摘要
Google Earth Engine(GEE)是一种基于云建立的地理空间处理平台,可以针对地理空间数据进行分析,实现全球范围内海量遥感数据的并行处理,为遥感大数据、大区域研究提供支持。MODIS积雪覆盖制图是利用MODIS资料建立的全球积雪覆盖产品,已广泛应用于区域乃至全球的气候与环境监测中。GEE云平台存储着百万景遥感影像,其中包括覆盖全球的MODIS逐日积雪产品MOD10A1V5数据和Landsat数据。以新疆西南部3个研究区为例,选取GEE云计算平台存储的Landsat数据,应用NDSI提取积雪范围作为地表覆盖真值,对MOD10A1展开精度评估。结果表明:2000~2016年新疆西南部积雪季MOD10A1的平均总体准确率达82%,平均误判率为2.9%,平均漏判率为58.8%。在晴空条件下,MOD10A1总体准确率可达98%,不同区域的地形及云量是影响MOD10A1精度评估的主要因素。GEE云计算平台可以快速有效地筛选高质量无云的Landsat数据,对全球范围内积雪区的MOD10A1进行精度评估,以在线地图的形式直观显示误判和漏判区域,并利用GEE提供的简单云分函数计算区域云量,使云量对MOD10A1积雪分类精度的影响更具区域代表性。
Google Earth Engine（GEE）is a cloud-based geospatial processing platform that can analyze geospatial data to achieve parallel processing of massive remote sensing data on a global scale,providing support for remote sensing big data and large-area research.MODIS snow cover mapping is a global snow cover product established using MODIS data and has been widely used in regional and global climate and environmental monitoring.In the GEE,millions of remote sensing images are stored,including MODIS daily snow products MOD10 A1 V5 data and Landsat data.Taking the three research areas in southwestern Xinjiang as examples,the Landsat stored by the GEE were selected,and the NDSI was used to extract the snow cover as the true value of the land cover to evaluate the MOD10 A1 accuracy.The results show that the average overall accuracy of MOD10 A1 in the snow cover season in southwestern Xinjiang during the period from2000 to 2016 is 82%,the average misjudgment rate is 2.9%,and the average missed rate is 58.8%.The overall accuracy of MOD10 A1 can reach 98% under the clear sky conditions.The accuracy of MOD10 A1 is effected by the terrain conditions and cloud cover in different regions.Therefore,the GEE can quickly and effectively filter high quality cloudless Landsat images,and evaluate the accuracy of the MOD10 A1 in the snow area around the global regions,displaying intuitively the misjudgment and missed areas in the form of online maps.Meanwhile,GEE provides the Landsat simple cloud score function to calculate the regional cloud cover,which makes the influence of cloud cover on the MOD10 A1 accuracy assessment more regionally representative.
作者
刘畅
李震
张平
田帮森
周建民
Liu Chang;Li Zhen;Zhang Ping;Tian BangsenI=;Zhou Jianmin(Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing lO0094,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《遥感技术与应用》
CSCD
北大核心
2018年第4期584-592,共9页
Remote Sensing Technology and Application
基金
科技基础资源调查专项"中国积雪时空分布特性遥感调查"
