湖南省不同云状态的时空特征分析
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摘要
云的存在使得遥感对地观测地表信息受损或缺失,特定区域的云状态时空分布特征有助于提升遥感数据云噪声去除的针对性和准确性。本文采用2001-2017年湖南省MOD09A1地表反射率产品像素级产品质量数据集,逐像素解析并提取云状态信息,利用地统计学方法和空间分析方法等,从云状态空间分布概率、云状态季节分布概率、云状态持续时长和云干扰率4个方面分析了湖南省晴朗无云、云污染和云混合3种云状态时空分布特征。研究结果表明:①湖南省整体受云影响较为严重,不同云状态空间分布呈现较为明显的差异性,云污染状态主要分布在湘西、湘南山区,云混合状态主要分布在湘北、湘中、湘南连接的地势相对平缓的平原、丘陵地区;②云污染状态在云影响中起主导作用,其主要分布在1-2月、11-12月以及5月下旬-7月上旬之间,云混合状态平均出现概率全年维持在10%左右,在6-10月增加至18%左右;③云状态持续时长为8、16 d是受云影响的主要情形;④随着合成窗口的增加,云干扰率迅速下降,采用月合成(4期)后,可以忽略云的影响;⑤云状态数据经主成分变换后,前2个主分量可表征不同云状态空间分布模式,全省可划分为4个具有显著云状态特征的区域;⑥不同云状态与高程变化具有显著的关系模型,除云污染状态外,均与高程呈负相关关系。本研究可为湖南省遥感数据选择、云去除方法选择、植被指数时间序列重构等提供技术支撑。
The presence of clouds hinders remote sensing of earth surface and causes information loss. Understanding the spatiotemporal patterns of different cloud states over a given region can improve the pertinence and accuracy of cloud noise elimination. In this study, the MODIS surface reflectance products, MOD09 A1, were used to extract the cloud state information for each pixel of Hunan Province, spanning from 2001 to 2017. Then, geostatistics and spatial analysis methods were used to detect Hunan's spatiotemporal patterns of the clear, cloudy,and mixed states. The examined patterns included the spatial distribution, seasonal distribution, gap length duration, and cloud interference for the different cloud states. The results show that:(1) Hunan Province in general was quite severely affected by clouds, with obvious spatial heterogeneity, i.e., the cloudy state when imagery are shadowed by clouds mainly occurred over the western and southern mountain areas of Hunan, while the mixed state when imagery are distorted by clouds existed over the gentle plains and hilly regions of the northern, central, and southern Hunan;(2) the cloudy state was dominant among the cloudy and mixed states,with the former more likely to occur between January and February, November and December, late May and early July, while the latter maintained 10% of cloud cover during the whole year and up to 18% from June to October;(3) cloud gap lengths of 8-days and 16-days were the primary situation for the cloudy and mixed states;(4) increasing the composite window size, cloud interference effects declined dramatically and could be neglected if monthly composite size(4 collections) was utilized;(5) by Principal Component Analysis(PCA) of the different cloudy state data, the first two PCA components were derived which indicated different spatial distribution patterns and divided Hunan into four sub regions with distinctive cloud characteristics; and(6) there are significant relationships between the different cloud states and elevation variation, and except for the cloudy state, they are all negatively correlated with elevation variation. This study can provide technical support for the selection of remote sensing data, the removal method of cloud noises, and the reconstruction of vegetation indices time series for Hunan Province.
The presence of clouds hinders remote sensing of earth surface and causes information loss. Understanding the spatiotemporal patterns of different cloud states over a given region can improve the pertinence and accuracy of cloud noise elimination. In this study, the MODIS surface reflectance products, MOD09 A1, were used to extract the cloud state information for each pixel of Hunan Province, spanning from 2001 to 2017. Then, geostatistics and spatial analysis methods were used to detect Hunan's spatiotemporal patterns of the clear, cloudy,and mixed states. The examined patterns included the spatial distribution, seasonal distribution, gap length duration, and cloud interference for the different cloud states. The results show that:(1) Hunan Province in general was quite severely affected by clouds, with obvious spatial heterogeneity, i.e., the cloudy state when imagery are shadowed by clouds mainly occurred over the western and southern mountain areas of Hunan, while the mixed state when imagery are distorted by clouds existed over the gentle plains and hilly regions of the northern, central, and southern Hunan;(2) the cloudy state was dominant among the cloudy and mixed states,with the former more likely to occur between January and February, November and December, late May and early July, while the latter maintained 10% of cloud cover during the whole year and up to 18% from June to October;(3) cloud gap lengths of 8-days and 16-days were the primary situation for the cloudy and mixed states;(4) increasing the composite window size, cloud interference effects declined dramatically and could be neglected if monthly composite size(4 collections) was utilized;(5) by Principal Component Analysis(PCA) of the different cloudy state data, the first two PCA components were derived which indicated different spatial distribution patterns and divided Hunan into four sub regions with distinctive cloud characteristics; and(6) there are significant relationships between the different cloud states and elevation variation, and except for the cloudy state, they are all negatively correlated with elevation variation. This study can provide technical support for the selection of remote sensing data, the removal method of cloud noises, and the reconstruction of vegetation indices time series for Hunan Province.
