Karbou F,Gérard E,Rabier F. 2010. Global 4DVAR assimilation and forecast experiments using AMSU observations over land. PartⅠ:Impacts of various land surface emissivity parameterizations. Wea Forecasting,25(1):5-19. doi: 10.1175/2009WAF2222243.1 [20] Li R,Min Q L. 2013. Dynamic response of microwave land surface properties to precipitation in Amazon rainforest. Remote Sens Environ,133:183-192 doi: 10.1016/j.rse.2013.02.001 [21] Li R,Hu J H,Wu S L,et al. 2022. Spatiotemporal variations of microwave land surface emissivity (MLSE) over China derived from four-year recalibrated Fengyun 3B MWRI data. Adv Atmos Sci,39(9):1536-1560 doi: 10.1007/s00376-022-1314-0 [22] Liu Z L,Wu H,Tang B H,et al. 2014. An empirical relationship of bare soil microwave emissions between vertical and horizontal polarization at 10.65 GHz. IEEE Geosci Remote Sens Lett,11(9):1479-1483 doi: 10.1109/LGRS.2013.2295927 [23] Min Q L,Lin B,Li R. 2010. Remote sensing vegetation hydrological states using passive microwave measurements. IEEE J Sel Top Appl Earth Obs Remote Sens,3(1):124-131 doi: 10.1109/JSTARS.2009.2032557 [24] Moncet J L,Liang P,Galantowicz J F,et al. 2011. Land surface microwave Emissivities derived from AMSR-E and MODIS measurements with advanced quality control. J Geophys Res,116(D16):D16104 doi: 10.1029/2010JD015429 [25] Norouzi H,Temimi M,Rossow W B,et al. 2011. The sensitivity of land emissivity estimates from AMSR-E at C and X bands to surface properties. Hydrol Earth Syst Sci Discuss,8:5667-5699 [26] Norouzi H,Rossow W,Temimi,M,et al. 2012. Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land. Remote Sens Environ,123:470-482 doi: 10.1016/j.rse.2012.04.015 [27] Norouzi,H,Temimi M,Rossow W B,et al. 2013. AMSR-E/Aqua Monthly Global Microwave Land Surface Emissivity,Version 1. Boulder,Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://nsidc.org/sites/default/files/nsidc-0543-v001-userguide.pdf [last access:22 September,2023 [28] Norouzi H,Temimi M,Prigent C,et al. 2015. Assessment of the consistency among global microwave land surface emissivity products. Atmos Meas Tech,8(3):1197-1205 doi: 10.5194/amt-8-1197-2015 [29] Prigent C,Rossow W B,Matthews E. 1997. Microwave land surface emissivities estimated from SSM/I observations. J Geophys Res,102(D18):21867-21890 doi: 10.1029/97JD01360 [30] Prigent C,Aires F,Rossow W B,et al. 2005. Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale:Relationship of satellite observations to in situ soil moisture measurements. J Geophys Res,110(D17):D07110 [31] Prigent C,Aires F,Rossow W B. 2006. Land surface microwave emissivities over the globe for a decade. Bull Amer Meteor Soc,87(11):1573-1584 doi: 10.1175/BAMS-87-11-1573 [32] Prigent C,Jaumouille E,Chevallier F,et al. 2008. A parameterization of the microwave land surface emissivity between 19 and 100Hz,anchored to satellite-derived estimates. IEEE Trans Geosci Remote Sens,46(2):344-352 doi: 10.1109/TGRS.2007.908881 [33] Prigent C,Liang P,Tian Y,et a1. 2015. Evaluation of modeled microwave land surface emissivities with satellite-based estimates. Journal of Geophysical Research:Atmospheres,120(7):2706-2718. [34] Prigent C, Aires F. 2016. A report for EUMETSAT:Tool to estimate land surface emissivity at microwaves and millimeter waves (Telsem2). available at:https://nwp-saf.eumetsat.int/downloads/emis_data/telsem2_mw_atlas.tar.bz2, last access: 1 November 2023. [35] Rossow W B,Schiffer R A. 1999. Advances in understanding clouds from ISCCP. Bull Amer Meteor Soc,80(11):2261-2288 [36] Ruston B C,Vonder Haar T H. 2004. Characterization of summertime microwave emissivities from the Special Sensor Microwave Imager over the conterminous United States. J Geophys Res,109(D19):D19103 [37] Sahoo A K,Pan M,Troy T J,et al. 2011. Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens Environ,115(8):1850-1865 doi: 10.1016/j.rse.2011.03.009 [38] Tian Y D,Peters-Lidard C D,Harrison K W,et al. 2014. Quantifying uncertainties in land-surface microwave emissivity retrievals. IEEE Trans Geosci Remote Sens,52(2):829-840 doi: 10.1109/TGRS.2013.2244214 [39] Wang D, Prigent C, Kilic L, et al. 2017. Surface emissivity at microwaves to millimeter waves over polar regions: parameterization and evaluation with aircraft experiments. Journal of Atmospheric & Oceanic Technology,(5). [40] Weng F Z,Yan B H,Grody N C. 2001. A microwave land emissivity model. J Geophys Res,106(D7):20115-20123. [41] Weng F Z,Zhu T,Yan B H. 2007. Satellite data assimilation in numerical weather prediction models. PartⅡ:Uses of rain-affected radiances from microwave observations for hurricane vortex analysis. J Atmos Sci,64(11):3910-3925 doi: 10.1175/2006JAS2051.1 [42] Weng F Z,Yu X W,Duan Y H,et al. 2020. Advanced Radiative Transfer Modeling System (ARMS):A new-generation satellite observation operator developed for numerical weather prediction and remote sensing applications. Adv Atmos Sci,37(2):131-136 doi: 10.1007/s00376-019-9170-2 [43] Wu Y,Qian B,Bao Y S,et al. 2019. Microwave land emissivity calculations over the Qinghai-Tibetan Plateau using FY-3B/MWRI measurements. Remote Sens,11(19):2206 doi: 10.3390/rs11192206 [44] Xie Y H,Shi J C,Ji D B,et al. 2017. A parameterized microwave emissivity model for bare soil surfaces. Remote Sens,9(2):155 doi: 10.3390/rs9020155 [45] Yang H,Weng F Z. 2011a. Error sources in remote sensing of microwave land surface emissivity. IEEE Trans Geosci Remote Sens,49(9):3437-3442 doi: 10.1109/TGRS.2011.2125794 [46] Yang H,Weng F Z,Lv L Q,et al. 2011b. The FengYun-3 microwave radiation imager on-orbit verification. IEEE Trans Geosci Remote Sens,49(11):4552-4560 doi: 10.1109/TGRS.2011.2148200 -
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