扫 描 看 全 文
Quote
祝新明，宋小宁，冷佩，胡容海.2021.多尺度地理加权回归的地表温度降尺度研究.遥感学报，25（8）： 1749-1766
Zhu X M，Song X N，Leng P and Hu R H. 2021. Spatial downscaling of land surface temperature with the multi-scale geographically weighted regression. National Remote Sensing Bulletin， 25（8）：1749-1766
由于星载热红外传感器研发技术的局限性，单一传感器尚不能提供兼具高频次、高空间分辨率地表温度数据。协同其他遥感辅助数据，对低空间分辨率、高时间频次地表温度产品开展降尺度研究成为了解决这一难题的有效途径。然而由于现有地表温度降尺度方法未充分考虑不同地表状态参数对地表温度空间分异格局的尺度影响差异，降尺度后的地表温度数据在异质性景观区域存在精度较差和空间纹理不清晰的问题。鉴于此，本文以北京和张掖地区的8期MODIS地表温度产品为例，通过引入多尺度地理加权回归MGWR（Multiscale Geographically Weighted Regression）来分析归一化植被指数NDVI、数字高程模型DEM、坡度和经纬度对地表温度空间格局影响的尺度差异，提出一种针对MODIS地表温度产品的空间降尺度算法，并与TsHARP算法、多元线性回归算法、地理加权回归算法和随机森林回归算法进行定量对比。结果表明，基于MGWR模型的地表温度降尺度转换函数能够良好地揭示多种地表状态参数与地表温度间的不同作用关系，其中NDVI和坡度对地表温度分布具有全局影响，DEM和经纬度对地表温度呈现出了局域性作用。与4种代表性方法相比，基于MGWR算法降尺度后的100 m分辨率地表温度数据具有更好的空间纹理，在城镇和沙漠等温度异质性明显地区保障了清晰的景观纹理；另外，对于所选研究区的8期MODIS地表温度产品而言，利用MGWR算法降尺度后的地表温度均拥有更好的精度，在0—1 K误差级别下的面积占比均大于57%，均方根误差RMSE（Root-Mean-Square Error）均小于2.85 K，决定系数,R,2,（coefficient of determination）均大于0.88。
Due to the limitations of thermal infrared technology, a single sensor cannot provide both high frequency and fine spatial resolution Land Surface Temperature (LST) data. For solving this problem, it becomes an effective way by conducting the spatial downscaling of LST product with low-resolution and high frequency in collaboration with other auxiliary data. However, the existing LST downscaling methods do not fully consider the scale effects of different biophysical parameters on the distribution of LST, which makes the accuracy and spatial distribution of the downscaled LST are inconsistent. In view of this, taking Beijing and Zhangye as two study areas, this paper proposed a kind of LST downscaling algorithm to sharpen the MODIS LST using Multi-scale Geographically Weighted Regression (MGWR) according to the analyse of effects of NDVI, DEM, slope, latitude, and longitude on LST heterogeneity. Furthermore, four kinds of LST downscaling methods (i.e., TsHARP algorithm, ML algorithm, GWR algorithm, and RF algorithm) were introduced in this paper for further comparison and validation. Results show that the constructed LST conversion function based on the MGWR reveals the actual interaction between various scale factors and LST at various spatial scales. NDVI and slope have global impacts on the LST, while DEM and geolocation present local impacts on the LST. Compared with the four referenced methods, the downscaled 100 m resolution LST based on the MGWR has better spatial textures and displays clear landscape features in heterogeneous areas such as deserts and towns. In addition, all images predicted by the MGWR algorithm showed better accuracy, in which the area proportion under the 0—1 K error level were all more than 57%, the root-mean-square error (RMSE) were all less than 2.85 K, and the coefficient of determination (,R,2,) were all more than 0.88.
