Arid
DOI10.3390/rs11040447
Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring
Egorov, Alexey V.1; Roy, David P.2,3; Zhang, Hankui K.1; Li, Zhongbin1; Yan, Lin1; Huang, Haiyan1
通讯作者Roy, David P.
来源期刊REMOTE SENSING
EISSN2072-4292
出版年2019
卷号11期号:4
英文摘要The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 x 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed.
英文关键词Landsat analysis ready data observation coverage big data
类型Article
语种英语
国家USA
开放获取类型Green Submitted, gold
收录类别SCI-E
WOS记录号WOS:000460766100077
WOS关键词TIME-SERIES ; CLOUD COVER ; REFLECTANCE ; WATER ; AVAILABILITY ; CALIBRATION ; MISSION ; TM
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/218340
作者单位1.South Dakota State Univ, Geospatial Sci Ctr Excellence, Brookings, SD 57007 USA;
2.Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA;
3.Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA
推荐引用方式
GB/T 7714
Egorov, Alexey V.,Roy, David P.,Zhang, Hankui K.,et al. Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring[J],2019,11(4).
APA Egorov, Alexey V.,Roy, David P.,Zhang, Hankui K.,Li, Zhongbin,Yan, Lin,&Huang, Haiyan.(2019).Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring.REMOTE SENSING,11(4).
MLA Egorov, Alexey V.,et al."Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring".REMOTE SENSING 11.4(2019).
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