引文
[1]童庆禧,张兵,郑兰芬.高光谱遥感——原理、技术与应用[M].北京:高等教育出版社,2006:83-86.[Tong Q X,Zhang B, Zheng L F. Hyperspectral remote sensing:Principle, technology and applications[M]. Beijing:Higher Education Press, 2006:83-86.]
[2] Berk A, Conforti P, Kennett R, et al. MODTRAN6:A major upgrade of the MODTRAN radiative transfer code[J]. 20146th Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing, 2014:1-4.
[3] Vermote E F, Tanre D, Deuze J L, et al. Second simulation of the satellite signal in the solar spectrum, 6s:An overview[J]. Geoscience&Remote Sensing IEEE Transactions on, 2002,35(3):675-686.
[4] King M D, Platnick S, Menzel W P, et al. Spatial and temporal distribution of clouds observed by MODIS onboard the terra and aqua satellites[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(7):3826-3852.
[5]张雪芹,彭莉莉,郑度,等.1971-2004年青藏高原总云量时空变化及其影响因子[J].地理学报,2007,62(9):959-969.[Zhang X Q, Peng L L, Zheng D, et al. Cloudiness variations over the Qinghai-Tibet Plateau during 1971-2004[J]. Journal of Geographical Science, 2007,62(9):959-969.]
[6]李慧晶,刘建西,刘东升,等.西南地区云量变化特征[J].干旱气象,2014,32(2):194-200,219.[Li H J, Liu J X, Liu D S, et al. Variation characteristics of cloud cover over Southwestern China[J]. Journal of Arid Meteorology,2014,32(2):194-200,219.]
[7] Ma J J, Wu H, Wang C, et al. Multiyear satellite and surface observations of cloud fraction over China[J]. Journal of Geophysical Research:Atmospheres, 2014,119(12):7655-7666.
[8] Ghasemifar E, Farajzadeh M, Perry M C, et al. Analysis of spatiotemporal variations of cloud fraction based on geographic characteristics over Iran[J]. Theoretical and Applied Climatology, 2018,134(3-4):1429-1445.
[9]钟洪麟,施润和,曲培青,等.MODIS影像的局地云量信息元数据提取算法与应用[J].地球信息科学学报,2010,12(4):587-592.[Zhong H L, Shi R H, Qu P Q, Zhang H F,et al. The regional cloud-cover metadata extraction based on MODIS image[J]. Journal of Geo-information Science, 2010,12(4):587-592.]
[10]彭秋志,秦国玲,吕乐婷,等.基于MODIS数据的东江流域云干扰时空特征分析[J].国土资源遥感,2018,30(1):109-115.[Peng Q Z, Qin G L, Lv L T, et al. Spatio-temporal patterns of cloud interference in Dongjiang River basin from MODIS data[J]. Remote Sensing for Land&Resources, 2018,30(1):109-115.]
[11] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002,83(1):195-213.
[12] Chen J, J?nsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004,91(3):332-344.
[13] Petitjean F, Ketterlin A, Gan?arski P. A global averaging method for dynamic time warping, with applications to clustering[J]. Pattern Recognition, 2011,44(3):678-693.
[14] J?nsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. Geoscience&Remote Sensing IEEE Transactions on, 2002,40(8):1824-1832.
[15]沈金祥,季漩.遥感影像云及云影多特征协同检测方法[J].地球信息科学学报,2016,18(5):599-605.[Shen J X,Ji X. Cloud and cloud shadow multi-feature collaborative detection from remote sensing image[J]. Journal of Geoinformation Science, 2016,18(5):599-605.]
[16]周惠慧,王楠,黄瑶,等.不同时间间隔下的遥感时间序列重构模型比较分析[J].地球信息科学学报,2016,18(10):1410-1417.[Zhou H H, Wang N, Huang Y, et al. Comparison and analysis of remotely sensed time series of reconstruction models at various intervals[J]. Journal of Geo-information Science, 2016,18(10):1410-1417.]
[17] LP DAAC. MODIS products table[OL]:https://lpdaac.usgs.gov/product_search/?view=list&collection_version_id=6&collections=Aqua+MODIS&collections=Combined+MODIS&collections=Terra+MODIS.
[18] Lyapustin A, Wang Y, Xiong X, et al. Scientific impact of MODIS C5 calibration degradation and C6+improvements[J]. Atmospheric Measurement Techniques, 2014,7(12):4353-4365.
[19] Roger J C, Vermote E F, Ray J P. MODIS surface reflectance user's guide, Collection 6[R]. 2015.