地表温度空间降尺度MGWR模型作用差异MODIS
Land Surface Temperature (LST)spatial downscalingMGWRscale differenceMODIS
Agam N, Kustas W P, Anderson M C, Li F Q and Neale C M U. 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment, 107 (4): 545-558 [DOI: 10.1016/j.rse.2006.10.006http://dx.doi.org/10.1016/j.rse.2006.10.006]
Dominguez A, Kleissl J, Luvall J C and Rickman D L. 2011. High-resolution urban thermal sharpener (HUTS). Remote Sensing of Environment, 115 (7): 1772-1780 [DOI: 10.1016/j.rse.2011.03.008http://dx.doi.org/10.1016/j.rse.2011.03.008]
Duan S B and Li Z L. 2016. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression case study in Northern China. IEEE Transactions on Geoscience and Remote Sensing, 54 (11): 6458-6469 [DOI: 10.1109/TGRS.2016.2585198http://dx.doi.org/10.1109/TGRS.2016.2585198]
Duan S B, Li Z L and Leng P. 2017. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sensing of Environment, 195: 107-117 [DOI: 10.1016/j.rse.2017.04.008http://dx.doi.org/10.1016/j.rse.2017.04.008]
Duan S B, Li Z L, Li H, Göttsche F M, Wu H, Zhao W, Leng P, Zhang X and Coll C. 2019. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225: 16-29 [DOI: 10.1016/j.rse.2019.02.020http://dx.doi.org/10.1016/j.rse.2019.02.020]
Duan S B, Ru C, Li Z L, Wang M M, Xu H Q, Li H, Wu P H, Zhan W F, Zhou J, Zhao W, Ren H Z, Wu H, Tang B H, Zhang X, Shang Guo F and Qin Z H. 2021. Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data. National Remote Sensing Bulletin, 25(8): 1591-1617
段四波, 茹晨, 李召良, 王猛猛, 徐涵秋, 历华, 吴鹏海, 占文凤, 周纪, 赵伟, 任华忠, 吴骅, 唐伯惠, 张霞, 尚国琲, 覃志豪. 2021. Landsat卫星热红外数据地表温度遥感反演研究进展. 遥感学报, 25(8): 1591-1617 [DOI: 10.11834/jrs.20211296http://dx.doi.org/10.11834/jrs.20211296]
Fotheringham A S, Brunsdon C and Charlton M. 2002. Geographically weighted regression: the analysis of spatially varying relationships. Chichester/Hoboken Wiley.
Fotheringham A S, Yang W and Kang W. 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107 (6): 1247-1265 [DOI: 10.1080/24694452.2017.1352480http://dx.doi.org/10.1080/24694452.2017.1352480]
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44 (8): 2207-2218 [DOI: 10.1109/TGRS.2006.872081http://dx.doi.org/10.1109/TGRS.2006.872081]
Gu H Y, Meng X, Shen T Y and Cui N N. Spatial variation of the determinants of China's urban floating population's settlement intention. Acta Geographica Sinica, 75 (2): 240-254
古恒宇, 孟鑫, 沈体雁, 崔娜娜. 2020. 中国城市流动人口居留意愿影响因素的空间分异特征. 地理学报, 75 (2): 240-254 [DOI: 10.11821/dlxb202002003http://dx.doi.org/10.11821/dlxb202002003]
Hua J W, Zhu S Y and Zhang G X. 2018. Downscaling land surface temperature based on random forest algorithm. Remote Sensing for Land and Resources, 2018, 30 (1): 78-86
华俊玮, 祝善友, 张桂欣. 2018. 基于随机森林算法的地表温度降尺度研究. 国土资源遥感, 30 (1): 78-86 [DOI: 10.6046/gtzyyg.2018.01.11http://dx.doi.org/10.6046/gtzyyg.2018.01.11]
Huang C, Duan S B, Jiang X G, Han X L, Leng P, Gao M F and Li Z L. 2018. A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements. International Journal of Remote Sensing, 40 (5-6): 1828-1843 [DOI: 10.1080/01431161.2018.1508920http://dx.doi.org/10.1080/01431161.2018.1508920]
Hutengs C and Vohland M. 2016. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178: 127-141 [DOI: 10.1016/j.rse.2016.03.006http://dx.doi.org/10.1016/j.rse.2016.03.006]
Kustas W P, Norman J M, Anderson M C and French A N. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85 (4): 429-440 [DOI: 10.1016/S0034-4257(03)00036-1http://dx.doi.org/10.1016/S0034-4257(03)00036-1]
