地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (5): 688-698.doi: 10.12082/dqxxkx.2019.180588

• 地理空间分析综合应用 • 上一篇    下一篇

湖南省不同云状态的时空特征分析

胡顺石1,2(), 张辰璐1, 彭雨龙1, 谭子芳1   

  1. 1. 湖南师范大学资源与环境科学学院,长沙 410081
    2. 湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室,长沙 410081
  • 收稿日期:2018-11-15 修回日期:2019-01-31 出版日期:2019-05-25 发布日期:2019-05-25
  • 作者简介:

    作者简介:胡顺石(1984-),男,湖南永州人,讲师,博士,主要从事资源环境遥感与灾害遥感、遥感大数据应用研究。E-mail: hufrank@163.com

  • 基金资助:
    湖南省自然科学基金项目(2018JJ3348);湖南省教育厅科学研究项目(17C0952);中国国家留学基金项目(201806725009)

Spatiotemporal Patterns of the Different Cloud States in Hunan Province

Shunshi HU1,2,*(), Chenlu ZHANG1, Yulong PENG1, Zifang TAN1   

  1. 1. College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China
    2. Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China
  • Received:2018-11-15 Revised:2019-01-31 Online:2019-05-25 Published:2019-05-25
  • Contact: Shunshi HU E-mail:hufrank@163.com
  • Supported by:
    Hunan Provincial Natural Science Foundation of China, No.2018JJ3348;Research Foundation of Education Bureau of Hunan Province, China, No.17C0952;Foundation of China Scholarship Council, No.201806725009.

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。

关键词: 云状态, 时空特征, 湖南省, MODIS, 遥感

Abstract:

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, MOD09A1, were used to extract the cloud state information for each pixel of Hunan Province, spanning from 2001 to 2017. Then, geo-statistics 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.

Key words: cloud states, spatiotemporal patterns, Hunan Province, MODIS, remote sensing