[20] Vermote E F. MOD09A1 MODIS/Terra surface reflectance 8-day l3 global 500m sin grid V006[DB]. 2015.doi:10.5067/MODIS/MOD09A1.006
[21]边金虎,李爱农,宋孟强,等. MODIS植被指数时间序列Savitzky-Golay滤波算法重构[J].遥感学报,2010,14(4):725-741.[Bian J H, Li A N, Song M Q, et al. Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter[J]. Journal of Remote Sensing,2010,14(4):725-741.]
[2] Berk A, Conforti P, Kennett R, et al. MODTRAN6:A major upgrade of the MODTRAN radiative transfer code[J]. 20146th Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing, 2014:1-4.
[3] Vermote E F, Tanre D, Deuze J L, et al. Second simulation of the satellite signal in the solar spectrum, 6s:An overview[J]. Geoscience&Remote Sensing IEEE Transactions on, 2002,35(3):675-686.
[4] King M D, Platnick S, Menzel W P, et al. Spatial and temporal distribution of clouds observed by MODIS onboard the terra and aqua satellites[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(7):3826-3852.
[5]张雪芹,彭莉莉,郑度,等.1971-2004年青藏高原总云量时空变化及其影响因子[J].地理学报,2007,62(9):959-969.[Zhang X Q, Peng L L, Zheng D, et al. Cloudiness variations over the Qinghai-Tibet Plateau during 1971-2004[J]. Journal of Geographical Science, 2007,62(9):959-969.]
[6]李慧晶,刘建西,刘东升,等.西南地区云量变化特征[J].干旱气象,2014,32(2):194-200,219.[Li H J, Liu J X, Liu D S, et al. Variation characteristics of cloud cover over Southwestern China[J]. Journal of Arid Meteorology,2014,32(2):194-200,219.]
[7] Ma J J, Wu H, Wang C, et al. Multiyear satellite and surface observations of cloud fraction over China[J]. Journal of Geophysical Research:Atmospheres, 2014,119(12):7655-7666.
[8] Ghasemifar E, Farajzadeh M, Perry M C, et al. Analysis of spatiotemporal variations of cloud fraction based on geographic characteristics over Iran[J]. Theoretical and Applied Climatology, 2018,134(3-4):1429-1445.
[9]钟洪麟,施润和,曲培青,等.MODIS影像的局地云量信息元数据提取算法与应用[J].地球信息科学学报,2010,12(4):587-592.[Zhong H L, Shi R H, Qu P Q, Zhang H F,et al. The regional cloud-cover metadata extraction based on MODIS image[J]. Journal of Geo-information Science, 2010,12(4):587-592.]
[10]彭秋志,秦国玲,吕乐婷,等.基于MODIS数据的东江流域云干扰时空特征分析[J].国土资源遥感,2018,30(1):109-115.[Peng Q Z, Qin G L, Lv L T, et al. Spatio-temporal patterns of cloud interference in Dongjiang River basin from MODIS data[J]. Remote Sensing for Land&Resources, 2018,30(1):109-115.]
[11] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002,83(1):195-213.
[12] Chen J, J?nsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004,91(3):332-344.
[13] Petitjean F, Ketterlin A, Gan?arski P. A global averaging method for dynamic time warping, with applications to clustering[J]. Pattern Recognition, 2011,44(3):678-693.
[14] J?nsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. Geoscience&Remote Sensing IEEE Transactions on, 2002,40(8):1824-1832.
[15]沈金祥,季漩.遥感影像云及云影多特征协同检测方法[J].地球信息科学学报,2016,18(5):599-605.[Shen J X,Ji X. Cloud and cloud shadow multi-feature collaborative detection from remote sensing image[J]. Journal of Geoinformation Science, 2016,18(5):599-605.]
[16]周惠慧,王楠,黄瑶,等.不同时间间隔下的遥感时间序列重构模型比较分析[J].地球信息科学学报,2016,18(10):1410-1417.[Zhou H H, Wang N, Huang Y, et al. Comparison and analysis of remotely sensed time series of reconstruction models at various intervals[J]. Journal of Geo-information Science, 2016,18(10):1410-1417.]
[17] LP DAAC. MODIS products table[OL]:https://lpdaac.usgs.gov/product_search/?view=list&collection_version_id=6&collections=Aqua+MODIS&collections=Combined+MODIS&collections=Terra+MODIS.
[18] Lyapustin A, Wang Y, Xiong X, et al. Scientific impact of MODIS C5 calibration degradation and C6+improvements[J]. Atmospheric Measurement Techniques, 2014,7(12):4353-4365.
[19] Roger J C, Vermote E F, Ray J P. MODIS surface reflectance user's guide, Collection 6[R]. 2015.
[20] Vermote E F. MOD09A1 MODIS/Terra surface reflectance 8-day l3 global 500m sin grid V006[DB]. 2015.doi:10.5067/MODIS/MOD09A1.006
[21]边金虎,李爱农,宋孟强,等. MODIS植被指数时间序列Savitzky-Golay滤波算法重构[J].遥感学报,2010,14(4):725-741.[Bian J H, Li A N, Song M Q, et al. Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter[J]. Journal of Remote Sensing,2010,14(4):725-741.]