Li W, Ni L, Li Z L, Duan S B and Wu H. 2019. Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (7): 2299-2307 [DOI: 10.1109/JSTARS.2019.2896923http://dx.doi.org/10.1109/JSTARS.2019.2896923]
Li X J, Jiang T, Xin X Z, Zhang H L and Liu Q H. 2016. Spatial downscaling of land surface temperature based on MODIS data. Chinese Journal of Ecology, 2016, 35 (12): 3443-3450
李小军, 江涛, 辛晓洲, 张海龙, 柳钦火. 2016. 基于MODIS的地表温度空间降尺度方法. 生态学杂志, 35 (12): 3443-3450 [DOI: 10.13292/j.1000-4890.201612.025http://dx.doi.org/10.13292/j.1000-4890.201612.025]
Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20 (5): 899-920
李召良, 段四波, 唐伯惠, 吴骅, 任华忠, 阎广建, 唐荣林, 冷佩. 2016. 热红外地表温度遥感反演方法研究进展. 遥感学报, 20 (5): 899-920 [DOI:10.11834/jrs.20166192http://dx.doi.org/10.11834/jrs.20166192]
Li Z L, Tang B H, Tang R L, Wu H, Duan S B, Leng P and Zhang R H. 2017. Theory and method of thermal infrared remote sensing inversion of land surface temperature. Science Focus, 12 (6): 57-69
李召良, 唐伯惠, 唐荣林, 吴骅, 段四波, 冷佩, 张仁华. 2017. 地表温度热红外遥感反演理论与方法. 科学观察, 12 (6): 57-69 [DOI: 10.15978/j.cnki.1673-5668.201706006http://dx.doi.org/10.15978/j.cnki.1673-5668.201706006]
Liu M, Tang R L, Li Z L, Gao M F and Yao Y J. 2021. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration. National Remote Sensing Bulletin, 25(8): 1517-1537
刘萌, 唐荣林, 李召良, 高懋芳, 姚云军. 2021. 数据驱动的蒸散发遥感反演方法及产品研究进展. 遥感学报, 25(8): 1517-1537 [DOI: 10.11834/jrs.20211310http://dx.doi.org/10.11834/jrs.20211310]
Mukherjee S, Joshi P K and Garg R D. 2014. A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape. Advances in Space Research, 54 (4): 655-669 [DOI: 10.1016/j.asr.2014.04.013http://dx.doi.org/10.1016/j.asr.2014.04.013]
Nie J L, Wu J J, Yang X, Liu M, Zhang J and Zhou L. Downscaling land surface temperature based on relationship between surface temperature and vegetation index. Acta Ecologica Sinica, 2011, 31 (17): 4961-4969
聂建亮, 武建军, 杨曦, 刘明, 张洁, 周磊. 2011. 基于地表温度——植被指数关系的地表温度降尺度方法研究. 生态学报, 31 (17): 4961-4969 [DOI: CNKI:SUN:STXB.0.2011-17-020http://dx.doi.org/CNKI:SUN:STXB.0.2011-17-020]
Oke T R. 1982. The energetic basis of the urban heat island. Royal Meteorological Society, 108 (455): 1-24 [DOI: 10.1002/qj.49710845502http://dx.doi.org/10.1002/qj.49710845502]
Oshan T M, Li Z Q, Kang W, Wolf L J and Fotheringham A S. 2019. Mgwr: a python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. International Journal of Geo-Information, 8 (269): 1-31 [DOI: 10.3390/ijgi8060269http://dx.doi.org/10.3390/ijgi8060269]
Peng X, Wu W, Zheng Y, Sun J, Hu T and Wang P. 2020. Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific Reports, 10 (1): 1-16 [DOI: 10.1038/s41598-020-67423-6http://dx.doi.org/10.1038/s41598-020-67423-6]
Peng Y D, Li W S, Luo X B and Li H. 2019. A geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions. IEEE Transactions on Geoscience and Remote Sensing, 57 (7): 5012-5027. [DOI: 10.1109/TGRS.2019.2895351http://dx.doi.org/10.1109/TGRS.2019.2895351]
Qin Z H, Karnieli A and Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International journal of remote sensing, 22 (18): 3719-3746 [DOI: 10.1080/01431160010006971http://dx.doi.org/10.1080/01431160010006971]
Qin Z H, Li W J, Xu B, Chen Z X and Liu J. 2004. The estimation of land surface emissivity for Landsat TM6. Remote Sensing for Land and Resources, 16 (3): 28-32
覃志豪, 李文娟, 徐斌, 陈仲新, 刘娟. 2004. 陆地卫星TM6波段范围内地表比辐射率的估计. 国土资源遥感, 16 (3): 28-32 [DOI: 10.3969/j.issn.1001-070X.2004.03.007http://dx.doi.org/10.3969/j.issn.1001-070X.2004.03.007]
Quan J L, Zhan W F, Chen Y H and Liu W Y. 2013. Downscaling remotely sensed land surface temperatures: A comparison of typical methods. Journal of Remote Sensing, 17 (2): 361-387
全金玲, 占文凤, 陈云浩, 刘闻雨. 2013. 遥感地表温度降尺度方法比较——性能对比及适应性评价. 遥感学报, 17 (2): 361-87) [DOI: 1007-4619( 2013 02-0361-27]
Shen T Y, Yu H C, Zhou L, Gu H Y and He H H. 2020. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: scale law of spatial heterogeneity. Economic Geography, 40 (3): 75-83
沈体雁, 于瀚辰, 周麟, 古恒宇, 何泓浩. 2020. 北京市二手住宅价格影响机制——基于多尺度地理加权回归模型(MGWR)的研究. 经济地理, 40 (3): 75-83 [DOI: 10.15957/j.cnki.jjdl.2020.03.009http://dx.doi.org/10.15957/j.cnki.jjdl.2020.03.009]
Voogt J A and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86 (3): 370-384 [DOI: 10.1016/S0034-4257(03)00079-8http://dx.doi.org/10.1016/S0034-4257(03)00079-8]
Wan Z M and Li Z L. 2008. Radiance-based validation of the V5 MODIS land-surface temperature product. International journal of remote sensing, 29 (17): 5373-5395 [DOI: 10.1080/01431160802036565http://dx.doi.org/10.1080/01431160802036565]
Wang S M, Luo X B and Peng Y D. 2020. Spatial downscaling of MODIS land surface temperature based on geographically weighted autoregressive model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-15 [DOI: 10.1109/JSTARS.2020.2968809http://dx.doi.org/10.1109/JSTARS.2020.2968809]
Wang Y T, Xie D H and Li Y H. 2014. Downscaling remotely sensed land surface temperature over urban areas using trend surface of spectral index. Journal of Remote Sensing, 18 (6): 1169-1181
王祎婷, 谢东辉, 李亚惠. 2014. 光谱指数趋势面的城市地表温度降尺度转换. 遥感学报, 18 (6): 1169-1181 [DOI: 10.11834/jrs.20144115http://dx.doi.org/10.11834/jrs.20144115]
Wang Z H, Qin Q M, Sun Y H, Zhang T Y and Ren H Z. 2018. Downscaling remotely sensed land surface temperature with the BP neural network. Remote Sensing Technology and Application, 2018, 33 (5): 793-802
汪子豪, 秦其明, 孙元亨, 张添源, 任华忠. 2018. 基于BP神经网络的地表温度空间降尺度方法. 遥感技术与应用, 33 (5): 793-802 [DOI: 10.11873/j.issn.1004-0323.2018.5.0793http://dx.doi.org/10.11873/j.issn.1004-0323.2018.5.0793]
Wei R and Shan J. 2018. Spatial and temporal fusion for urban land surface temperature image mapping. Geomatics and Information Science of Wuhan University, 43 (3): 428-435
魏然, 单杰. 2018. 城市地表温度影像时空融合方法研究. 武汉大学学报(信息科学版), 43 (3): 428-435 [DOI: 10.13203/j.whugis20150489http://dx.doi.org/10.13203/j.whugis20150489]
Weng Q H. 2009. Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (4): 335-344 [DOI: 10.1016/j.isprsjprs.2009.03.007http://dx.doi.org/10.1016/j.isprsjprs.2009.03.007]
Weng Q H, Fu P and Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145: 55-67 [DOI: 10.1016/j.rse.2014.02.003http://dx.doi.org/10.1016/j.rse.2014.02.003]
Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156: 169-181 [DOI: 10.1016/j.rse.2014.09.013http://dx.doi.org/10.1016/j.rse.2014.09.013]
Wu P H, Yin Z X, Chao Z, Duan S B, Gottsche F M, Ma X S, Li X H, Yang H and Shen H F. 2021. Spatially continuous and high-resolution land surface temperature: a review of reconstruction and spatiotemporal fusion techniques. Electrical Engineering and Systems Science, 1-42 [DOI: 10.1109/MGRS.2021.3050782http://dx.doi.org/10.1109/MGRS.2021.3050782]
Yang G J, Pu R L, Huang W J, Wang J H and Zhao C J. 2010. A novel method to estimate subpixel temperature by fusing solar-reflective and thermal-infrared remote-sensing data with an artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 48 (4): 2170-2178 [DOI: 10.1109/TGRS.2009.2033180http://dx.doi.org/10.1109/TGRS.2009.2033180]
Yang Y B, Cao C, Pan X, Li X L and Zhu X. 2017. Downscaling land surface temperature in an arid area by using multiple remote sensing indices with Random Forest regression. Remote Sensing, 9 (789): 1-18 [DOI: 10.3390/rs9080789http://dx.doi.org/10.3390/rs9080789]
Yin Z X, Wu P H, Foody G M, Liu Z H, Du Y and Ling F. 2020. Spatiotemporal fusion of land surface temperature based on a convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, PP (99): 1-15 [DOI: 10.1109/TGRS.2020.2999943http://dx.doi.org/10.1109/TGRS.2020.2999943]
Yoo C, Im J, Park S and Cho D. 2020. Spatial downscaling of MODIS land surface temperature: recent research trends, challenges, and future directions. Korean Journal of Remote Sensing, 36 (4): 609-626 [DOI: 10.7780/kjrs.2020.36.4.9http://dx.doi.org/10.7780/kjrs.2020.36.4.9]
Yu H C, Fotheringham A S, Li Z Q, Oshan T, Kang W and Wolf L J. 2019. Inference in multiscale geographically weighted regression. Geographical Analysis, 52 (1): 87-106 [DOI: 10.1111/gean.12189http://dx.doi.org/10.1111/gean.12189]
Zhan W F, Chen Y H, Wang J F, Zhou J, Quan J L, Liu W Y and Li J. 2012. Downscaling land surface temperatures with multi-spectral and multi-resolution images. International Journal of Applied Earth Observations and Geoinformation, 18: 23-36 [DOI: 10.1016/j.jag.2012.01.003http://dx.doi.org/10.1016/j.jag.2012.01.003]
Zhan W F, Chen Y H, Zhou J, Li J and Liu W Y. 2011. Sharpening thermal imageries: a generalized theoretical framework from an assimilation perspective. IEEE Transactions on Geoscience and Remote Sensing, 49 (2): 773-789 [DOI: 10.1109/TGRS.2010.2060342http://dx.doi.org/10.1109/TGRS.2010.2060342]
Zhang Q, Wang N, Cheng J and Xu S. 2020. A stepwise downscaling method for generating high-resolution land surface temperature from AMSR-E data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5669-5681 [DOI: 10.1109/JSTARS.2020.3022997http://dx.doi.org/10.1109/JSTARS.2020.3022997]
Zhao W and Duan S B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sensing of Environment 247, 111931 [DOI: 10.1016/j.rse.2020.111931http://dx.doi.org/10.1016/j.rse.2020.111931]
Zhu S Y, Guan H D, Millington A C and Zhang G X. 2013. Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai, China. International Journal of Remote Sensing, 34 (5-6): 1707-1723 [DOI: 10.1080/01431161.2012.725957http://dx.doi.org/10.1080/01431161.2012.725957]
Zhu X L, Cai F Y, Tian J Q and Trecia W. 2018. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10 (4): 1-23 [DOI: 10.3390/rs10040527http://dx.doi.org/10.3390/rs10040527]
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114 (11): 2610-2623 [DOI: 10.1016/j.rse.2010.05.032http://dx.doi.org/10.1016/j.rse.2010.05.032]
Zhu X M, Song X N, Leng P, Guo D and Cai S H. 2020. Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions. IEEE Transactions on Geoscience and Remote Sensing, 59 (3): 2680-2697 [DOI: 10.1109/TGRS.2020.3002821http://dx.doi.org/10.1109/TGRS.2020.3002821]
Zhu X M, Wang X H, Yan D J, Liu Z and Zhou Y F. 2019. Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi'an, China. International Journal of Remote Sensing, 40 (9): 3421-3447 [DOI: 10.1080/01431161.2018.1547448http://dx.doi.org/10.1080/01431161.2018.1547448]
Zhu X M, Wang X H, Zhou Y F, Wu W H and Liu Z. 2017. Spatial variability of thermal environment in Xi'an under the build-up area expansion. Chinese Journal of Ecology, 36 (12): 3574-3583
祝新明, 王旭红, 周永芳, 吴文恒, 刘状. 2017. 建成区扩张下的西安市热环境空间分异性. 生态学杂志, 36 (12): 3574-3583 [DOI:10.13292/j.1000-4890.201712.015http://dx.doi.org/10.13292/j.1000-4890.201712.015]
相关文章
相关作者
相关机构
京公网安备11010802024621